diff --git a/emlmanewpretrainingobjectiveforemotionrelatedtasks/c4818e5f-e6a5-433c-b257-e63272586091_content_list.json b/emlmanewpretrainingobjectiveforemotionrelatedtasks/c4818e5f-e6a5-433c-b257-e63272586091_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..323052a380280d17194e86c9c4527f9a8b6253d3 --- /dev/null +++ b/emlmanewpretrainingobjectiveforemotionrelatedtasks/c4818e5f-e6a5-433c-b257-e63272586091_content_list.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a177dbf169de3c3fdafe80df120f3b88dcc98cf48f1f525121ef08bc058d5075 +size 50409 diff --git a/emlmanewpretrainingobjectiveforemotionrelatedtasks/c4818e5f-e6a5-433c-b257-e63272586091_model.json b/emlmanewpretrainingobjectiveforemotionrelatedtasks/c4818e5f-e6a5-433c-b257-e63272586091_model.json new file mode 100644 index 0000000000000000000000000000000000000000..5cd542366d64e913b136561c80e2516def9a26b7 --- /dev/null +++ b/emlmanewpretrainingobjectiveforemotionrelatedtasks/c4818e5f-e6a5-433c-b257-e63272586091_model.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6113de9d4c92db0286daa78ad458edc0f73437ed651e83f9e28e3782f22e4feb +size 62815 diff --git a/emlmanewpretrainingobjectiveforemotionrelatedtasks/c4818e5f-e6a5-433c-b257-e63272586091_origin.pdf b/emlmanewpretrainingobjectiveforemotionrelatedtasks/c4818e5f-e6a5-433c-b257-e63272586091_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..49cb05eefbf47868e040ccf6418a68bfffbd8423 --- /dev/null +++ b/emlmanewpretrainingobjectiveforemotionrelatedtasks/c4818e5f-e6a5-433c-b257-e63272586091_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c82f645dc5695cfd646f164c33dc5208b9a8a26c0cbe7fb3012e61c35aa85199 +size 246422 diff --git a/emlmanewpretrainingobjectiveforemotionrelatedtasks/full.md b/emlmanewpretrainingobjectiveforemotionrelatedtasks/full.md new file mode 100644 index 0000000000000000000000000000000000000000..c17277b549efbca097f37d1f58a93c7f1c202c91 --- /dev/null +++ b/emlmanewpretrainingobjectiveforemotionrelatedtasks/full.md @@ -0,0 +1,183 @@ +# eMLM: A New Pre-training Objective for Emotion Related Tasks + +Tiberiu Sosea + +Computer Science + +University of Illinois at Chicago + +tsosea2@uic.edu + +Cornelia Caragea + +Computer Science + +University of Illinois at Chicago + +cornelia@uic.edu + +# Abstract + +Bidirectional Encoder Representations from Transformers (BERT) have been shown to be extremely effective on a wide variety of natural language processing tasks, including sentiment analysis and emotion detection. However, the proposed pre-training objectives of BERT do not induce any sentiment or emotion-specific biases into the model. In this paper, we present Emotion Masked Language Modeling, a variation of Masked Language Modeling, aimed at improving the BERT language representation model for emotion detection and sentiment analysis tasks. Using the same pre-training corpora as the original BERT model, Wikipedia and BookCorpus, our BERT variation manages to improve the downstream performance on 4 tasks for emotion detection and sentiment analysis by an average of $1.2\%$ F1. Moreover, our approach shows an increased performance in our task-specific robustness tests. We make our code and pre-trained model available at https://github.com/tsosea2/eMLM. + +# 1 Introduction + +Language models have been studied extensively in the NLP community (Dai and Le, 2015; Howard and Ruder, 2018; Peters et al., 2018; Devlin et al., 2019; Liu et al., 2019), with approaches attaining state-of-the-art results on multiple token-level or sentence-level tasks. BERT (Devlin et al., 2019) is a pre-trained language model, which proposed a new pre-training objective inspired by the Clozetask (Taylor, 1953), which enables the training of a deep bi-directional transformer network. This objective, called Masked Language Modeling (MLM) is used on large amounts of unlabeled data from Wikipedia and BookCorpus to produce powerful universal language representations. However, the pre-training does not take into account the downstream task on which the model will be applied. + +In this paper, we posit that we can leverage the characteristics of a downstream task to design better task-tailored pre-training objectives. Concretely, we induce information from emotion or sentiment lexicons into our BERT pre-training objective to improve the performance on tasks from sentiment analysis and emotion detection. + +There are numerous studies that focus on emotion detection (Demszky et al., 2020; Desai et al., 2020; del Arco et al., 2020; Sosea and Caragea, 2020; Majumder et al., 2019; Mohammad and Kiritchenko, 2018; Abdul-Mageed and Ungar, 2017; Mohammad and Kiritchenko, 2015; Mohammad, 2012; Strapparava and Mihalcea, 2008) and sentiment analysis (Yin et al., 2020; Tian et al., 2020; Phan and Ogunbona, 2020; Zhai and Zhang, 2016; Chen et al., 2016; Liu, 2012; Glorot et al., 2011; Pang and Lee, 2005). Various lexicons have been used to improve model performance on these tasks. For instance, Katz et al. (2007) used occurrences of emotion words to identify various emotion types in news headlines. Moreover, emotion lexicons have been used to produce important features which can be used inside a machine learning algorithm to improve the performance on emotion detection tasks (Mohammad, 2012; Sykora et al., 2013; Khanpour and Caragea, 2018; Biyani et al., 2014). In this paper, however, instead of leveraging these lexicons to design features, in contrast, we use them to obtain language representations that are more suitable for emotion and sentiment tasks. + +To this end, we introduce Emotion Masked Language Modeling (eMLM), a new pre-training BERT (Devlin et al., 2019) objective aimed at improving the BERT performance on tasks related to sentiment analysis and emotion detection. Inspired by the well-known Masked Language Modeling objective, eMLM adds only a few simple, yet powerful changes. Instead of uniformly masking the tokens in the input sequence, eMLM leverages + +
SENTTheylookabsolutelyperfecttogetherIhopeitsthatwayinreallifetoo
MLM0.150.150.150.150.150.150.150.150.150.150.150.150.150.15
eMLM0.090.090.090.500.090.090.500.090.090.090.090.090.090.09
SENTMosttiringthingwasthedriveonehoureachway
MLM0.150.150.150.150.150.150.150.150.150.15
eMLM0.110.500.110.110.110.110.110.110.110.11
+ +Table 1: Comparison of masked probabilities between MLM and eMLM on two example sentences. + +lexicon information, and assigns higher masking probabilities to words that are more likely to be important in the sentiment or emotion contexts. To enable a fair comparison with the vanilla BERT model, we train the eMLM BERT model in the same fashion as the vanilla BERT, pre-training on Wikipedia and BookCorpus (Zhu et al., 2015). To our knowledge, we are the first to study different masking probabilities for the BERT pre-training procedure guided by sentiment and emotion lexicons. Similar to our work, some studies also focused on incorporating sentiment information into pre-trained language models. For example, Yin et al. (2020) built an attention network on top of BERT to predict sentiment labels of phrase nodes obtained through a constituency parse tree. On the other hand, Tian et al. (2020) designed various pretraining objectives, such as masking and predicting all words from a pre-defined small set of seeds, and predicting an aspect-sentiment pair or the polarity of words. In contrast, we leverage information from available sentiment and emotion lexicons. + +We show the feasibility of our approach by testing eMLM on two sentiment analysis benchmark datasets and two emotion detection datasets. These datasets span diverse domains, such as movie reviews, online health communities, and Reddit discussions, enabling a comprehensive analysis of eMLM. + +Our contributions are as follows: 1) We introduce a new pre-training objective for BERT (leveraging available lexicons), aimed at producing better task-guided universal representations for downstream tasks from sentiment analysis and emotion detection. We offer the pre-trained model as an easy way to leverage our approach on downstream applications. 2) We show the efficacy of our approach by testing our method on four benchmark datasets for emotion and sentiment and obtain an average improvement in F1 score of $1.2\%$ . 3) We verify the robustness of our model in the face of input perturbations, which occur frequently in informal contexts (e.g., due to mispellings). + +# 2 Proposed Approach + +Background Bidirectional Encoder from Transformers for Language Understanding (BERT) (Devlin et al., 2019) is a pre-trained language model trained on large amounts of unlabeled data using two objectives: 1) Masked Language Modeling (MLM) randomly masks $15\%$ of tokens in a sequence, followed by a supervised prediction of the masked tokens; 2) Next Sentence Prediction (NSP) predicts in a binary fashion if two sentences follow each other. By using these two tasks on large-scale data repositories such as BookCorpus (800M words) (Zhu et al., 2015) and Wikipedia (2,500M words), BERT produces powerful universal language representations, applicable on a wide range of tasks, such as sentiment analysis, question answering, and commonsense reasoning. + +However, to be used in various downstream tasks, BERT has to undergo a task-specific finetuning step (Devlin et al., 2019), where the contextualized embedding is adapted to the needed task. We posit that we can improve the downstream performance by focusing on the target task in the pre-training phase as well. Specifically, we focus on sentiment analysis and emotion detection, and show that task-guided unsupervised pre-training helps the performance considerably. + +Masking Emotion Words Now we introduce Emotion Masked Language Modeling (eMLM), a variation of MLM targeted at inducing emotion or sentiment-specific biases in the BERT pre-training phase. Specifically, unlike BERT, which uses a uniform probability (15%) to mask the tokens in an input sentence, we assign higher probabilities to tokens which are emotionally rich words from an available lexicon $\mathcal{L}$ . We denote this probability by $k$ , which is a hyperparameter in our eMLM method. Our masking process can be summarized as follows: Given an input sentence $S$ : 1) We extract the words that belong to the lexicon $\mathcal{L}$ , and we denote them by $E$ ; 2) We set the masking probability of these words as $P(w_{e}) = k \forall w_{e} \in E$ ; 3) To ensure + +we mask $15\%$ of the words in total, we lower the masking probability of the non-emotionally-rich words using the following formula: + +$$ +P (w _ {n}) = \frac {\operatorname* {m a x} (| S | \cdot 0 . 1 5 - | E | \cdot k , 0)}{| S | - | E |}, \forall w _ {n} \notin E +$$ + +where $|\cdot|$ represents the size of a set. We show examples of how our masked probabilities change from MLM to eMLM in Table 1. For instance, in the first example, there are two emotion words, perfect and hope, and we use a masking probability of $k = 0.50$ . While the probabilities of these two words are set to $50\%$ , the non emotionally-rich word probability is lowered from $15\%$ to $9\%$ to keep the sum of probabilities constant. The rest of the training process is the same as the original BERT pre-training. That is, we train our BERT model from scratch using eMLM and NSP on the same datasets: Wikipedia and BookCorpus. We mention that we use whole word masking, both for eMLM and the MLM (i.e., we mask all the subtokens corresponding to a word). + +# 3 Experiments and Results + +In this section, we first describe our experimental setup (§3.1), then present our datasets and lexicons (§3.2), and then discuss the results that contrast eMLM with the original BERT MLM (§3.3). + +# 3.1 Experimental Setup + +We use various benchmark datasets from sentiment analysis and emotion detection to test our eMLM approach. For every dataset considered, we use the provided training, validation, and test splits. To assert statistical significance, we fine-tune each model 10 times with different random seeds and report the average F1 score. We investigate various masking probabilities $k$ , ranging from 0.2 to 1.0, and find that 0.5 works best in our setting. For low values around 0.2 we notice that the performance is similar to that of the original BERT, while for high values (closer to 1.0), the performance is negatively affected. + +# 3.2 Datasets and Lexicons + +We test our models on various benchmark datasets described below. + +Stanford Sentiment Treebank (SST) (Socher et al., 2013) SST contains 11,855 sentences from + +
SST-2SST-5
ACCF-1ACCF-1
BERT0.9120.9220.5320.541
eMLM (S)0.9190.9280.5410.552
eMLM (E)0.9200.931†0.5470.558†
+ +Table 2: Performance on the sentiment analysis task. We assert significance† if $p < 0.05$ under a t-test with the vanilla BERT model. + +movie reviews, annotated with five sentiment labels: negative, somewhat negative, neutral, somewhat positive, and positive. First, we consider the binarized dataset, called SST-2, where the examples with the negative and somewhat negative labels are merged into a negative class, and the examples with the somewhat positive and positive labels are merged into a positive class (with neutral class being removed). Second, we consider the SST fine-grained version (SST-5), which uses all five labels. + +GoEmotions (Demszky et al., 2020) is a sentence-level multilabel dataset of 58,000 comments curated from Reddit and annotated with 27 emotion categories and the neutral class. + +CancerEmo (Sosea and Caragea, 2020) is a sentence-level multilabel dataset of 8,500 sentences labeled with the eight Plutchik (Plutchik, 1980) basic emotions from an Online Health Community for people suffering from diseases such as cancer. + +We analyze the behaviour of eMLM in diverse environments: sentiment analysis or emotion detection, various data platforms (e.g., Reddit,OHCs), and variate emotion or sentiment granularity (from 2 classes to as many as 28 classes). + +**Lexicons** As mentioned above, our eMLM focuses on emotionally rich words from a lexicon. In this paper, we use EmoLex (Mohammad and Turney, 2013), a lexicon of 6,000 words associated with eight Plutchik basic emotions (Plutchik, 1980) (sadness, anger, joy, surprise, anticipation, trust, fear, disgust) and 5,555 words associated with the positive and negative sentiments. We consider the sentiment and emotion words separately to analyze the impact of each on the performance of eMLM. We denote the approach which masks the emotion-revealing words by eMLM (E), and the sentiment-revealing words by eMLM (S). + +
EMOTIONBERTeMLM (E)eMLM (S)
ADMIRATION0.650.68†0.67
AMUSEMENT0.800.83†0.82
ANGER0.470.460.46
ANNOYANCE0.340.340.34
APPROVAL0.360.380.37
CARING0.390.430.42
CONFUSION0.370.370.37
CURIOSITY0.540.57†0.57
DESIRE0.490.490.49
DISAPPOINTMENT0.280.300.30
DISAPPROVAL0.390.43†0.41
DISGUST0.450.48†0.48
EMBARRASSMENT0.430.430.44
EXCITEMENT0.340.340.34
FEAR0.600.64†0.63
GRATITUDE0.860.88†0.87
GRIEF0.000.000.00
JOY0.510.530.52
LOVE0.780.80†0.80
NERVOUSNESS0.350.370.36
NEUTRAL0.680.670.68
OPTIMISM0.510.530.52
PRIDE0.360.360.36
REALIZATION0.210.210.21
RELIEF0.150.160.16
REMORSE0.660.650.66
SADNESS0.490.490.48
SURPRISE0.500.53†0.52
AVERAGE0.4620.4760.469
+ +# 3.3 Results + +Results on Sentiment Analysis We show the results of our approaches on SST in Table 2. First, we observe that eMLM (E) and eMLM (S) improve upon the vanilla BERT model on both tasks, with eMLM (E) obtaining as much as $1.7\%$ improvement in F1. Interestingly, eMLM (E) outperforms eMLM (S) suggesting that masking finer-granularity emotion words in eMLM produces better representations for the task. At the same time, eMLM (E) achieves better performance on the fine-grained SST-5 task, where the improvements over the vanilla BERT are considerable. + +Results on Emotion Detection We show the results of eMLM on the GoEmotions dataset in Table 3 and observe that, similar to sentiment analysis, eMLM (E) is the best performing approach, improving upon vanilla BERT by $1.4\%$ in F1. We show the results on CancerEmo in Table 4 and observe the same pattern: eMLM (E) consistently outperforms the other approaches. We see improvements as high as $4\%$ on Joy and $2\%$ on Sad + +Table 3: F-1 scores on the Goemotion dataset. We assert significance† if $p < 0.05$ under a t-test with the vanilla BERT model. + +
EMOTIONBERTeMLM (E)eMLM (S)
SADNESS0.710.73†0.73†
JOY0.810.85†0.84
FEAR0.770.770.77
ANGER0.680.690.69
SURPRISE0.680.680.67
DISGUST0.590.580.57
TRUST0.670.670.67
ANTICIPATION0.700.78†0.74
AVERAGE0.7010.7180.706
+ +Table 4: Performance on CancerEmo dataset. We assert significance† if $p < 0.05$ under a t-test with the vanilla BERT model. + +
KSST-2SST-5CANCEREMOGOEMOTIONS
0.150.9220.5410.7010.462
0.300.9230.5400.7040.466
0.500.9310.5580.7180.476
0.700.9210.5390.7000.455
0.900.9110.5400.6910.412
+ +Table 5: Average F-1 on the considered datasets using various values of the emotion masking probability $k$ : + +ness. Overall, eMLM (E) obtains an $1.7\%$ F1 improvement over the vanilla BERT model. + +Discussion The presented results reveal the feasibility of our proposed approach. Our BERT model trained using the eMLM objective produces high quality contextualized embeddings for downstream tasks that span the sentiment analysis and emotion detection tasks. Moreover, our methods incur no additional computational cost over the original BERT (Devlin et al., 2019), and undergo the same amount of pre-training. We also tried combining and masking both sentiment and emotion words; however, we did not see any performance improvements. As a step forward, we are interested in gaining more insights into the differences between eMLM (E) and the vanilla BERT model. We study this in the robustness context in the next section, and analyze how our models behave in the face of various input perturbations (i.e., noise). + +Varying the Emotion Masking Probability $k$ To offer additional insights into our eMLM approach and show the impact of the sentiment or emotion-rich word masking probability on downstream tasks, we show the results obtained using various values of $k$ in Table 5. First, we note that using a slightly lower probability of 0.30 still adds improvements to our model on three of the considered datasets. In contrast, too high of a proba + +bility hurts the F1 performance. Concretely, using $k = 0.90$ , our eMLM approach decreases the F1 compared to the vanilla BERT by $1\%$ on CancerEmo, $5\%$ on GoEmotions, and $1\%$ on SST-2. + +# 4 Robustness Test + +It has been shown that neural models are often sensitive to various input perturbations (Niu et al., 2020; Belinkov and Bisk, 2018). In this section, we aim to investigate the robustness of our proposed approach in the face of input noise. We focus on the following two questions: 1) Does eMLM improve the robustness of the model? 2) What type of input noise is successful in misleading our model? We study these questions on the SST-5 sentiment analysis task using the framework introduced by Hsieh et al. (2019). We explore three ways to generate input perturbations and verify their "success." We say a perturbation is "successful" on a model $M$ for an example $e$ if 1) The model $M$ classifies $e$ correctly and 2) The model $M$ misclassifies the example $e$ when noise is applied to it. Naturally, the lower the perturbation success rate, the more robust a model is. The perturbations that we considered are as follows: + +1. Random (Alzantot et al., 2018) replaces one word from the input sentence with a random word from the vocabulary. For a word, we repeat this process 100 times. If at least one of the replacements leads to an incorrect prediction, the perturbation is deemed to be successful. +2. LIST (Alzantot et al., 2018) replaces each word (one at a time) in the input text with a synonym. The input perturbation is successful if at least one replacement leads to an incorrect prediction. +3. EmoWord If there is an emotion word in the input sentence, then we zero out that word, otherwise, we zero out a random word from the input sequence. + +Results We show the results of the robustness tests for the vanilla BERT and the eMLM approach in Table 6. First, EmoWord is the most successful perturbation, being twice as effective compared to the other methods. Second, we observe that Random and LIST obtain the same success rates among both the BERT and eMLM approach. However, + +
EMOTIONRANDOMLISTEMOWORD
BERT1.5%2.4%9.8%
eMLM1.5%2.4%5.4%
+ +Table 6: Robustness of our models in terms of perturbation success rates. Lower success rates indicate more robust models. + +on EmoWord, our eMLM approach is considerably more robust, outperforming the simple BERT model by $4.4\%$ . We argue that this is the byproduct of the eMLM training procedure, which focuses on predicting emotion words in the pre-training step. + +# 5 Conclusion + +In this paper, we introduced a new BERT pretraining objective suited for sentiment analysis and emotion detection tasks. We showed that the approach is feasible; it needs no additional pretraining compared to the vanilla BERT, and improves the performance by $1.2\%$ F1 on average on various tasks. Our analysis also suggests that eMLM is more robust in the face of input perturbations. As future work, we note that our approach is general enough, so we plan to leverage different lexicons outside the sentiment analysis and emotion detection domains to investigate if the model generalizes well on other domains (e.g., financial). We also plan to study if our method is effective for non-English languages. Finally, we note that there exist lexicons that assign to words not only their emotion, but also their emotion intensity (Mohammad, 2018). Therefore, we plan to investigate if associating the masking probability with the emotion intensity (i.e., assign a higher probability to a more intensive word) would further help improve the performance. + +# Acknowledgments + +We thank our anonymous reviewers for their constructive comments and feedback. This work is partially supported by the NSF Grants IIS-1912887 and IIS-1903963. Any opinions, findings, and conclusions expressed here are those of the authors and do not necessarily reflect the views of NSF. The computation for this project was performed on Amazon Web Services through a research grant. + +# References + +Muhammad Abdul-Mageed and Lyle Ungar. 2017. EmoNet: Fine-grained emotion detection with gated recurrent neural networks. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 718-728, Vancouver, Canada. Association for Computational Linguistics. +Moustafa Alzantot, Yash Sharma, Ahmed Elgohary, Bo-Jhang Ho, Mani Srivastava, and Kai-Wei Chang. 2018. Generating natural language adversarial examples. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2890–2896, Brussels, Belgium. Association for Computational Linguistics. +Flor Miriam Plaza del Arco, Carlo Strapparava, Luis Alfonso Ureña López, and María Teresa Martín Valdivia. 2020. Emoevent: A multilingual emotion corpus based on different events. In Proceedings of The 12th Language Resources and Evaluation Conference, LREC 2020, Marseille, France, May 11-16, 2020, pages 1492-1498. European Language Resources Association. +Yonatan Belinkov and Yonatan Bisk. 2018. Synthetic and natural noise both break neural machine translation. In International Conference on Learning Representations. +Prakhar Biyani, Cornelia Caragea, Prasenjit Mitra, and John Yen. 2014. Identifying emotional and informational support in online health communities. In Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, pages 827-836, Dublin, Ireland. Dublin City University and Association for Computational Linguistics. +Huimin Chen, Maosong Sun, Cunchao Tu, Yankai Lin, and Zhiyuan Liu. 2016. Neural sentiment classification with user and product attention. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 1650-1659, Austin, Texas. Association for Computational Linguistics. +Andrew M Dai and Quoc V Le. 2015. Semi-supervised sequence learning. In Advances in Neural Information Processing Systems, volume 28, pages 3079-3087. Curran Associates, Inc. +Dorottya Demszky, Dana Movshovitz-Attias, Jeongwoo Ko, Alan Cowen, Gaurav Nemade, and Sujith Ravi. 2020. GoEmotions: A dataset of fine-grained emotions. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4040–4054, Online. Association for Computational Linguistics. +Shrey Desai, Cornelia Caragea, and Junyi Jessy Li. 2020. Detecting perceived emotions in hurricane disasters. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, + +pages 5290-5305, Online. Association for Computational Linguistics. +Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171-4186, Minneapolis, Minnesota. Association for Computational Linguistics. +Xavier Glorot, Antoine Bordes, and Yoshua Bengio. 2011. Domain adaptation for large-scale sentiment classification: A deep learning approach. In *In Proceedings of the Twenty-eight International Conference on Machine Learning*, ICML. +Jeremy Howard and Sebastian Ruder. 2018. Universal language model fine-tuning for text classification. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 328-339, Melbourne, Australia. Association for Computational Linguistics. +Yu-Lun Hsieh, Minhao Cheng, Da-Cheng Juan, Wei Wei, Wen-Lian Hsu, and Cho-Jui Hsieh. 2019. On the robustness of self-attentive models. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1520-1529, Florence, Italy. Association for Computational Linguistics. +Phil Katz, Matt Singleton, and Richard Vicentowski. 2007. SWAT-MP: the SemEval-2007 systems for task 5 and task 14. In Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007), pages 308-313, Prague, Czech Republic. Association for Computational Linguistics. +Hamed Khanpour and Cornelia Caragea. 2018. Fine-grained emotion detection in health-related online posts. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1160-1166, Brussels, Belgium. Association for Computational Linguistics. +Bing Liu. 2012. Sentiment analysis and opinion mining. Synthesis lectures on human language technologies, 5(1):1-167. +Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692. +Navonil Majumder, Soujanya Poria, Devamanyu Hazarika, Rada Mihalcea, Alexander F. Gelbukh, and Erik Cambria. 2019. Dialoguerrn: An attentive RNN for emotion detection in conversations. In The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference, IAAI + +2019, The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, Hawaii, USA, January 27 - February 1, 2019, pages 6818-6825. AAAI Press. +Saif Mohammad. 2012. #emotional tweets. In *SEM 2012: The First Joint Conference on Lexical and Computational Semantics – Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012), pages 246–255, Montréal, Canada. Association for Computational Linguistics. +Saif Mohammad. 2018. Word affect intensities. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan. European Language Resources Association (ELRA). +Saif Mohammad and Svetlana Kiritchenko. 2018. Understanding emotions: A dataset of tweets to study interactions between affect categories. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan. European Language Resources Association (ELRA). +Saif M. Mohammad and Svetlana Kiritchenko. 2015. Using hashtags to capture fine emotion categories from tweets. Computational Intelligence, 31(2):301-326. +Saif M Mohammad and Peter D Turney. 2013. Crowdsourcing a word-emotion association lexicon. Computational Intelligence, 29(3):436-465. +Xing Niu, Prashant Mathur, Georgiana Dinu, and Yaser Al-Onaizan. 2020. Evaluating robustness to input perturbations for neural machine translation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 8538-8544, Online. Association for Computational Linguistics. +Bo Pang and Lillian Lee. 2005. Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL'05), pages 115-124, Ann Arbor, Michigan. Association for Computational Linguistics. +Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018. Deep contextualized word representations. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 2227-2237, New Orleans, Louisiana. Association for Computational Linguistics. +Minh Hieu Phan and Philip O. Ogunbona. 2020. Modelling context and syntactical features for aspect-based sentiment analysis. In Proceedings of the + +58th Annual Meeting of the Association for Computational Linguistics, pages 3211-3220, Online. Association for Computational Linguistics. +Robert Plutchik. 1980. A general psychoevolutionary theory of emotion. In *Theories of emotion*, pages 3-33. Elsevier. +Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Ng, and Christopher Potts. 2013. Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pages 1631-1642, Seattle, Washington, USA. Association for Computational Linguistics. +Tiberiu Sosea and Cornelia Caragea. 2020. CancerEmo: A dataset for fine-grained emotion detection. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 8892-8904, Online. Association for Computational Linguistics. +Carlo Strapparava and Rada Mihalcea. 2008. Learning to identify emotions in text. In Proceedings of the 2008 ACM Symposium on Applied Computing (SAC), Fortaleza, Ceara, Brazil, March 16-20, 2008, pages 1556-1560. ACM. +Martin D Sykora, Thomas Jackson, Ann O'Brien, and Suzanne Elayan. 2013. Emotive ontology: Extracting fine-grained emotions from terse, informal messages. IADIS Int. J. Comput. Sci. Inf. Syst, 2013:19-26. +Wilson L Taylor. 1953. "cloze procedure": A new tool for measuring readability. Journalism quarterly, 30(4):415-433. +Hao Tian, Can Gao, Xinyan Xiao, Hao Liu, Bolei He, Hua Wu, Haifeng Wang, and Feng Wu. 2020. SKEP: Sentiment knowledge enhanced pre-training for sentiment analysis. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4067-4076, Online. Association for Computational Linguistics. +Da Yin, Tao Meng, and Kai-Wei Chang. 2020. SentiBERT: A transferable transformer-based architecture for compositional sentiment semantics. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3695-3706, Online. Association for Computational Linguistics. +Shuangfei Zhai and Zhongfei (Mark) Zhang. 2016. Semisupervised autoencoder for sentiment analysis. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, February 12-17, 2016, Phoenix, Arizona, USA, pages 1394-1400. AAAI Press. +Yukun Zhu, Ryan Kiros, Richard S. Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. 2015. Aligning books and movies: + +Towards story-like visual explanations by watching movies and reading books. In 2015 IEEE International Conference on Computer Vision, ICCV 2015, Santiago, Chile, December 7-13, 2015, pages 19-27. IEEE Computer Society. \ No newline at end of file diff --git a/emlmanewpretrainingobjectiveforemotionrelatedtasks/images.zip b/emlmanewpretrainingobjectiveforemotionrelatedtasks/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..ec91af946fa03c544a8c7b60e3d0c8a259631840 --- /dev/null +++ b/emlmanewpretrainingobjectiveforemotionrelatedtasks/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4d93df7c43f87e64ce9de332337b5ddf983af2e5940d412cc734b3b2f9b2f24d +size 206238 diff --git a/emlmanewpretrainingobjectiveforemotionrelatedtasks/layout.json b/emlmanewpretrainingobjectiveforemotionrelatedtasks/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..20fb39561039810af5d259d58adfc9a6862b0106 --- /dev/null +++ b/emlmanewpretrainingobjectiveforemotionrelatedtasks/layout.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a64125431b7ed49370432872a220c5daf2d144846c29d1d0899b452c57f88eb5 +size 223359 diff --git a/mtvrmultilingualmomentretrievalinvideos/417b5ca6-6009-429a-ad5a-be7d95871179_content_list.json b/mtvrmultilingualmomentretrievalinvideos/417b5ca6-6009-429a-ad5a-be7d95871179_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..c80756e137d75eac3e5fc51621a83138c9cca65f --- /dev/null +++ b/mtvrmultilingualmomentretrievalinvideos/417b5ca6-6009-429a-ad5a-be7d95871179_content_list.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:53d7d5b6f522f86e7a8735eca1ab0e8ce77e8181873f969a9fdee7d692b99e9f +size 69016 diff --git a/mtvrmultilingualmomentretrievalinvideos/417b5ca6-6009-429a-ad5a-be7d95871179_model.json b/mtvrmultilingualmomentretrievalinvideos/417b5ca6-6009-429a-ad5a-be7d95871179_model.json new file mode 100644 index 0000000000000000000000000000000000000000..d11c8ff4293cf08e3700bc19f6ae6ae8df0c2d71 --- /dev/null +++ b/mtvrmultilingualmomentretrievalinvideos/417b5ca6-6009-429a-ad5a-be7d95871179_model.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:35a97ab94cbd63269ea339d62c25bc1b32454da0dc351d3788d0489f8da70094 +size 91244 diff --git a/mtvrmultilingualmomentretrievalinvideos/417b5ca6-6009-429a-ad5a-be7d95871179_origin.pdf b/mtvrmultilingualmomentretrievalinvideos/417b5ca6-6009-429a-ad5a-be7d95871179_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..c5a6a1a08e05a8c4f7689b0b074b7d36f50202b7 --- /dev/null +++ b/mtvrmultilingualmomentretrievalinvideos/417b5ca6-6009-429a-ad5a-be7d95871179_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1f677bb99eef4bc3980efed37284e985f3fd2618366841e27271e1c027aa1e20 +size 2642289 diff --git a/mtvrmultilingualmomentretrievalinvideos/full.md b/mtvrmultilingualmomentretrievalinvideos/full.md new file mode 100644 index 0000000000000000000000000000000000000000..0122c606097a1a5b0546421f5e299820bc733712 --- /dev/null +++ b/mtvrmultilingualmomentretrievalinvideos/full.md @@ -0,0 +1,347 @@ +# MTVR: Multilingual Moment Retrieval in Videos + +Jie Lei Tamara L. Berg Mohit Bansal + +Department of Computer Science + +University of North Carolina at Chapel Hill + +{jielei, tlberg, mbansal}@cs.unc.edu + +# Abstract + +We introduce MTVR, a large-scale multilingual video moment retrieval dataset, containing 218K English and Chinese queries from 21.8K TV show video clips. The dataset is collected by extending the popular TVR dataset (in English) with paired Chinese queries and subtitles. Compared to existing moment retrieval datasets, MTVR is multilingual, larger, and comes with diverse annotations. We further propose mXML, a multilingual moment retrieval model that learns and operates on data from both languages, via encoder parameter sharing and language neighborhood constraints. We demonstrate the effectiveness of mXML on the newly collected MTVR dataset, where mXML outperforms strong monolingual baselines while using fewer parameters. In addition, we also provide detailed dataset analyses and model ablations. Data and code are publicly available at https://github.com/jayleicn/mTVRetrieval + +# 1 Introduction + +The number of videos available online is growing at an unprecedented speed. Recent work (Escorchia et al., 2019; Lei et al., 2020) introduced the Video Corpus Moment Retrieval (VCMR) task: given a natural language query, a system needs to retrieve a short moment from a large video corpus. Figure 1 shows a VCMR example. Compared to the standard text-to-video retrieval task (Xu et al., 2016; Yu et al., 2018), it allows more fine-grained moment-level retrieval, as it requires the system to not only retrieve the most relevant videos, but also localize the most relevant moments inside these videos. Various datasets (Krishna et al., 2017; Hendricks et al., 2017; Gao et al., 2017; Lei et al., 2020) have been proposed or adapted for the task. However, they are all created for a single language (English), though the application could be useful for users speaking other languages as well. Besides, it is also unclear + +# Video Corpus: + +![](images/52a87699954c377a23cdccff5ad85ba34733964ae10137149fcf2c5c219f9c50.jpg) + +00:00,327→00:04,320 + +Whitney: This is my fiancé... +惠特尼：这是我的未婚夫... + +00:32,192→00:34,626 + +House: Nine months later, a miracle... +豪斯：9个月之后，一个奇迹… + +![](images/252398a70944a98abe009420d4592cc5f90e94c6e2f795adde6cc2c0715dd489.jpg) + +00:07,786→00:13,156 + +Monica: Who wasn't invited.. + +莫妮卡：还没有被邀请到… + +00:44,223→00:52,929 + +Rachel: Daddy, I can't marry him... + +瑞秋：爸爸，我不能嫁给他… + +![](images/3056377ae75f6933931506d5b056e0b783654bcad60d161d45cd47df22121686.jpg) + +00:03,897→00:07,731 + +Ross: Somebody seems to be. + +罗斯：有人在… + +00:36,497→00:38,761 + +Rachel: Call me when you get this. + +瑞秋：听到留言请回电。 + +Query: + +Rachel explains to her dad on the phone why she can't marry her fiancé. + +瑞秋在电话里向她父亲解释了她不能和其未婚夫结婚的原因。 + +Query Type: video + subtitle + +Figure 1: A MTVR example in the Video Corpus Moment Retrieval (VCMR) task. Ground truth moment is shown in green box. Colors in the query text indicate whether the words are more related to video (orchid) or subtitle (salmon) or both (orange). The query and the subtitle text are presented in both English and Chinese. The video corpus typically contains thousands of videos, for brevity, we only show 3 videos here. + +whether the progress and findings in one language generalizes to another language (Bender, 2009). While there are multiple existing multilingual image datasets (Gao et al., 2015; Elliott et al., 2016; Shimizu et al., 2018; Pappas et al., 2016; Lan et al., 2017; Li et al., 2019), the availability of multilingual video datasets (Wang et al., 2019a; Chen and Dolan, 2011) is still limited. + +Therefore, we introduce MTVR, a large-scale, multilingual moment retrieval dataset, with 218K human-annotated natural language queries in two + +languages, English and Chinese. MTVR extends the TVR (Lei et al., 2020) dataset by collecting paired Chinese queries and Chinese subtitle text (see Figure 1). We choose TVR over other moment retrieval datasets (Krishna et al., 2017; Hendricks et al., 2017; Gao et al., 2017) because TVR is the largest moment retrieval dataset, and also has the advantage of having dialogues (in the form of subtitle text) as additional context for retrieval, in contrast to pure video context in the other datasets. We further propose mXML, a compact, multilingual model that learns jointly from both English and Chinese data for moment retrieval. Specifically, on top of the state-of-the-art monolingual moment retrieval model XML (Lei et al., 2020), we enforce encoder parameter sharing (Sachan and Neubig, 2018; Dong et al., 2015) where the queries and subtitles from the two languages are encoded using shared encoders. We also incorporate a language neighborhood constraint (Wang et al., 2018; Kim et al., 2020) to the output query and subtitle embeddings. It encourages sentences of the same meaning in different languages to lie close to each other in the embedding space. Compared to separately trained monolingual models, mXML substantially reduces the total model size while improving retrieval performance (over monolingual models) as we show in Section 4. Detailed dataset analyses and model ablations are provided. + +# 2 Dataset + +The TVR (Lei et al., 2020) dataset contains 108,965 high-quality English queries from 21,793 videos from 6 long-running TV shows (provided by TVQA (Lei et al., 2018)). The videos are associated with English dialogues in the form of subtitle text. mTVR extends this dataset with translated dialogues and queries in Chinese to support multilingual multimodal research. + +# 2.1 Data Collection + +Dialogue Subtitles. We crawl fan translated Chinese subtitles from subtitle sites.1 All subtitles are manually checked by the authors to ensure they are of good quality and are aligned with the videos. The original English subtitles come with speaker names from transcripts that we map to the Chinese subtitles, to ensure that the Chinese subtitles have the same amount of information as the English version. + +
QType (%)Query Examples (in English and Chinese)
video-only (74.2)Howard places his plate onto the coffee table.霍华德将盘子放在咖啡桌子上。
sub-only (9.1)Alexis and Castle talk about the timeline of the murder.亚历克西斯和卡塞尔谈论谋杀的时间顺序。
video+sub (16.6)Joey waivers his hand when he asks for his food.乔伊催餐时摆了摆手。
+ +Table 1: MTVR English and Chinese query examples in different query types. The percentage of the queries in each query type is shown in brackets. + +Query. To obtain Chinese queries, we hire human translators from Amazon Mechanical Turk (AMT). Each AMT worker is asked to write a Chinese translation of a given English query. Languages are ambiguous, hence we also present the original videos to the workers at the time of translation to help clarify query meaning via spatiotemporal visual grounding. The Chinese translations are required to have the exact same meaning as the original English queries and the translation should be made based on the aligned video content. To facilitate the translation process, we provide machine translated Chinese queries from Google Cloud Translation $^2$ as references, similar to (Wang et al., 2019b). To find qualified bilingual workers in AMT, we created a qualification test with 5 multiple-choice questions designed to evaluate workers' Chinese language proficiency and their ability to perform our translation task. We only allow workers that correctly answer all 5 questions to participate our annotation task. In total, 99 workers finished the test and 44 passed, earning our qualification. To further ensure data quality, we also manually inspect the submitted results during the annotation process and disqualify workers with poor annotations. We pay workers $0.24 every three sentences, this results in an average hourly pay of $8.70. The whole annotation process took about 3 months and cost approximately $12,000.00. + +# 2.2 Data Analysis + +In Table 2, we compare the average sentence lengths and the number of unique words under different part-of-speech (POS) tags, between the two languages, English and Chinese, and between query and subtitle text. For both languages, dialogue subtitles are linguistically more diverse than queries, i.e., they have more unique words in all + +
DataAvg +Len#unique words by POS tags
allverbnounadj.adv.
English
Q13.4515,2013,0157,1432,290763
Sub10.7849,3256,44119,2237,5041,740
Q+Sub11.2752,5457,15120,6898,0211,976
Chinese
Q12.5534,75212,77318,7061,4151,669
Sub9.04101,01836,810537364,9585,568
Q+Sub9.67117,44842,28462,6115,5056,185
+ +Table 2: Comparison of English and Chinese data in MTVR. We show average sentence length, and number of unique tokens by POS tags, for Query $(Q)$ and or Subtitle (Sub). + +categories. This is potentially because the language used in subtitles are unconstrained human dialogues while the queries are collected as declarative sentences referring to specific moments in videos (Lei et al., 2020). Comparing the two languages, the Chinese data is typically more diverse than the English data.3 In Table 1, we show English and their translated Chinese query examples in Table 1, by query type. In the appendix, we compare MTVR with existing video and language datasets. + +# 3 Method + +Our multilingual moment retrieval model mXML is built on top of the Cross-model Moment Localization (XML) (Lei et al., 2020) model, which performs efficient video-level retrieval at its shallow layers and accurate moment-level retrieval at its deep layers. To adapt the monolingual XML model into the multilingual setting in MTVR and improve its efficiency and effectiveness, we apply encoder parameter sharing and neighborhood constraints (Wang et al., 2018; Kim et al., 2020) which encourages the model to better utilize multilingual data to improve monolingual task performance while maintaining smaller model size. + +Query and Context Representations. We represent videos using ResNet-152 (He et al., 2016) and I3D (Carreira and Zisserman, 2017) features extracted every 1.5 seconds. We extract language features using pre-trained, then finetuned (on our queries and subtitles) RoBERTa-base (Liu et al., 2019), for English (Liu et al., 2019) and Chinese (Cui et al., 2020), respectively. For queries, we use token-level features. For subtitles, we max + +![](images/903fb0520232239d4bdaf437a99161a1284b7d7a8bc4199bb8ff4b35a3b0b859.jpg) +Figure 2: Illustration of mXML's encoding process. Compared to monolingual models, mXML learns from the two languages simultaneously, and allows them to benefit each other via encoder parameter sharing and neighborhood constraints. We show the detailed encoding process of the model in the appendix (Figure 3). + +pool the token-level features every 1.5 seconds to align with the video features. We then project the extracted features into a low-dimensional space via a linear layer, and add learned positional encoding (Devlin et al., 2018) after the projection. We denote the resulting video features as $E^{v} \in \mathbb{R}^{l \times d}$ , subtitle features as $E_{en}^{s} \in \mathbb{R}^{l \times d}$ , $E_{zh}^{s} \in \mathbb{R}^{l \times d}$ , and query features as $E_{en}^{q} \in \mathbb{R}^{l_{q} \times d}$ , $E_{zh}^{q} \in \mathbb{R}^{l_{q} \times d}$ . $l$ is video length, $l_{q}$ is query length, and $d$ is hidden size. The subscripts $en$ and $zh$ denote English and Chinese text features, respectively. + +Encoders and Parameter Sharing. We follow Lei et al. (2020) to use Self-Encoder as our main component for query and context encoding. A Self-Encoder consists of a self-attention (Vaswani et al., 2017) layer, a linear layer, and a residual (He et al., 2016) connection followed by layer normalization (Ba et al., 2016). We use a Self-Encoder followed by a modular attention (Lei et al., 2020) to encode each query into two modularized query vectors $\pmb{q}_{lang}^{v}, \pmb{q}_{lang}^{s} \in \mathbb{R}^{d}$ ( $lang \in \{en, zh\}$ ) for video and subtitle retrieval, respectively. For videos, we apply two Self-Encoders instead of a Self-Encoder and a Cross-Encoder as in XML, because we found this modification simplifies the implementation while maintains the performance. We use the outputs from the first and the second Self-Encoder $H_{vr,lang}^{v}, H_{mr,lang}^{v} \in \mathbb{R}^{l \times d}$ for video retrieval and moment retrieval. Similarly, we have $H_{vr,lang}^{s}, H_{mr,lang}^{s} \in \mathbb{R}^{l \times d}$ for subtitles. All the Self-Encoders are shared across languages, e.g., we use the same Self-Encoder to encode both English and Chinese queries, as illustrated in Figure 2. This parameter sharing strategy greatly reduces the + +model size while maintaining or even improving model performance, as we show in Section 4. + +Language Neighborhood Constraint. To facilitate stronger multilingual learning, we add neighborhood constraints (Wang et al., 2018; Kim et al., 2020; Burns et al., 2020) to the model. This encourages sentences that express the same or similar meanings to lie close to each other in the embedding space, via a triplet loss. Given paired sentence embeddings $e_{en}^{i} \in \mathbb{R}^{d}$ and $e_{zh}^{i} \in \mathbb{R}^{d}$ , we sample negative sentence embeddings $e_{en}^{j} \in \mathbb{R}^{d}$ and $e_{zh}^{k} \in \mathbb{R}^{d}$ from the same mini-batch, where $i \neq j, i \neq k$ . We use cosine similarity function $S$ to measure the similarity between embeddings. Our language neighborhood constraint can be formulated as: + +$$ +\begin{array}{l} \mathcal {L} _ {n c} = \frac {1}{n} \sum_ {i} [ \max (0, \mathcal {S} (\boldsymbol {e} _ {e n} ^ {i}, \boldsymbol {e} _ {z h} ^ {k}) - \mathcal {S} (\boldsymbol {e} _ {e n} ^ {i}, \boldsymbol {e} _ {z h} ^ {i}) + \Delta) \\ \left. + \max (0, \mathcal {S} (\pmb {e} _ {e n} ^ {j}, \pmb {e} _ {z h} ^ {i}) - \mathcal {S} (\pmb {e} _ {e n} ^ {i}, \pmb {e} _ {z h} ^ {i}) + \Delta) ], (1) \right. \\ \end{array} +$$ + +where $\Delta = 0.2$ is the margin. We apply this constraint on both query and subtitle embeddings, across the two languages, as illustrated in Figure 2. For queries, we directly apply it on the query vectors $q_{lang}^{v}, q_{lang}^{s}$ . For the subtitle embeddings, we apply it on the embeddings $H_{vr,lang}^{s}, H_{mr,lang}^{s}$ , after max-pooling them in the temporal dimension. + +Training and Inference. During training, we optimize video retrieval scores with a triplet loss, and moment scores with a cross-entropy loss. At inference, these two scores are aggregated together as the final score for video corpus moment retrieval. See appendix for details. + +# 4 Experiments and Results + +We evaluate our proposed mXML model on the newly collected MTVR dataset, and compare it with several existing monolingual baselines. We also provide ablation studies evaluating our model design and the importance of each input modality (videos and subtitles). + +Data Splits and Evaluation Metrics. We follow TVR (Lei et al., 2020) to split the data into $80\%$ train, $10\%$ val, $5\%$ test-public and $5\%$ test-private. We report average recall $(R@1)$ on the Video Corpus Moment Retrieval (VCMR) task. A predicted moment is correct if it has high Intersection-over-Union (IoU) with the ground-truth. + +Baseline Comparison. In Table 3, we compare mXML with multiple baseline approaches. Given + +
Method#paramEnglish R@1Chinese R@1
IoU=0.5IoU=0.7IoU=0.5IoU=0.7
Chance-0.000.000.000.00
Proposal based
MCN6.4M0.020.000.130.02
CAL6.4M0.090.040.110.04
Retrieval + Re-ranking
MEE+MCN10.4M0.920.421.430.64
MEE+CAL10.4M0.970.391.510.62
MEE+ExCL10.0M0.920.331.430.72
XML6.4M7.253.255.912.57
mXML4.5M8.303.826.763.20
+ +Table 3: Baseline comparison on MTVR test-public split. mXML achieves better retrieval performance on both languages while using fewer parameters. + +a natural language query, the goal of video corpus moment retrieval is to retrieve relevant moments from a large video corpus. The methods for this task can be grouped into two categories, $(i)$ proposal based approaches (MCN (Hendricks et al., 2017) and CAL (Escorcia et al., 2019)), where they perform video retrieval on the pre-segmented moments from the videos; $(ii)$ retrieval+re-ranking methods (MEE (Miech et al., 2018)+MCN, MEE+CAL, MEE+ExCL (Ghosh et al., 2019) and XML (Lei et al., 2020)), where one approach is first used to retrieve a set of videos, then another approach is used to re-rank the moments inside these retrieved videos to get the final moments. Our method mXML also belongs to the retrieval+re-ranking category. Across all metrics and both languages, we notice retrieval+re-ranking approaches achieve better performance than proposal based approaches, indicating that retrieval+re-ranking is potentially better suited for the VCMR task. Meanwhile, our mXML outperforms the strong baseline XML significantly4 while using few parameters. XML is a monolingual model, where a separate model is trained for each language. In contrast, mXML is multilingual, trained on both languages simultaneously, with parameter sharing and language neighborhood constraints to encourage multilingual learning. mXML prediction examples are provided in the appendix. + +Ablations on Model Design. In Table 4, we present our ablation study on mLX. We use 'Baseline' to denote the mLX model without parameter sharing and neighborhood constraint. Shar + +
Method#paramEnglish R@1Chinese R@1
IoU=0.5IoU=0.7IoU=0.5IoU=0.7
Baseline6.4M5.772.634.72.38
+ Share Enc.4.5M6.092.854.722.25
+ NC (mXML)4.5M6.222.965.172.41
+ +Table 4: mXML ablation study on MTVR val split. Share Enc. = encoder parameter sharing, NC = Neighborhood Constraint. Each row adds an extra component to the row above it. + +
Model TypeEnglish R@1Chinese R@1
IoU=0.5IoU=0.7IoU=0.5IoU=0.7
Query type: video
Baseline5.462.534.782.47
mXML5.772.675.142.32
Query type: subtitle
Baseline4.151.973.111.14
mXML6.123.324.051.87
Query type: video+subtitle
baseline8.023.385.182.62
mXML8.294.095.893.11
+ +ing encoder parameter across languages greatly reduces #parameters while maintaining (Chinese) or even improving (English) model performance. Adding neighborhood constraint does not introduce any new parameters but brings a notable $(p < 0.06)$ performance gain to both languages. We hypothesize that this is because the learned information in the embeddings of the two languages are complementary (though the sentences in the two languages express the same meaning, their language encoders (Liu et al., 2019; Cui et al., 2020)) are pre-trained differently, which may lead to different meanings at the embedding level. In Table 5, we show a detailed comparison between mXML and its baseline version, by query types. Overall, we notice the mXML perform similarly with Baseline in 'video' queries, but shows a significant performance gain in 'subtitle' queries, suggesting the parameter sharing and neighborhood constraint are more useful for queries that need more language understanding. + +Ablations on Input Modalities. In Table 6, we compare mXML variants with different context inputs, i.e., video or subtitle or both. We report their performance under the three annotated query types, + +Table 5: Comparison of mXML and the baseline on MTVR val set, with breakdown on query types. Both models are trained with video and subtitle as inputs. + +
QType (percentage)English R@1Chinese R@1
IoU=0.5IoU=0.7IoU=0.5IoU=0.7
Model input: video
video (74.32%)4.121.893.731.86
sub (8.85%)1.971.241.351.04
video+sub (16.83%)2.671.22.451.15
Model input: subtitle
video1.350.621.110.51
sub6.332.94.151.97
video+sub6.222.624.22.13
Model input: video+subtitle
video5.772.675.142.32
sub6.123.324.051.87
video+sub8.294.095.893.11
+ +Table 6: mXML performance breakdown on MTVR val set by query types, with different inputs. + +video, sub and video+sub. Overall, the model with both video and subtitle as inputs perform the best. The video model performs much better on the video queries than on the sub queries, while the subtitle model achieves higher scores on the sub queries than the video queries. + +In the appendix, we also present results on 'generalization to unseen TV shows' setup. + +# 5 Conclusion + +In this work, we collect MTVR, a new large-scale, multilingual moment retrieval dataset. It contains 218K queries in English and in Chinese from 21.8K video clips from 6 TV shows. We also propose a multilingual moment retrieval model mXML as a strong baseline for the MTVR dataset. We show in experiments that mXML outperforms monolingual models while using fewer parameters. + +# Acknowledgements + +We thank the reviewers for their helpful feedback. This research is supported by NSF Award #1562098, DARPA KAIROS Grant #FA8750-19-2-1004, and ARO-YIP Award #W911NF-18-1-0336. The views contained in this article are those of the authors and not of the funding agency. + +# References + +Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. 2016. Layer normalization. arXiv preprint arXiv:1607.06450. +Emily M Bender. 2009. Linguistically naïve! = language independent: Why nlp needs linguistic typology. In Proceedings of the EACL 2009 Workshop + +on the Interaction between Linguistics and Computational Linguistics: Virtuous, Vicious or Vacuous? +Andrea Burns, Donghyun Kim, Derry Wijaya, Kate Saenko, and Bryan A Plummer. 2020. Learning to scale multilingual representations for vision-language tasks. In ECCV. +Joao Carreira and Andrew Zisserman. 2017. Quo vadis, action recognition? a new model and the kinetics dataset. In CVPR. +David Chen and William B Dolan. 2011. Collecting highly parallel data for paraphrase evaluation. In ACL. +Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Shijin Wang, and Guoping Hu. 2020. Revisiting pretrained models for chinese natural language processing. In EMNLP. +Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805. +Daxiang Dong, Hua Wu, Wei He, Dianhai Yu, and Haifeng Wang. 2015. Multi-task learning for multiple language translation. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). +Bradley Efron and Robert J Tibshirani. 1994. An introduction to the bootstrap. CRC press. +Desmond Elliott, Stella Frank, Khalil Sima'an, and Lucia Specia. 2016. Multi30K: Multilingual English-German image descriptions. In Proceedings of the 5th Workshop on Vision and Language, pages 70-74, Berlin, Germany. Association for Computational Linguistics. +Victor Escorcia, Mattia Soldan, Josef Sivic, Bernard Ghanem, and Bryan Russell. 2019. Temporal localization of moments in video collections with natural language. arXiv preprint arXiv:1907.12763. +Haoyuan Gao, Junhua Mao, Jie Zhou, Zhiheng Huang, Lei Wang, and Wei Xu. 2015. Are you talking to a machine? dataset and methods for multilingual image question. In NIPS. +Jiyang Gao, Chen Sun, Zhenheng Yang, and Ram Nevatia. 2017. Tall: Temporal activity localization via language query. In ICCV. +Soham Ghosh, Anuva Agarwal, Zarana Parekh, and Alexander Hauptmann. 2019. Excl: Extractive clip localization using natural language descriptions. In *NAACL*. +Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In CVPR. + +Lisa Anne Hendricks, Oliver Wang, Eli Shechtman, Josef Sivic, Trevor Darrell, and Bryan Russell. 2017. Localizing moments in video with natural language. In ICCV. +Donghyun Kim, Kuniaki Saito, Kate Saenko, Stan Sclaroff, and Bryan A Plummer. 2020. Mule: Multimodal universal language embedding. In AAAI. +Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980. +Ranjay Krishna, Kenji Hata, Frederic Ren, Li Fei-Fei, and Juan Carlos Niebles. 2017. Dense-captioning events in videos. In ICCV. +Weiyu Lan, Xirong Li, and Jianfeng Dong. 2017. Fluency-guided cross-lingual image captioning. In ACM MM. +Jie Lei, Licheng Yu, Mohit Bansal, and Tamara L. Berg. 2018. Tvqa: Localized, compositional video question answering. In EMNLP. +Jie Lei, Licheng Yu, Tamara L Berg, and Mohit Bansal. 2020. Tvr: A large-scale dataset for video-subtitle moment retrieval. In ECCV. +Linjie Li, Yen-Chun Chen, Yu Cheng, Zhe Gan, Licheng Yu, and Jingjing Liu. 2020. Hero: Hierarchical encoder for video+ language omni-representation pre-training. In EMNLP. +Xirong Li, Chaoxi Xu, Xiaoxu Wang, Weiyu Lan, Zhengxiong Jia, Gang Yang, and Jieping Xu. 2019. Coco-cn for cross-lingual image tagging, captioning, and retrieval. TMM. +Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692. +Antoine Miech, Ivan Laptev, and Josef Sivic. 2018. Learning a text-video embedding from incomplete and heterogeneous data. arXiv preprint arXiv:1804.02516. +Eric W Noreen. 1989. Computer-intensive methods for testing hypotheses. Wiley New York. +Nikolaos Pappas, Miriam Redi, Mercan Topkara, Brendan Jou, Hongyi Liu, Tao Chen, and Shih-Fu Chang. 2016. Multilingual visual sentiment concept matching. In ICMR. +Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. 2017. Automatic differentiation in PyTorch. In NeurIPS Autodiff Workshop. +Michaela Regneri, Marcus Rohrbach, Dominikus Wetzel, Stefan Thater, Bernt Schiele, and Manfred Pinkal. 2013. Grounding action descriptions in videos. TACL. + +Devendra Sachan and Graham Neubig. 2018. Parameter sharing methods for multilingual self-attentional translation models. In Proceedings of the Third Conference on Machine Translation: Research Papers. + +Nobuyuki Shimizu, Na Rong, and Takashi Miyazaki. 2018. Visual question answering dataset for bilingual image understanding: A study of cross-lingual transfer using attention maps. In COLING. + +Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In NeurIPS. + +Liwei Wang, Yin Li, Jing Huang, and Svetlana Lazebnik. 2018. Learning two-branch neural networks for image-text matching tasks. TPAMI. + +Xin Wang, Jiawei Wu, Junkun Chen, Lei Li, Yuan-Fang Wang, and William Yang Wang. 2019a. Vatex: A large-scale, high-quality multilingual dataset for video-and-language research. In ICCV. + +Xin Wang, Jiawei Wu, Junkun Chen, Lei Li, Yuan-Fang Wang, and William Yang Wang. 2019b. Vatex: A large-scale, high-quality multilingual dataset for video-and-language research. In ICCV. + +Jun Xu, Tao Mei, Ting Yao, and Yong Rui. 2016. Msr-vtt: A large video description dataset for bridging video and language. In CVPR. + +Youngjae Yu, Jongseok Kim, and Gunhee Kim. 2018. A joint sequence fusion model for video question answering and retrieval. In ECCV. + +# A Appendix + +Data Analysis. In Table 8 we show a comparison of MTVR with existing moment retrieval datasets and related video and language datasets. Compared to other moment retrieval datasets, MTVR is significantly larger in scale, and comes with query type annotations that allows in-depth analyses for the models trained on it. Besides, it is also the only moment retrieval dataset with multilingual annotations, which is vital in studying the moment retrieval problem under the multilingual context. Compared to the existing multilingual video and language datasets, MTVR is unique as it has a more diverse set of context and annotations, i.e., dialogue, query type, and timestamps. + +Training and Inference Details. In Figure 3 we show an overview of the mXML model. We compute video retrieval score as: + +$$ +s ^ {v r} = \frac {1}{2} \sum_ {m \in \{v, s \}} \max \left(\frac {H _ {v r} ^ {m}}{\| H _ {v r} ^ {m} \|} \frac {\boldsymbol {q} ^ {m}}{\| \boldsymbol {q} ^ {m} \|}\right). \tag {2} +$$ + +
SettingEnglish R@1Chinese R@1
IoU=0.5IoU=0.7IoU=0.5IoU=0.7
unseen1.680.7910.54
seen4.822.794.182.32
+ +Table 7: mXML performance on the MTVR val split Friends examples, in both unseen and seen settings. + +The subscript $lang \in \{en, zh\}$ is omitted for simplicity. It is optimized using a triplet loss similar to main text Equation (1). For moment retrieval, we first compute the query-clip similarity scores $S^{q,c} \in \mathbb{R}^l$ as: + +$$ +S ^ {q, c} = \frac {1}{2} \left(H _ {m r} ^ {s} \boldsymbol {q} ^ {s} + H _ {m r} ^ {v} \boldsymbol {q} ^ {v}\right). \tag {3} +$$ + +Next, we apply Convolutional Start-End Detector (ConvSE module) (Lei et al., 2020) to obtain start, end probabilities $P_{st}, P_{ed} \in \mathbb{R}^l$ . These scores are optimized using a cross-entropy loss. The single video moment retrieval score for moment $[t_{st}, t_{ed}]$ is computed as: + +$$ +s ^ {m r} \left(t _ {s t}, t _ {e d}\right) = P _ {s t} \left(t _ {s t}\right) P _ {e d} \left(t _ {e d}\right), t _ {s t} \leq t _ {e d}. \tag {4} +$$ + +Given a query $q_{i}$ , the retrieval score for moment $[t_{st}:t_{ed}]$ in video $v_{j}$ is computed following the aggregation function as in (Lei et al., 2020): + +$$ +\begin{array}{l} s ^ {v c m r} \left(v _ {j}, t _ {s t}, t _ {e d} | q _ {i}\right) = \\ s ^ {m r} \left(t _ {s t}, t _ {e d}\right) \exp \left(\alpha s ^ {v r} \left(v _ {j} \mid q _ {i}\right)\right), \tag {5} \\ \end{array} +$$ + +where $\alpha = 20$ is used to assign higher weight to the video retrieval scores. The overall loss is a simple summation of video and moment retrieval loss across the two languages, and the language neighborhood constraint loss. + +Implementation Details. mL is implemented in PyTorch (Paszke et al., 2017). We use Adam (Kingma and Ba, 2014) with initial learning rate 1e-4, $\beta_{1} = 0.9$ , $\beta_{2} = 0.999$ , L2 weight decay 0.01, learning rate warm-up over the first 5 epochs. We train mL for at most 100 epochs at batch size 128, with early stop based on the sum of R@1 (IoU=0.7) scores for English and Chinese. The experiments are conducted on a NVIDIA RTX 2080Ti GPU. Each run takes around 7 hours. + +Generalization to Unseen TV shows. To investigate whether the learned model can be transferred to other TV shows, we conduct an experiment by using the TV show 'Friends' as an 'unseen' TV + +![](images/0bf5b9293e49d58f5a9d1ef18e5de445a84e35f85d0766e99092de3b7be7431e.jpg) +Figure 3: mXML overview. For brevity, we only show the modeling process for a single language (Chinese). The cross-language modifications, i.e., parameter sharing and neighborhood constraint are illustrated in Figure 2. This figure is edited from the Figure 4 in (Lei et al., 2020). + +
DatasetDomain#Q/#videosMultilingualDialogueQTypeTimestamp
QA datasets with temporal annotation
TVQA (Lei et al., 2018)TV show152.5K/21.8K-✓-✓
How2QA (Li et al., 2020)Instructional44K/22K-✓-✓
Multilingual video description datasets
MSVD (Chen and Dolan, 2011)Open70K/2K✓---
VATEX (Wang et al., 2019b)Activity826K/41.3K✓---
Moment retrieval datasets
TACoS (Regneri et al., 2013)Cooking16.2K/0.1K---✓
DiDeMo (Hendricks et al., 2017)Flickr41.2K/10.6K---✓
ActivityNet Captions (Krishna et al., 2017)Activity72K/15K---✓
CharadesSTA (Gao et al., 2017)Activity16.1K/6.7K---✓
How2R (Li et al., 2020)Instructional51K/24K-✓-✓
TVR (Lei et al., 2020)TV show109K/21.8K-✓✓✓
MTVRTV show218K/21.8K✓✓✓✓
+ +Table 8: Comparison of MTVR with related video and language datasets. + +show for testing, and train the model on all the other 5 TV shows. For comparison, we also include a model trained on 'seen' setting, where we use all the 6 TV shows including Friends for training. To ensure the models on these two settings are trained on the same number of examples, we downsample the examples in the seen setting to match the unseen setting. The results are shown in Table 7. We notice our mXML achieves a reasonable performance even though it does see a single example from the TV show Friends. Meanwhile, the gap between unseen and seen settings are still large, we encourage future work to further explore this direction. + +Prediction Examples We show mXML prediction examples in Figure 4. We show both Chinese (top) and English (bottom) prediction examples, and correct (left) and incorrect (right) examples. + +![](images/be118c57b1a575b0a3d2e26685bd219adfc6d209c3ac08a1bde730bdcb27ea7b.jpg) +00:48.033 $\rightarrow$ 00:52.265 +00:54.309→00:58.143 + +![](images/a9409595fe2251b7cf5a5a98bb67227a24d14b03cfc93f61d9181834086d15c3.jpg) +钱德勒：帮帮我啦... +瑞秋：钱德勒！好，够了.. +00:48.033 $\rightarrow$ 00:52.265 +00:54.309→00:58.143 + +![](images/85d447cbc6a2a4a4e94bc91ad700b59a53595f80498c663fbc3a9ffe6fb82ef7.jpg) +钱德勒：帮帮我啦.. +瑞秋：钱德勒！好，够了.. +00:08,737→00:10,790 +瑞秋：你是什么意思... +00:11,103→00:13,844 +你不想和任何人谈... + +![](images/b19bc279aa2b297c82c93093275a6dba1caf8e1415d10d7812ff304bfc59f99a.jpg) +00:54,309→00:58,143 +01:24,806 $\rightarrow$ 00:86,899 + +![](images/5e98d45c46dcf1b8f7ae629bc3c38f42f39faaa9009379aeb5438d8d2c31740b.jpg) +卡迪：如果你不想坐牢... +卡梅隆：那得看菌株 +00:02.098→00:05.518 +00:14.944→00:16.529 + +![](images/8f8dde0fe83d58958f6b4678dfc36973d83776650e8cccf7e21a4b3a5d3cb0a5.jpg) +豪斯：看，这就是面对面... +卡迪：让他出去。 +00:21,079→00:22,063 +卡迪：你剪坏了我那... +00:22,681 $\rightarrow$ 00:25,081 +豪斯：那张罕见的银版照片 + +![](images/88d5bb0e5bde57c437c19d3a021add21bf84c779f3f559e11aa189ee684c2c5f.jpg) +瑞秋从门上取下一把钥匙，以帮助钱德勒摆脱手铐。 +00:40.058→00:42.986 +00:53,680→00:55,801 + +![](images/eb456f67f71a0a7560834cc3f218b56ff188a013eafc8afe7420c449b5987856.jpg) +Marshall: If you guys... +Jerry: You were probably too young. +00:40,058→00:42,986 +00:53,680→00:55,801 + +![](images/05f434ace6112c04ffd7360794a0ee2dfc1c08728bf07c8465c31a8396688032.jpg) +Marshall: If you guys... +Jerry: You were probably too young. +00:00,382→00:01,800 +Barney: See those pinstripes? +00:01,925→00:02,925 +Barney: Diamonds. +Jerry shows Barney a picture at the dining room table. +Rachael runs to Ross, jumps on his back and takes the phone away from him. +Figure 4: Qualitative examples of mXML. Top: examples in Chinese. Bottom: examples in English. Left: correct predictions. Right: incorrect predictions. We show top-3 retrieved moments for each query. salmon bar shows the predictions, green box indicates the ground truth. + +![](images/2c5739a1dc1852ff3fc3bacea490e8a105bef77cc6d8aa9d25cdd5944c0b79c2.jpg) +卡迪从豪斯前面的桌子上拿起一些文件。 +00:36,230→00:41,133 +00:56,184→00:58,448 + +![](images/e57f9c5526f8e85a5776b3dd0f564ad42cf75531d475b6608b9c02d8c068bc76.jpg) +Okay, well, I can maybe gra +Ross: No, Rach! +00:15,317→00:17,046 +00:20,055→00:21,989 + +![](images/4651d50487aa52b1a26b0e24dbd54709a4d3b4747cb43a1398b046d2d22357ce.jpg) +Ross: I got a message from you... +Rachel: Give me the phone! +00:15.317→00:17.046 +Ross: I got a message from you... +00:20.055→00:21.989 +Rachel: Give me the phone! \ No newline at end of file diff --git a/mtvrmultilingualmomentretrievalinvideos/images.zip b/mtvrmultilingualmomentretrievalinvideos/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..b9f54b45516894c812a2ecf31fd0d888112cfa7f --- /dev/null +++ b/mtvrmultilingualmomentretrievalinvideos/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d4122c8b52eebf5cf284fc741bbe69da6498b442dff19810641e086193b5930e +size 651703 diff --git a/mtvrmultilingualmomentretrievalinvideos/layout.json b/mtvrmultilingualmomentretrievalinvideos/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..88303fc252403498c717299323bf78330eae3b00 --- /dev/null +++ b/mtvrmultilingualmomentretrievalinvideos/layout.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6b480ce024552190f7c4d8b5bd93fd6adfb51ae55db87aa898963b8437f4475f +size 378770 diff --git a/nmt5isparalleldatastillrelevantforpretrainingmassivelymultilinguallanguagemodels/19d83ad1-09dc-4497-8632-a93dd689f053_content_list.json b/nmt5isparalleldatastillrelevantforpretrainingmassivelymultilinguallanguagemodels/19d83ad1-09dc-4497-8632-a93dd689f053_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..90a19806699d01d2533152a83825d289255d4127 --- /dev/null +++ b/nmt5isparalleldatastillrelevantforpretrainingmassivelymultilinguallanguagemodels/19d83ad1-09dc-4497-8632-a93dd689f053_content_list.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d7cb468e820b7bafe494514a1f66490e96b8faf0e4ca8c198d6d32eaa3e8a19c +size 50480 diff --git a/nmt5isparalleldatastillrelevantforpretrainingmassivelymultilinguallanguagemodels/19d83ad1-09dc-4497-8632-a93dd689f053_model.json b/nmt5isparalleldatastillrelevantforpretrainingmassivelymultilinguallanguagemodels/19d83ad1-09dc-4497-8632-a93dd689f053_model.json new file mode 100644 index 0000000000000000000000000000000000000000..6aec43273b49ba3ffb26aadbdf838d12b13a3ef2 --- /dev/null +++ b/nmt5isparalleldatastillrelevantforpretrainingmassivelymultilinguallanguagemodels/19d83ad1-09dc-4497-8632-a93dd689f053_model.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:51f0338f02e8c56107260714160482bb335c118ce9ad16e3201f6fae0cb206b8 +size 60939 diff --git a/nmt5isparalleldatastillrelevantforpretrainingmassivelymultilinguallanguagemodels/19d83ad1-09dc-4497-8632-a93dd689f053_origin.pdf b/nmt5isparalleldatastillrelevantforpretrainingmassivelymultilinguallanguagemodels/19d83ad1-09dc-4497-8632-a93dd689f053_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..ac56af8948cd2720ce0821d983db742ad5e1b5bf --- /dev/null +++ b/nmt5isparalleldatastillrelevantforpretrainingmassivelymultilinguallanguagemodels/19d83ad1-09dc-4497-8632-a93dd689f053_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e64342e3ec9a1b7a3edaaafabb8a7006d84df9bcc938a7afcf67be04b4990f90 +size 279426 diff --git a/nmt5isparalleldatastillrelevantforpretrainingmassivelymultilinguallanguagemodels/full.md b/nmt5isparalleldatastillrelevantforpretrainingmassivelymultilinguallanguagemodels/full.md new file mode 100644 index 0000000000000000000000000000000000000000..cd2a6e05ec26965698517037690dc5f98d76cdf1 --- /dev/null +++ b/nmt5isparalleldatastillrelevantforpretrainingmassivelymultilinguallanguagemodels/full.md @@ -0,0 +1,178 @@ +# nmT5 - Is parallel data still relevant for pre-training massively multilingual language models? + +Mihir Kale* Aditya Siddhant* Rami Al-Rfou +Linting Xue Noah Constant Melvin Johnson +Google Research + +# Abstract + +Recently, mT5 - a massively multilingual version of T5 - leveraged a unified text-to-text format to attain state-of-the-art results on a wide variety of multilingual NLP tasks. In this paper, we investigate the impact of incorporating parallel data into mT5 pre-training. We find that multi-tasking language modeling with objectives such as machine translation during pretraining is a straightforward way to improve performance on downstream multilingual and cross-lingual tasks. However, the gains start to diminish as the model capacity increases, suggesting that parallel data might not be as essential for larger models. At the same time, even at larger model sizes, we find that pre-training with parallel data still provides benefits in the limited labelled data regime. + +# 1 Introduction + +Recent works have shown that cross-lingual transfer learning in pre-trained multilingual models such as mBERT (Devlin et al., 2019) and XLM-R (Conneau et al., 2020) could be improved further by using parallel data (Conneau and Lample, 2019; Hu et al., 2020a; Ouyang et al., 2020; Luo et al., 2020). In this paper, we continue this line of work by improving the recent mT5 model (Xue et al., 2020) by leveraging parallel corpora. We experiment with several text-to-text objectives that incorporate parallel data (spanning 198 language pairs) into mT5 pre-training. Our key findings are summarized below: + +- In the regime of very small fine-tuning datasets, objectives with parallel data improve results significantly. +- The gain from using parallel data decreases as we scale up the size of the pre-trained model. + +- Simple objectives based on neural machine translation (NMT) perform better than the traditionally employed "translation language modeling" (TLM) objective. + +# 2 Method + +We focus on the mT5-Large model, which is a 24 layer encoder-decoder transformer model and has shown strong performance on a variety of crosslingual benchmarks (Xue et al., 2020). Instead of training a new model from scratch, we start from the publicly available mT5-Large checkpoint - which has been trained for over 1 trillion tokens - and do a second stage pre-training with a mix of monolingual and parallel data. + +# 2.1 Objectives + +The mT5 - multilingual version of T5 (Raffel et al., 2020) - series of models were pre-trained on a multilingual version of the C4 corpus with a masked language modeling "span-corruption" objective (Raffel et al., 2020), where the encoder is fed a chunk of text with random spans replaced with a mask token, and the decoder must reconstruct the masked-out tokens. One of their primary distinctions is the use of a unified "text-to-text" format for all text-based NLP problems. + +In keeping with the text-to-text format, we experiment with the following objectives to incorporate parallel data into pre-training: + +- TLM - A text-to-text version of translation language modeling, proposed by Conneau and Lample (2019) and subsequently used in several prior works for encoder only pre-training. We trivially extend it to the encoder-decoder setting. +- NMT - Standard machine translation. The input is the source text and the target is its + +![](images/819bcdc5914aeae171fd31432c44ab6b48c9d421a4e0a9f276b628078bb427b2.jpg) +Figure 1: Example source and targets for different text-to-text style pre-training objectives incorporating parallel data. All objectives except TLM specify target language in the source sentence. + +translation. A language code is prefixed to the input to inform the model of the target language (Johnson et al., 2017). + +- Denoised-NMT - Similar to NMT, but we additionally mask spans in the source sentence. The model must now learn to implicitly perform language modeling of the source language while translating into the target language. +- Denoised-NMT+LM - Similar to Denoised-NMT, but instead of implicit language modeling, the model must explicitly predict the source text in addition to the translation. The target is a concatenation of the translation and source sentence, while the input is the masked source sentence. + +We refer to the model trained with the standard NMT objective as nmT5. + +# 3 Experiment Setup + +Pre-training datasets For pre-training we use monolingual data from mC4 (Xue et al., 2020) and parallel data from OPUS-100 (Zhang et al., 2020). OPUS-100 is a dataset of 55M translations covering 100 languages (198 language pairs, either into or from English). The mC4 corpus consists of unlabeled web text covering 101 languages, of which 81 overlap with the OPUS-100 languages. + +Fine-tuning datasets For downstream evaluation, we use the following four tasks: + +- TyDi QA (Clark et al., 2020) - The GoldP subtask, which corresponds to extractive question answering. The input is a passage and a question, with the answer being a span from the passage. +- MTOP (Li et al., 2020) - Multilingual Task-Oriented Parsing. The task is one of structured + +
DatasetLangsTrain sizeSetting
TyDi QA93.7Kzero-shot
MTOP622Kzero-shot
WikiAnn NER4020Kzero-shot
WikiLingua18660Kmultilingual
+ +Table 1: Statistics of datasets used in the paper. + +prediction, where user queries must be parsed into a tree, capturing the domain, intent and slots. + +- WikiAnn NER (Pan et al., 2019) - Named entity recognition task covering 40 languages featured in the XTREME benchmark (Hu et al., 2020b). There are 4 categories of entities - location, person, organization and miscellaneous. +- WikiLingua (Ladhak et al., 2020) - A recently introduced cross-lingual summarization dataset, where a document from an arbitrary language must be summarized in English. Since the dataset does not come with training and evaluation splits, we randomly create validation and test sets of 1000 examples each, and the rest of the data is used for training. + +Table 1 lists further details of each dataset. Following Xue et al. (2020), all tasks are cast into the text-to-text format. The evaluation for TyDi QA, MTOP and NER is done in the zero-shot setting, where the model is trained on the English data and evaluated on all languages. Since zero-shot cross-lingual language generation is much harder, for WikiLingua we train the model in a multilingual setting, using available training data for all languages. + +Hyperparameters Pre-training is done with a batch size of 1M tokens and fine-tuning with 131,072 tokens, with a constant learning rate of + +
Model (Metric)TyDi QA (F1/EM)MTOP (EM)NER (F1)WikiLingua (ROUGE-L)Avg.
mT566.3 / 49.843.758.425.246.3
+MLM (additional 100K steps)71.3 / 55.648.659.926.149.5
+MLM+TLM71.1 / 54.648.661.426.149.7
+MLM+NMT75.1 / 60.157.761.427.453.5
+MLM+denoised NMT75.3 / 60.256.561.527.453.3
+MLM+denoised NMT-LM75.0 / 59.456.062.426.953.1
+ +0.001. Starting from publicly available mT5-Large checkpoints, we further pre-train for 100K steps with a mix of monolingual and parallel objectives. The parallel data is mixed into monolingual data at a $10\%$ ratio, which amounts to roughly 4 passes over the OPUS-100 corpus. Examples from each language pair are sampled using the same language sampling distribution as Xue et al. (2020), with alpha=0.3. For downstream tasks, we fine-tune for 10K steps for TyDiQA, MTOP, NER and 25K for WikiLingua, since it is a much larger dataset. Checkpoint selection is done based on the validation set. + +Baselines Our first baseline is the publicly available mT5-Large model (1.3 billion parameters). For a fair comparison, we also experiment with an mT5 model further pre-trained for 100k steps with only monolingual data from mC4 (see row 2: mT5+MLM in Table 2). This lets us assess whether improvements stem from using parallel data or just pre-training for longer. + +# 4 Results + +We report results in table 2. Overall, adding parallel data through neural machine translation objectives improves scores for all 4 tasks, with the NMT objective performing the best. + +Simply pre-training mT5 for longer with just monolingual data (MLM) leads to improved scores for all tasks. The TLM objective is not be able to effectively leverage the parallel data and performs on par with MLM. On the other hand, our three NMT-based objectives show gains over MLM across all tasks. Among these, NMT and Denoised-NMT are the best and perform similarly, while Denoised-NMT+LM fares slightly worse. Averaged across all tasks, NMT and Denoised-NMT outperform + +MLM by 4 points. + +# 4.1 Model size + +Xue et al. (2020) find that cross-lingual performance of language models increases monotonically with model size. To study the impact of model capacity, we also experiment with larger model sizes. Even at the XL size (3.7B params, $3 \times$ larger than mT5-Large), we observe gains for all tasks with nmT5 (Table 3). However, the magnitude of the gains is largely diminished, hinting that the need for parallel data reduces as model capacity increases. This finding is particularly promising for low-resource languages, where it is difficult to obtain high-quality parallel data. + +At the same time, nmT5-Large substantially reduces the performance gap between mT5-Large and mT5-XL, covering $70\%$ of the headroom. Since bigger models are expensive to train and even more expensive to deploy, this opens up avenues for effectively using parallel data to improve performance of smaller language models. Turc et al. (2019) found that pre-training student models before model distillation is helpful, and using parallel data to improve student pre-training is another interesting avenue of future work. + +Table 2: Results are averaged across all the languages in each dataset. We report F1/EM for QA, exact match accuracy (EM) for structured prediction, ROUGE-L (Lin, 2004) for summarization and F1 for NER. Each score is the median over five runs. The final columns lists the average of all the scores. Refer to Appendix A for scores on individual languages. + +
ModelTyDi QAMTOPNERWikiLinguaAvg.
mT5-Large66.3 / 49.843.758.425.246.3
nmT5-Large75.1 / 60.157.761.427.453.5
Δ8.8 / 10.314.03.02.27.2
mT5-XL77.8 / 61.863.465.527.956.7
nmT5-XL78.4 / 63.364.966.228.457.6
Δ0.6 / 1.51.50.70.50.9
+ +Table 3: Impact of model size on nmT5's performance. + +
ModelFew-Shot (100)Low (3.7K)High (80K)
mT5-Large33.1 / 23.666.3 / 49.878.1 / 64.8
nmT5-Large48.8 / 37.175.1 / 60.178.2 / 65.5
Δ15.7 / 13.58.8 / 10.30.1 / 0.7
mT5-XL45.0 / 31.777.8 / 61.878.7 / 65.8
nmT5-XL57.2 / 44.478.4 / 63.379.7 / 67.0
Δ12.2 / 12.70.6 / 1.51.0 / 1.2
+ +Table 4: Performance on the TyDi QA eval set when fine-tuned in the few-shot (100 examples from TyDi QA English), low (full TyDi QA English with 3.7K examples) and high data regime (SQuAD English with 80K examples). + +# 4.2 Limited labeled data + +The TyDi QA dataset has only 3.7K English training examples. To study the impact of the size of fine-tuning data, we run experiments in two additional settings: a few-shot regime and a high data regime. Few-shot uses just 100 randomly sampled training examples, while for the latter we use the much larger SQuAD corpus (Rajpurkar et al., 2016), which consists of 80k examples. + +When fine-tuned with SQuAD, nmT5 performs slightly better than mT5 for both Large and XL model sizes. However, in the few-shot setting, nmT5-Large improves over mT5-Large by 15 points. Even at the XL size, nmT5 is over 10 points higher than mT5. nmT5-Large even outperforms the much larger mT5-XL. Our experiments suggest that pre-training with parallel data is particularly useful in the limited labelled data setting. + +# 4.3 Mixing ratio + +So far, we have mixed parallel data into monolingual data at a $10\%$ ratio. To assess how the mixing ratio impacts performance, we compare results with a $50\%$ mix. With the $50\%$ mix, average performance is slightly lower, validating our initial choice. + +
MixTyDi QAMTOPNERWikiLinguaAvg.
10%75.1 / 60.157.761.427.453.5
50%76.5 / 60.153.962.026.552.7
+ +Table 5: Impact of mixing ratio on nmT5. + +# 4.4 Performance on unseen languages + +We also test downstream performance on languages previously unseen by the models. We randomly pick 30 languages from the WikiAnn NER dataset + +that are not covered in either $\mathrm{mC4}^1$ or OPUS, and hence none of our models have seen them during pre-training. Table 6 shows nmT5 outperforms mT5 on this subset of languages as well, indicating that the representations of the nmT5 model are better suited for cross-lingual transfer. + +
Modelckbhsbxmf“Avg.”
mT5-Large66.564.858.454.9
nmT5-Large72.269.862.257.4
Δ5.75.03.82.5
+ +Table 6: Performance on three randomly picked unseen languages. "Avg." is calculated by averaging performance across 30 unseen languages. + +# 5 Related Work + +Pre-trained multilingual models such as mBERT and XLM-R have shown to be effective at cross-lingual transfer learning (Devlin et al., 2019; Conneau et al., 2020). Subsequently, many attempts have leveraged parallel data to improve cross-lingual capability of these models. Conneau and Lample (2019) proposed translation language modeling (TLM), to encourage the model to align representations across languages. Alternating language modeling (Yang et al., 2020) and back-translation masked language modeling (Ouyang et al., 2020) used code-switched sentences and back-translation respectively to utilize parallel data. Other works using parallel data in this line of work include FILTER (Fang et al., 2020), AMBER (Hu et al., 2020a) and, MMTE (Siddhant et al., 2020). A key factor that differentiates this paper from these works is that our pre-trained models use a text-to-text architecture, having both an encoder and a decoder, while the aforementioned models only have the encoder. Other pretrained multilingual encoder-decoder models such as mT5 (Xue et al., 2020), mBART (Liu et al., 2020) and MASS (Song et al., 2019) do not make use of parallel data during pretraining. + +# 6 Conclusion + +In this work we attempted to improve mT5 pretraining by incorporating parallel data. We experimented with various text-to-text objectives and found that multi-tasking with the standard neural machine translation objective during pre-training + +leads to improved cross-lingual transfer. The improvements from parallel data are most pronounced in the limited labeled data scenario. Our experiments also indicate that smaller models, with the help of parallel data, can approach the performance of larger ones, while also suggesting that the need for parallel data is lesser as the model capacity increases. + +# References + +Jonathan H Clark, Eunsol Choi, Michael Collins, Dan Garrette, Tom Kwiatkowski, Vitaly Nikolaev, and Jennimaria Palomaki. 2020. Tydi qa: A benchmark for information-seeking question answering in ty po- logically di verse languages. Transactions of the Association for Computational Linguistics, 8:454-470. +Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Édouard Grave, Myle Ott, Luke Zettle-moyer, and Veselin Stoyanov. 2020. Unsupervised cross-lingual representation learning at scale. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 8440-8451. +Alexis Conneau and Guillaume Lample. 2019. Crosslingual language model pretraining. In Advances in Neural Information Processing Systems. +Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171-4186, Minneapolis, Minnesota. Association for Computational Linguistics. +Yuwei Fang, Shuohang Wang, Zhe Gan, Siqi Sun, and Jingjing Liu. 2020. Filter: An enhanced fusion method for cross-lingual language understanding. arXiv preprint arXiv:2009.05166. +Junjie Hu, Melvin Johnson, Orhan First, Aditya Siddhant, and Graham Neubig. 2020a. Explicit alignment objectives for multilingual bidirectional encoders. arXiv preprint arXiv:2010.07972. +Junjie Hu, Sebastian Ruder, Aditya Siddhant, Graham Neubig, Orhan First, and Melvin Johnson. 2020b. Xtreme: A massively multilingual multi-task benchmark for evaluating cross-lingual generalisation. In International Conference on Machine Learning. +M. Johnson, Mike Schuster, Quoc V. Le, M. Krikun, Y. Wu, Z. Chen, Nikhil Thorat, F. Viégas, M. Wattenberg, G. S. Corrado, Macduff Hughes, and J. Dean. + +2017. Google's multilingual neural machine translation system: Enabling zero-shot translation. Transactions of the Association for Computational Linguistics, 5:339-351. +Faisal Ladhak, Esin Durmus, Claire Cardie, and K. McKeown. 2020. Wikilingua: A new benchmark dataset for cross-lingual abstractive summarization. ArXiv, abs/2010.03093. +Haoran Li, A. Arora, Shuohui Chen, Anchit Gupta, Sonal Gupta, and Yashar Mehdad. 2020. Mtop: A comprehensive multilingual task-oriented semantic parsing benchmark. *ArXiv*, abs/2008.09335. +Chin-Yew Lin. 2004. Rouge: A package for automatic evaluation of summaries. In Text summarization branches out, pages 74-81. +Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, and Luke Zettlemoyer. 2020. Multilingual denoising pre-training for neural machine translation. Transactions of the Association for Computational Linguistics, 8:726-742. +Fuli Luo, Wei Wang, Jiahao Liu, Yijia Liu, Bin Bi, Songfang Huang, Fei Huang, and Luo Si. 2020. Veco: Variable encoder-decoder pre-training for cross-lingual understanding and generation. arXiv preprint arXiv:2010.16046. +Xuan Ouyang, Shuohuan Wang, Chao Pang, Yu Sun, Hao Tian, Hua Wu, and Haifeng Wang. 2020. Ernie-m: Enhanced multilingual representation by aligning cross-lingual semantics with monolingual corpora. arXiv preprint arXiv:2012.15674. +Xiaoman Pan, Thamme Gowda, Heng Ji, Jonathan May, and Scott Miller. 2019. Cross-lingual joint entity and word embedding to improve entity linking and parallel sentence mining. In Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019), pages 56–66, Hong Kong, China. Association for Computational Linguistics. +Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research, 21(140):1-67. +Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016. Squad: 100, $000+$ questions for machine comprehension of text. In EMNLP. +Aditya Siddhant, Melvin Johnson, Henry Tsai, Naveen Ari, Jason Riesa, Ankur Bapna, Orhan Firat, and Karthik Raman. 2020. Evaluating the cross-lingual effectiveness of massively multilingual neural machine translation. In Proceedings of the AAAI Conference on Artificial Intelligence. + +Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and TieYan Liu. 2019. Mass: Masked sequence to sequence pre-training for language generation. In International Conference on Machine Learning. +Iulia Turc, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. Well-read students learn better: On the importance of pre-training compact models. arXiv: Computation and Language. +Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, and Colin Raffel. 2020. mt5: A massively multilingual pre-trained text-to-text transformer. arXiv preprint arXiv:2010.11934. +Jian Yang, Shuming Ma, Dongdong Zhang, ShuangZhi Wu, Zhoujun Li, and Ming Zhou. 2020. Alternating language modeling for cross-lingual pre-training. In Proceedings of the AAAI Conference on Artificial Intelligence. +Biao Zhang, Philip Williams, Ivan Titov, and Rico Senrich. 2020. Improving massively multilingual neural machine translation and zero-shot translation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. + +# A Per-Language Results on All Tasks + +
enarbnfiid
mt575.0 / 63.068.9 / 51.454.5 / 37.270.4 / 54.674.3 / 57.0
+MLM78.5 / 68.276.1 / 59.959.0 / 40.773.5 / 61.076.7 / 60.0
+MLM+TLM77.3 / 67.075.7 / 57.261.7 / 39.873.3 / 59.077.0 / 60.0
+MLM+NMT78.4 / 69.378.9 / 63.174.0 / 54.977.0 / 64.879.9 / 64.8
+MLM+denoised NMT78.7 / 68.679.8 / 64.772.6 / 53.177.2 / 64.279.8 / 67.6
+MLM+denoised NMT-LM78.2 / 68.278.8 / 62.369.1 / 49.678.2 / 65.779.6 / 64.8
koruswteavg
mt557.4 / 47.561.5 / 37.169.7 / 52.565.5 / 48.066.3 / 49.8
+MLM64.4 / 55.468.6 / 48.974.2 / 57.771.1 / 48.671.3 / 55.6
+MLM+TLM66.5 / 55.867.8 / 48.073.9 / 57.166.5 / 47.571.1 / 54.6
+MLM+NMT64.9 / 56.272.1 / 51.877.2 / 63.173.3 / 53.175.1 / 60.1
+MLM+denoised NMT67.9 / 58.771.9 / 51.575.7 / 59.774.3 / 53.575.3 / 60.2
+MLM+denoised NMT-LM67.8 / 59.472.7 / 51.176.0 / 59.974.4 / 54.075.0 / 59.4
+ +Table 7: TyDi QA GoldP results (F1/EM) for each language. + +
endeesfrhithavg
mt583.541.245.443.321.327.543.7
+MLM83.344.546.351.831.934.048.6
+MLM+TLM85.042.447.549.631.835.248.6
+MLM+NMT86.155.159.061.742.242.157.7
+MLM+denoised NMT85.851.655.259.542.743.956.5
+MLM+denoised NMT-LM85.951.955.057.044.141.956.0
+ +Table 8: MTOP results (EM) for each language. + +
enafarbgbndeeleseteufafifrhe
mt580.564.547.757.266.567.063.962.059.045.541.456.976.745.1
+MLM81.465.150.255.269.368.666.970.562.846.644.958.976.646.4
+MLM+TLM82.465.648.867.272.270.170.872.661.247.547.161.478.748.0
+MLM+NMT82.264.256.761.069.170.564.666.366.249.348.960.678.446.2
+MLM+denoised NMT82.565.750.363.669.670.768.673.764.948.644.363.377.745.5
+MLM+denoised NMT-LM82.966.149.567.774.571.171.374.267.149.944.863.280.249.6
hihuiditjajvkakkkomlmrmsmynl
mt566.857.744.975.436.046.053.022.529.544.838.665.527.077.3
+MLM66.561.446.276.435.849.053.623.731.446.039.367.433.078.5
+MLM+TLM69.661.947.276.737.351.059.429.330.748.242.170.229.080.4
+MLM+NMT69.861.746.177.334.553.055.227.031.443.046.769.027.078.9
+MLM+denoised NMT65.863.046.677.637.054.058.326.429.844.842.164.330.080.2
+MLM+denoised NMT-LM67.764.448.177.939.249.059.430.031.447.436.471.034.080.2
ptruswtatethtltrurviyozhavg
mt573.148.466.839.937.98.577.857.645.176.458.041.858.4
+MLM75.547.364.540.538.09.276.956.551.776.959.041.859.9
+MLM+TLM76.358.866.340.241.28.876.962.043.079.656.043.561.4
+MLM+NMT75.556.065.840.341.68.078.760.357.079.863.041.061.4
+MLM+denoised NMT75.558.966.240.440.47.978.760.550.080.364.041.461.5
+MLM+denoised NMT-LM78.660.965.640.640.99.177.063.153.579.860.045.562.4
+ +Table 9: WikiAnn NER results (F1) for each language. + +
enarcsdeesfrhiiditja
mt529.223.222.425.025.324.625.225.324.126.2
+MLM30.024.022.926.026.625.526.125.824.927.8
+MLM+TLM30.024.423.125.626.325.626.425.825.127.6
+MLM+NMT31.525.724.027.027.526.427.727.025.829.5
+MLM+denoised NMT31.325.724.727.327.526.827.827.225.829.2
+MLM+denoised NMT-LM30.825.023.726.527.126.327.326.725.628.7
konlptruthtrvizhavg
mt523.825.724.623.925.330.922.925.825.2
+MLM25.226.525.324.627.131.123.227.126.1
+MLM+TLM24.726.625.224.426.531.323.327.026.1
+MLM+NMT26.727.726.325.928.634.123.928.127.4
+MLM+denoised NMT26.628.025.925.828.333.424.328.427.4
+MLM+denoised NMT-LM25.927.425.624.927.333.123.827.826.9
+ +Table 10: Wikilingua results (Rouge-L) for each language. + +
acearzastbaceckbcsbemlfurgangn
mt5-Large44.850.883.338.121.766.556.739.864.242.148.2
nmt5-Large46.753.684.843.728.372.258.141.965.641.251.0
hsbiajbolijlmominnapnovpdcpmspnb
mt5-Large64.863.242.146.369.839.162.262.148.181.561.1
nmt5-Large69.862.443.643.072.045.561.766.751.283.555.4
rmsatlquvecvepvlsxmfavg
mt5-Large64.117.478.627.566.963.674.458.454.9
nmt5-Large67.623.079.435.666.768.077.562.257.4
+ +Table 11: WikiAnn NER results on unseen languages. Refer to section 4.4 \ No newline at end of file diff --git a/nmt5isparalleldatastillrelevantforpretrainingmassivelymultilinguallanguagemodels/images.zip b/nmt5isparalleldatastillrelevantforpretrainingmassivelymultilinguallanguagemodels/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..a9f4af67ddfdce0f8035516f2c7db8d8757bd105 --- /dev/null +++ b/nmt5isparalleldatastillrelevantforpretrainingmassivelymultilinguallanguagemodels/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:497a0f621bcce28ddaa480fc2d65329502eded0c5d5abc19f80d8e03dcc7e446 +size 665582 diff --git a/nmt5isparalleldatastillrelevantforpretrainingmassivelymultilinguallanguagemodels/layout.json b/nmt5isparalleldatastillrelevantforpretrainingmassivelymultilinguallanguagemodels/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..f180a514c526057897d0cc770649311fa0daa49e --- /dev/null +++ b/nmt5isparalleldatastillrelevantforpretrainingmassivelymultilinguallanguagemodels/layout.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:631b089a2eeb02f4995e8e161cdd9b77bb6919b604fe4d8f06880708c66e81c5 +size 193547 diff --git a/towardsamorerobustevaluationforconversationalquestionanswering/f9a5ee66-6d8b-4a18-b856-04b75950ca1b_content_list.json b/towardsamorerobustevaluationforconversationalquestionanswering/f9a5ee66-6d8b-4a18-b856-04b75950ca1b_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..1591c80f645c1af080841a1b042fb40324735ea7 --- /dev/null +++ b/towardsamorerobustevaluationforconversationalquestionanswering/f9a5ee66-6d8b-4a18-b856-04b75950ca1b_content_list.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2b6abeceacfd51f1ce88dfe4aa17e6065f0a6114dffdd36509005520e8f59a2a +size 42740 diff --git a/towardsamorerobustevaluationforconversationalquestionanswering/f9a5ee66-6d8b-4a18-b856-04b75950ca1b_model.json b/towardsamorerobustevaluationforconversationalquestionanswering/f9a5ee66-6d8b-4a18-b856-04b75950ca1b_model.json new file mode 100644 index 0000000000000000000000000000000000000000..3a5a9b004465b55cd176b785eb499da7f01db752 --- /dev/null +++ b/towardsamorerobustevaluationforconversationalquestionanswering/f9a5ee66-6d8b-4a18-b856-04b75950ca1b_model.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0cc3341644d33a50bcce558614fef19427d8ce9c02de6888f27000795f191879 +size 52085 diff --git a/towardsamorerobustevaluationforconversationalquestionanswering/f9a5ee66-6d8b-4a18-b856-04b75950ca1b_origin.pdf b/towardsamorerobustevaluationforconversationalquestionanswering/f9a5ee66-6d8b-4a18-b856-04b75950ca1b_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..1481fabe080aed6336fd9483b80f9c1763ce5414 --- /dev/null +++ b/towardsamorerobustevaluationforconversationalquestionanswering/f9a5ee66-6d8b-4a18-b856-04b75950ca1b_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c68eccc111e129d586e988222975e3bd2b8d0720777257f3e2228d7b61eb40b3 +size 186536 diff --git a/towardsamorerobustevaluationforconversationalquestionanswering/full.md b/towardsamorerobustevaluationforconversationalquestionanswering/full.md new file mode 100644 index 0000000000000000000000000000000000000000..f789014a89ed855d83c7637a62e7c87f62b7a0af --- /dev/null +++ b/towardsamorerobustevaluationforconversationalquestionanswering/full.md @@ -0,0 +1,171 @@ +# Towards a more Robust Evaluation for Conversational Question Answering + +Wissam Siblini, Baris Sayil, Yacine Kessaci + +Worldline, France + +{wissam.siblini,yacine.kessaci}@worldline.com + +baris.sayil@insa-lyon.fr + +# Abstract + +With the explosion of chatbot applications, Conversational Question Answering (CQA) has generated a lot of interest in recent years. Among proposals, reading comprehension models which take advantage of the conversation history (previous QA) seem to answer better than those which only consider the current question. Nevertheless, we note that the CQA evaluation protocol has a major limitation. In particular, models are allowed, at each turn of the conversation, to access the ground truth answers of the previous turns. Not only does this severely prevent their applications in fully autonomous chatbots, it also leads to unsuspected biases in their behavior. In this paper, we highlight this effect and propose new tools for evaluation and training in order to guard against the noted issues. The new results that we bring come to reinforce methods of the current state of the art. + +# 1 Introduction + +The ability to automatically answer questions from a set of raw text paragraphs has long been coveted by computer scientists (Woods, 1977). For applications in search engines, one could consider an isolated task where a user formulates a single question (Croft et al., 2010; Siblini et al., 2020). But recently, with usage in conversational agents (e.g. chatbots), a more contextualized variant referred to as Conversational Question Answering (CQA) has attracted a great deal of attention (Reddy et al., 2019; Choi et al., 2018). CQA differs from traditional (extractive) Question Answering (Rajpurkar et al., 2016) because Question-Answer (QA) pairs are not single but come in sequences within conversations. Therefore, models can use previous turns as context to extract the answer of the current question (Zhu et al., 2018; Huang et al., 2018; Qu et al., 2019a). In some cases, the history is even crucial to disambiguate pronouns in the question. + +Similarly to other NLP tasks, the state-of-the-art approaches for CQA are variants of the Transformer Encoder (Vaswani et al., 2017), a deep neural network with several self-attention layers that produce contextualized representations of the "tokens" (words, subwords) that compose a text. For instance, models like BERT (Devlin et al., 2019; Lan et al., 2019; Sanh et al., 2019) obtain a more than decent performance on CQA datasets like QuAC (Choi et al., 2018) or CoQA (Reddy et al., 2019). However, they miss the context to fully understand the questions. Proposals have been made to integrate the history in several manners: using a recursive strategy (Huang et al., 2018), appending previous QAs to the current question as input (Zhu et al., 2018), and contextualizing the question–paragraph pair with respect to the history. We can mention in particular BERT-HAE and BERT-PHAE (Qu et al., 2019a,b) which improve BERT in a simple yet efficient way by encoding, in addition to segment and position, the fact that parts of the paragraph's words belonged to previous answers. + +# 2 Motivation and main contributions + +Our objective here is not to propose yet another model to try to obtain the best predictive score on CQA leaderboards. Instead, we focus our thinking around the current evaluation/training protocols with regards to the possible application cases. The starting point of our reflection is that currently, when evaluated on CQA datasets, models like BERT-HAE use the ground-truth answers of previous turns as context to answer the current question. This limits the scope of applicability to only a "semi-automatic" bot that would require a human providing supervision at each turn. We also show how it biases the selection of models towards those with an undesired filter behavior. + +To make approaches from the literature usable + +in more difficult/realistic scenarios like standalone chatbots (in which they can only access the previous questions and their predictions of the answers), we make the following contributions: (1) We implement new evaluation tools to first highlight the current unnoticed and undesirable behavior: in ground-truth free conditions, CQA approaches can become even less accurate than baselines like BERT which do not exploit the history at all. (2) We develop the analog training protocol to make approaches robust to the observed issues. In particular, this gives back state-of-the-art models the strength to outperform the baseline but this time in a scenario that connects better to real-world conversational agents. Our work comes with an implementation of conversational QA tools, based on the most widely used transformers library (Wolf et al., 2019). + +# 3 Conversational Question Answering + +Conversational Question Answering (CQA) is a Natural Language Processing task related to Machine Comprehension (MC) (Zhang et al., 2019; Gupta et al., 2020). MC has grown significantly over the last decade, particularly thanks to (1) large scale datasets such as SQuAD (Rajpurkar et al., 2016) or Natural Questions (Kwiatkowski et al., 2019), (2) the improvement of representation learning models (Joulin et al., 2017), (3) powerful mechanisms such as attention (Yang et al., 2016; Vaswani et al., 2017), and (4) the emergence of several related topics like multi-lingual modeling (Pires et al., 2019; Siblini et al., 2019) or Conversational Question Answering (Choi et al., 2018; Reddy et al., 2019). + +In CQA, questions are grouped in conversations and often require the context, i.e. previous QA turns, to be fully understandable. QuAC (Choi et al., 2018) and CoQA (Reddy et al., 2019) are two examples of CQA datasets. They were both generated by humans (a "student" and a "teacher") through conversations where the student asks a series of questions, complementary or not, on a given paragraph and the teacher answers them. In this paper, we focus on QuAC (Question Answering in Context) which is more recent and described as more challenging than CoQA (Choi et al., 2018). It contains 14k conversations and around 100k question-paragraph pairs, split into a training set (11,567 conversations / 83,568 questions), a validation set (1,000 conversations / 7,354 questions) and a test set. It evaluates models with several metrics, + +the main one being the F1-score (Flach, 2003). + +Models proposed for QuAC are similar to those developed for SQuAD (e.g. BiDAF (Seo et al., 2016) or BERT (Devlin et al., 2019)) but they additionally integrate the history. A popular example is BERT-HAE (Qu et al., 2019a). It uses BERT's architecture but modifies the input embedding layer to add a novel component: the History Answer Embedding (HAE). As usual, the input question-paragraph pair is tokenized and marked with positions and segments. Then, an additional History Answer marker is added to indicate whether the tokens belonged to answers of previous questions or not, and the resulting embedding is simply added to the other embedding vectors (token, position, segment) before the self-attention blocks. BERT-HAE was enhanced, in a later publication (Qu et al., 2019b) by BERT-PHAE (Positional HAE) which additionally encodes the turn position of the answers in the history. Although very promising, we note that BERT-HAE and BERT-PHAE, as well as other state of the art models for QuAC, access the ground-truth answers of previous turns during evaluation. Therefore, reported results only reflect the performance within a reduced scope of applicability. In the following, we detail this limitation and propose to complement the current protocol in order to improve both evaluation and training. + +# 4 A more Robust Protocol + +Consider a standalone chatbot that successively answers questions from documents. At each turn, it cannot know for sure the ground truth (GT) answers of the previous turns except if the user or another human provides supervision. This could happen in scenarios where the role of the algorithm is only to provide answer suggestions (semi-automatic) to a human agent (e.g. in customer support). However, applications often seek bots where the question-answer loop is automated (standalone). Here we investigate this second setting. We start by reproducing the literature results on the semi-automatic scenario, then we exhibit the limits and propose solutions for our target scenario. + +# 4.1 Reproducing the Regular Evaluation in the Semi-automatic Scenario + +To evaluate the baseline performance (semi-automatic), we train BERT-HAE and BERT-PHAE on QuAC using the protocol described by the authors (Qu et al., 2019a) and the same hyperparame + +ters: history markers from up to 6 turns, and specific optimization parameters (12 as batch size, 3e-5 as learning rate with a linear decrease to 0 over 24k training steps). We implement our own training script on the basis of codes pieces from the transformers library (Wolf et al., 2019) and BERT-HAE's authors1. Experiments are run with a Nvidia Tesla V100 GPU. + +
ModelF1Uses history
BiDAF++ (Choi et al., 2018)51.8No
BERT (Qu et al., 2019a)54.4 (54.8)No
BERT-HAE (Qu et al., 2019a)63.1 (63.4)Yes
BERT-PHAE (Qu et al., 2019b)64.7 (64.4)Yes
+ +Table 1: F1-score of BERT, BERT-HAE, BERT-PHAE and a previous baseline on QuAC using the regular evaluation protocol. We display the original results published by the authors and the ones we reproduced (in parentheses). + +Our results are roughly equal to those previously reported (Table 1). BERT's F1 score is 54.8, which compares favorably to previous baselines such as BiDAF. By adjusting the representation of the tokens based on the history of answers, BERT-HAE allows a significant improvement to 63.4 (+15.7%). The position of the turns in the history also has its importance allowing BERT-PHAE to further improve the F1-score to 64.4. This is probably because questions are often related to the answers that directly precede them. To improve the results even further, one can also select a specific subset of turns in the history (Qu et al., 2019b). + +# 4.2 Critical Analysis: The Filtering Behavior + +Although promising, the aforementioned results need to be considered with caution. A hasty conclusion is that adding the history allows the model to benefit from a context and hence to better process the current question. However the improvement could also be explained by a bias in the dataset at hand. Indeed, this question answering task is extractive, i.e. answers are selected from a paragraph. In the course of a conversation in QuAC, an average of 7 questions are successively asked on the same rather small paragraph. Thus simply filtering the paragraph tokens with the answer history provides the advantage of reducing considerably the list of possible remaining answers. Note however that such a filtering could also have a negative effect, in the presence of overlap between answers. + +To get better insights of the impact of a filtering behavior in practice, we run three experiments. + +
ModelF1F1 w/ post filtering
BEST95.692.7
BERT54.856.9
BERT-HAE63.462.5
+ +Table 2: Evaluation of the impact of post-filtering on BEST, BERT and BERT-HAE. + +Experiment 1: The negative impact of filtering due to overlap We first compute the best reachable F1-score (that we refer to as BEST) as if we had a model that always predicts the expected answer. Then we compute "BEST w/ post filtering" with the same predictions except that we post-filter all tokens that belong the 6 previous turns' answers, except for the "Cannot Answer" tokens (reserved for unanswerable questions). BEST F1 score is $95.6^2$ while "BEST F1 w/ post filtering" is lower but very close: 92.7 (Table 2). This tells us that the maximal negative impact of a filtering strategy on QuAC is weak. We find an explanation by doing proportion measurements in QuAC's eval set: in particular, the percentage of overlapping tokens (resp. non overlapping tokens) between answers is low (resp. high): $5.7\%$ (resp. $74.1\%$ ), the other $20.2\%$ being the "Cannot Answer" tokens. + +Experiment 2: Global impact of a post filtering on the models After 6 turns, sometimes almost half of the paragraph tokens belong to the history of answers. Even if experiment 1 suggests a negative impact of filtering due to overlap, the positive impact on our baselines (due to the significant reduction of the number of candidate answers) could counterbalance. We therefore re-evaluate the models trained in section 4.1, but this time we apply a post processing of their predictions: the start/end logits of tokens that belong to the answers of previous turns are set to $-\infty$ , except for the "Cannot Answer" tokens. This forces previous answers to be excluded from the final predicted span text. This simple strategy to integrate the history in BERT allows an improvement to an F1-score of 56.9 (Table 2). On the contrary, it globally reduces the score + +of BERT-HAE to 62.5 (a reduction factor slightly lower than with BEST). These results suggest that access to ground-truth answers of previous turns allows in QuAC, in which the overlap is weak, a filtering mechanism to be a positive way of integrating history. Results also suggest that BERT-HAE might already implicitly integrate a filtering behavior. Unquestionably, it does it in a more expressive manner than our hard post-processing, since the history markers are passed as inputs to the model. + +Experiment 3: Does BERT-HAE exhibit a filter behavior? Although suggested by the previous experiment, we want to answer this question more clearly. We consider an experiment aligned with the philosophy of adversarial attacks (Akhtar and Mian, 2018; Morris et al., 2020). During evaluation, we systematically modify the history answer markers so that the tokens of the current expected answer are marked as if they belonged to the history. The results obtained from this evaluation protocol are displayed under the column "F1 w/ Adv" in Table 3. F1 w/ Adv allows to measure, with the F1 metric, the ability of the models to answer a question when its answer has already appeared in the conversation before. In this condition, we observe a dramatic drop in BERT-HAE's performance (from 63.4 to 41.7), and an even worse for BERT-PHAE. This confirms that these models tend to output lower probabilities for tokens that are in the history, which suggests a filtering behavior and makes their usage potentially counter productive. + +# 4.3 Proposed Evaluation for the Standalone Scenario + +The current evaluation protocol on QuAC's validation set can bias model selection towards those able to implement a filtering behavior, which seems to be the case for BERT-(P)HAE. Thus, it does not guarantee a robust behavior in a fully autonomous bot. Here we propose an extension. + +Inspired by the literature of recurrent models, we refer to the regular evaluation protocol, which access to ground truth answers of previous turns, as the "Teacher Forcing" (w/ TF) protocol. Analogically, we consider a mode "without Teacher Forcing" (w/o TF) where models process a conversation in the natural order and only use their predictions as history. The latter is outlined in Algorithm 1, where "build_mark" refers to a function that computes the new HAE markers given the previous ones and the new answer. + +Note that the algorithm for evaluation w/ TF simply replaces "build_mark(HAE,answerpred)" with "build_mark(HAE,answerGT)". + +Algorithm 1 Evaluation w/o TF +1: s ← 0 +2: for conversation ∈ valid set do +3: HAE ← None +4: for turn ∈ conversation do +5: question ← turn['question'] +6: answerGT ← turn['answer'] +7: answerpred ← model(question, HAE) +8: HAE ← build_mark(HAE, answerpred) +9: s ← s + F1(answerpred, answerGT) +10: end for +11: end for +12: return s / card(valid set) + +When we take the models trained in section 4.1 (w/ TF) and evaluate them with the new standalone protocol (w/o TF), Table 3 shows that the performance drops from 63.4 to 53.5 with BERT-HAE and from 64.4 to 54.2 with BERT-PHAE. Concretely, although unsuspected with the original protocol, the approaches do not necessarily seem advantageous compared to BERT here. This in no way detracts the interest of these proposals, which implement clever architectures to integrate the history. It only prevents their application, as is, in the standalone scenario. Nevertheless, now that this issue is identified, we can try to design an appropriate strategy to avoid it from the start, by taking measures at the training phase. + +# 4.4 Training for the Standalone Scenario + +To complement the proposed evaluation protocol with a training one, we propose to apply a recipe inspired by the most popular defense mechanism against adversarial attacks called adversarial training (Ren et al., 2020), i.e. we introduce the disruptive element (here the mode without Teacher Forcing) at training time. We consider three heuristics: (1) we disable TF during all the training steps (Robust), (2) we disable TF randomly based on a Coin Flip (Robust-CF), (3) we progressively disable TF from $0\%$ of the steps to $100\%$ of the steps over the training iterations (Robust-P). The new training process is detailed in Algorithm 2, where "update" refers to the optimization algorithm that updates the model based on the loss and "heuristic_condition" is a condition that depends on the + +heuristic (e.g. always true for heuristic (1)). Note that both heuristics (2) and (3) are inspired from scheduled sampling methods (Bengio et al., 2015) adapted to the context of CQA. + +# Algorithm 2 Robust Training + +1: for conversation $\in$ train set do +2: HAE $\leftarrow$ None +3: for turn $\in$ conversation do +4: question $\leftarrow$ turn['question'] +5: answerGT $\leftarrow$ turn['answer'] +6: answerpred $\leftarrow$ model(question, HAE) +7: $l \leftarrow$ loss( answerpred, answerGT) +8: update(model,l) +9: if heuristic_condition then +10: answeradd $\leftarrow$ answerpred +11: else +12: answeradd $\leftarrow$ answerGT +13: end if +14: HAE $\leftarrow$ build_mark(HAE, answeradd) +15: end for +16: end for +17: return model + +We obtain encouraging results (Table 3). In particular, BERT-PHAE Robust-P reaches a F1-score of 58.1 in the standalone scenario which is better than BERT's F1. Besides, "F1 /w Adv" for BERT(P)HAE Robust seems to indicate that, the less we apply TF, the less the entailed model exhibits a filtering behavior. In fact, all the robust variants exhibit a weaker filtering behaviour than the original methods. + +
ModelF1 w/ TFF1 w/o TFF1 w/ Adv
BERT-54.4-
BERT-HAE63.453.541.7
BERT-HAE Robust59.556.651.7
BERT-HAE Robust-CF61.655.947.4
BERT-HAE Robust-P60.756.750.7
BERT-PHAE64.454.240.7
BERT-PHAE Robust60.557.453.3
BERT-PHAE Robust-CF62.256.447.7
BERT-PHAE Robust-P62.458.151.6
BERT-AH-58.3-
+ +Table 3: Evaluation of BERT, BERT-HAE, BERT-PHAE, BERT-AH and the robust variants with different validation protocols. + +Our experiment and results leave room for improvement with additional considerations on protocols/parameters/models. For instance, contrary to answers, standalone models can have access to + +the exact history of questions. What if we integrated the latter instead of the answer history in the model's input? We tested this by implementing a simple model that we refer to as BERT-AH (Appended History) in which previous questions are added to the regular BERT's inputs, and marked with a special embedding. BERT-AH obtains an F1-score of 58.3 (whatever the evaluation protocol, since answer history is not used). Thus, our guess is that the best direction for standalone CQA lies towards both the integration of previous questions and the robust integration of previous answers. + +# 5 Conclusion + +The work presented in this paper comes to complement the current training and evaluation protocols for CQA. It allows (1) highlighting unnoticed and undesirable behavior in existing approaches from the literature and (2) more robustness for their application in autonomous chatbots. We hope that this will encourage additional proposals in the same direction. Several improvements could be made in the future. First, because without Teacher Forcing the history is now predicted and not fixed, we could explore the impact of updating the model by backpropagating an answer's error through all previous turns and not only the current one. This would be analog to backpropagation through time. Second, we could augment the current CQA datasets or propose new ones to prevent the biases we observed: for example, QuAC could have conversations including wrong answers, since this occurs in real-life, so that models could be properly trained for. The associated turns would of course only be used as a part of histories. Finally, we should perform user tests to evaluate the robustness of models in real-life, because when a model's answer is wrong, we expect it to impact the next user's question(s). And this cannot be taken into account with the current protocol since the datasets are static. + +# References + +Naveed Akhtar and Ajmal Mian. 2018. Threat of adversarial attacks on deep learning in computer vision: A survey. IEEE Access, 6:14410-14430. +Samy Bengio, Oriol Vinyals, Navdeep Jaitly, and Noam Shazeer. 2015. Scheduled sampling for sequence prediction with recurrent neural networks. +Eunsol Choi, He He, Mohit Iyyer, Mark Yatskar, Wentau Yih, Yejin Choi, Percy Liang, and Luke Zettle-moyer. 2018. QuAC: Question answering in con + +text. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2174-2184, Brussels, Belgium. Association for Computational Linguistics. +W Bruce Croft, Donald Metzler, and Trevor Strohman. 2010. Search engines: Information retrieval in practice, volume 520. Addison-Wesley Reading. +Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171-4186, Minneapolis, Minnesota. Association for Computational Linguistics. +Peter A Flach. 2003. The geometry of roc space: understanding machine learning metrics through roc isometrics. In Proceedings of the 20th international conference on machine learning (ICML-03), pages 194-201. +Somil Gupta, Bhanu Pratap Singh Rawat, and Hong Yu. 2020. Conversational machine comprehension: a literature review. In Proceedings of the 28th International Conference on Computational Linguistics, pages 2739-2753, Barcelona, Spain (Online). International Committee on Computational Linguistics. +Hsin-Yuan Huang, Eunsol Choi, and Wen-tau Yih. 2018. Flowqa: Grasping flow in history for conversational machine comprehension. In International Conference on Learning Representations. +Armand Joulin, Edouard Grave, Piotr Bojanowski, and Tomas Mikolov. 2017. Bag of tricks for efficient text classification. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 427-431, Valencia, Spain. Association for Computational Linguistics. +Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, Kristina Toutanova, Llion Jones, Matthew Kelcey, Ming-Wei Chang, Andrew M. Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov. 2019. Natural questions: A benchmark for question answering research. Transactions of the Association for Computational Linguistics, 7:452-466. +Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2019. Albert: A lite bert for self-supervised learning of language representations. In International Conference on Learning Representations. +John Morris, Eli Lifland, Jin Yong Yoo, Jake Grigsby, Di Jin, and Yanjun Qi. 2020. TextAttack: A framework for adversarial attacks, data augmentation, and adversarial training in NLP. In Proceedings of the + +2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 119-126, Online. Association for Computational Linguistics. +Telmo Pires, Eva Schlinger, and Dan Garrette. 2019. How multilingual is multilingual BERT? In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4996-5001, Florence, Italy. Association for Computational Linguistics. +Chen Qu, Liu Yang, Minghui Qiu, W Bruce Croft, Yongfeng Zhang, and Mohit Iyyer. 2019a. Bert with history answer embedding for conversational question answering. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 1133-1136. +Chen Qu, Liu Yang, Minghui Qiu, Yongfeng Zhang, Cen Chen, W Bruce Croft, and Mohit Iyyer. 2019b. Attentive history selection for conversational question answering. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pages 1391-1400. +Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016. SQuAD: 100,000+ questions for machine comprehension of text. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 2383-2392, Austin, Texas. Association for Computational Linguistics. +Siva Reddy, Danqi Chen, and Christopher D. Manning. 2019. CoQA: A conversational question answering challenge. Transactions of the Association for Computational Linguistics, 7:249-266. +Kui Ren, Tianhang Zheng, Zhan Qin, and Xue Liu. 2020. Adversarial attacks and defenses in deep learning. Engineering, 6(3):346-360. +Victor Sanh, Lysandre Debut, Julien Chaumont, and Thomas Wolf. 2019. Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108. +Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi. 2016. Bidirectional attention flow for machine comprehension. arXiv preprint arXiv:1611.01603. +Wissam Siblini, Mohamed Challal, and Charlotte Pasqual. 2020. Delaying interaction layers in transformer-based encoders for efficient open domain question answering. arXiv preprint arXiv:2010.08422. +Wissam Siblini, Charlotte Pasqual, Axel Lavielle, Mohamed Challal, and Cyril Cauchois. 2019. Multilingual question answering from formatted text applied to conversational agents. arXiv preprint arXiv:1910.04659. + +Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in neural information processing systems, pages 5998-6008. +Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumont, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, et al. 2019. Huggingface's transformers: State-of-the-art natural language processing. ArXiv, pages arXiv-1910. +William A. Woods. 1977. Lunar rocks in natural english: Explorations in natural language question answering. Linguistic Structures Processing, pages 521-569. +Zichao Yang, Diyi Yang, Chris Dyer, Xiaodong He, Alex Smola, and Eduard Hovy. 2016. Hierarchical attention networks for document classification. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1480-1489, San Diego, California. Association for Computational Linguistics. +Xin Zhang, An Yang, Sujian Li, and Yizhong Wang. 2019. Machine reading comprehension: a literature review. arXiv preprint arXiv:1907.01686. +Chenguang Zhu, Michael Zeng, and Xuedong Huang. 2018. Sdnet: Contextualized attention-based deep network for conversational question answering. arXiv preprint arXiv:1812.03593. \ No newline at end of file diff --git a/towardsamorerobustevaluationforconversationalquestionanswering/images.zip b/towardsamorerobustevaluationforconversationalquestionanswering/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..a9ac69ea2cb5d5cca41efdeee0908c0e113929c7 --- /dev/null +++ b/towardsamorerobustevaluationforconversationalquestionanswering/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:898670c7e1d71dcc07e3fe0d752c4511490ff525d8a477637b6cce0b04343aea +size 62569 diff --git a/towardsamorerobustevaluationforconversationalquestionanswering/layout.json b/towardsamorerobustevaluationforconversationalquestionanswering/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..a32cfb640061e119feaea5f19c580db843559d67 --- /dev/null +++ b/towardsamorerobustevaluationforconversationalquestionanswering/layout.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2c728ddb4d61a0c7747729524299d5da0ddc44e0c8dc5574a0267b1f0153cf5c +size 168246 diff --git a/towardsmoreequitablequestionansweringsystemshowmuchmoredatadoyouneed/fdbd283a-547a-45f4-89d3-ec0b370a66b1_content_list.json b/towardsmoreequitablequestionansweringsystemshowmuchmoredatadoyouneed/fdbd283a-547a-45f4-89d3-ec0b370a66b1_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..b923114feaab4b2d9846d447f1766c807915230d --- /dev/null +++ b/towardsmoreequitablequestionansweringsystemshowmuchmoredatadoyouneed/fdbd283a-547a-45f4-89d3-ec0b370a66b1_content_list.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f7f35f17462d2d249c7975e9c40ae9ab822e8f972a0f6343464a10d5b50b5212 +size 57245 diff --git a/towardsmoreequitablequestionansweringsystemshowmuchmoredatadoyouneed/fdbd283a-547a-45f4-89d3-ec0b370a66b1_model.json b/towardsmoreequitablequestionansweringsystemshowmuchmoredatadoyouneed/fdbd283a-547a-45f4-89d3-ec0b370a66b1_model.json new file mode 100644 index 0000000000000000000000000000000000000000..0128470e7a7b472af626252a0d0448dc74792d50 --- /dev/null +++ b/towardsmoreequitablequestionansweringsystemshowmuchmoredatadoyouneed/fdbd283a-547a-45f4-89d3-ec0b370a66b1_model.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:db103f1b3ae7eda01b72faefa6655bc54ff7b0f6a8ceec5c065b82e96e43665a +size 67138 diff --git a/towardsmoreequitablequestionansweringsystemshowmuchmoredatadoyouneed/fdbd283a-547a-45f4-89d3-ec0b370a66b1_origin.pdf b/towardsmoreequitablequestionansweringsystemshowmuchmoredatadoyouneed/fdbd283a-547a-45f4-89d3-ec0b370a66b1_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..35bcffe05dc8d81056fd46330e8c3066c6d83d08 --- /dev/null +++ b/towardsmoreequitablequestionansweringsystemshowmuchmoredatadoyouneed/fdbd283a-547a-45f4-89d3-ec0b370a66b1_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8e21bd10b7a10d2a6cfca49cc01a15b5ddd5585f9a024ab5cefdd95c57a22694 +size 248438 diff --git a/towardsmoreequitablequestionansweringsystemshowmuchmoredatadoyouneed/full.md b/towardsmoreequitablequestionansweringsystemshowmuchmoredatadoyouneed/full.md new file mode 100644 index 0000000000000000000000000000000000000000..b45e9435b22e3d2ccc3b1e67a6cc9c272a691769 --- /dev/null +++ b/towardsmoreequitablequestionansweringsystemshowmuchmoredatadoyouneed/full.md @@ -0,0 +1,163 @@ +# Towards More Equitable Question Answering Systems: How Much More Data Do You Need? + +Arnab Debnath*, Navid Rajabi*, Fardina Fathmiul Alam*, Antonios Anastasopoulos + +Department of Computer Science, George Mason University + +{adebnath,nrajabi,falam5,antonis}@gmu.edu + +# Abstract + +Question answering (QA) in English has been widely explored, but multilingual datasets are relatively new, with several methods attempting to bridge the gap between high- and low-resourced languages using data augmentation through translation and cross-lingual transfer. In this project, we take a step back and study which approaches allow us to take the most advantage of existing resources in order to produce QA systems in many languages. Specifically, we perform extensive analysis to measure the efficacy of few-shot approaches augmented with automatic translations and permutations of context-question-answer pairs. In addition, we make suggestions for future dataset development efforts that make better use of a fixed annotation budget, with a goal of increasing the language coverage of QA datasets and systems. + +# 1 Introduction + +Automatic question answering (QA) systems are showing increasing promise that they can fulfil the information needs of everyday users, via information seeking interactions with virtual assistants. The research community, having realized the obvious needs and potential positive impact, has produced several datasets on information seeking QA. The effort initially focused solely on English, with datasets like WikiQA (Yang et al., 2015), MS MARCO (Nguyen et al., 2016), SQuAD (Rajpurkar et al., 2016), QuAC (Choi et al., 2018), CoQA (Reddy et al., 2019), and Natural Questions (NQ) (Kwiatkowski et al., 2019), among others. More recently, heading calls for linguistic and typological diversity in natural language processing + +research (Joshi et al., 2020), larger efforts have produced datasets in multiple languages, such as TyDi QA (Clark et al., 2020), XQuAD (Artetxe et al., 2020), or MLQA (Lewis et al., 2020). + +Despite these efforts, the linguistic and typological coverage of question answering datasets is far behind the world's diversity. For example, while TyDi QA includes 11 languages –less than $0.2\%$ of the world's approximately 6,500 languages (Hammarström, 2015) – from 9 language families, its typological diversity is 0.41, evaluated in a [0,1] range with the measure defined by Ponti et al. (2020); MLQA provides data in 7 languages from 4 families, for a typological diversity of 0.32. The total population coverage of TyDi QA, based on population estimates from Glottolog (Nordhoff and Hammarström, 2012), is less than $20\%$ of the world's population (the TyDiQA languages total around 1.45 billion speakers). + +Obviously, the ideal solution to this issue would be to collect enough data in every language. Unfortunately, this ideal seems unattainable at the moment. In this work, we perform extensive analysis to investigate the next-best solution: using the existing resources, large multilingual pre-trained models, data augmentation, and cross-lingual learning to improve performance with just a few or no training examples. Specifically: + +- we study how much worse a multilingual few-shot training setting would perform compared to training on large training datasets, +- we show how data augmentation through translation can reduce the performance gap for few-shot setting, and +- we study the effect of different fixed-budget allocation for training data creation across languages, making suggestions for future dataset creators. + +# 2 Problem Description and Settings + +We focus on the task of simplified minimal answer span selection over a gold passage: The inputs to the model include the full text of an article (the passage or context) and the text of a question (query). The goal is to return the start and end byte indices of the minimal span that completely answers the question. + +Our models follow the current state-of-the-art in extractive question answering, relying on large multilingually pre-trained language models (in our case, multilingual BERT (Devlin et al., 2019)) and the task-tuning strategy of Alberti et al. (2019), which outperforms approaches like DocumentQA (Clark and Gardner, 2018) or decomposable attention (Parikh et al., 2016). In all cases, we treat the official TyDi QA development set as our test set, since the official test set is not public. We provide concrete details (model cards, hyperparameters, etc) on our model and training/finetuning regime in Appendix A. + +To simulate the scenario of data-scarce adaptation of such a model to unseen languages, we will treat the TyDi QA languages as our test, unseen ones. We will assume that we have access to (a) other QA datasets in more resource-rich languages (in particular, the SQuAD dataset which provides training data in English), and (b) translation models between the languages of existing datasets (again, English) and our target "unseen" languages. + +In the experiments sections, we first focus on few- and zero-shot experiments (§3) and then study the effects of language selection and budget-restricted decisions on training data creation (§4). + +Evaluation We report F1 score on the test set of each language, as well as a macro-average excluding English $(\mathbf{avg}_{\mathcal{L}})$ . In addition, to measure the expected impact on actual systems' users, we follow Faisal et al. (2021) in computing a population-weighted macro-average $(\mathbf{avg}_{\mathbf{pop}})$ based on language community populations provided by Ethnologue (Eberhard et al., 2019). + +# 3 Is Few-Shot a Viable Solution? + +We first set out to explore the effect of the amount of available data on downstream performance. Starting with baselines relying solely on English-only SQuAD, we implement a few-shot setting for + +fine-tuning on the target languages of TyDi QA.3 To our knowledge, this is the first study of its type on the TyDi QA benchmark. + +The straightforward baseline simply provides zero-shot results on TyDi QA after training only on English. Table 1 provides our (improved) reproduction of the baseline experiments of Clark et al. (2020). The skyline results (bottom of Table 1) reflect the presumably best possible results under our current modeling approach, which trains jointly on all languages using all available TyDi QA training data. We note that for most languages the gap between the baseline and the skyline is more than 20 percentage points, with the exception of English where -unsurprisingly- there is a difference of only 3.3 percentage points. The performance gap is smallest for Russian (rus) at 10.9 percentage points, and largest for Telugu (tel) at 34 points. + +We first study a monolingual few-shot setting. That is, we fine-tune the model trained on the English SQuAD dataset, with only a small amount of data (10, 20, or 50 training instances) in the test language. Due to space limitations, we only present results with 50 examples per language in Table 1, but the full experiments are available in Appendix C. We observe that even just 50 additional training instances are enough for significant improvements, which are consistent across all languages. For example, the improvement in Finnish (fin) exceeds 15 percentage points and covers about more than $60\%$ of the performance gap between the baseline and the skyline. + +We now turn to a multilingual few-shot setting. Exactly as before, we assume a scenario where we only have access to a small amount of data in each language, but now we fine-tune using that small amount of data in all languages. For example, 10 training instances in each language result in training with 90 training examples over the 9 test languages. A sample of our experimental results are presented in Table 1 under "multilingual few-shot," with complete results in Appendix C. + +Simply adding 50 instances from each language we obtain an F1 score of 67.9 over the zero-shot baseline, an improvement of almost 7 percentage points which reduces the zero-full gap by $43.4\%$ . + +
ModelResults (F1-score)avgLavgpop
engarabenfinindswarustel(without eng)
Baseline: SQuAD zero-shot (reproduction)74.259.057.355.763.260.365.644.658.0±6.359.3
Monolingual Few-Shot (+50)73.964.966.470.973.370.166.362.567.8±3.567.1
Multilingual Few-Shot (+10/lang, 90 total)73.764.662.966.567.063.165.959.664.2±2.464.4
(+50/lang, 450 total)73.469.265.869.073.468.867.266.268.5±2.468.6
(+100/lang, 900 total)74.272.570.971.975.572.369.369.371.7±2.071.9
(+500/lang, 4500 total)76.176.374.578.281.479.273.373.776.7±2.876.2
Data Augmentation + Multilingual Few-Shot
+tSQuAD74.965.458.466.765.269.460.244.761.4±7.761.2
+mSQuAD75.165.668.671.770.366.275.549.466.7±7.767.6
+mSQuAD +500/lang77.678.775.078.583.582.573.275.378.1±3.677.6
+tSQuAD +500/lang77.978.880.079.582.883.672.573.578.7±3.978.6
Skyline: Full training on TyDi QA train (reproduction)77.582.478.980.185.483.876.578.380.8±3.080.9
+ +Table 1: Data augmentation combined with multilingual few-shot learning can reach about $98\%$ of the skyline accuracy using only 10 times less training data on the test languages beyond English. + +We note that the total 450 training instances represent less than $1\%$ of the full TyDi QA training set! Doubling that amount of data to 100 examples per language further increases downstream performance to an average overall F1 score of 71.7. Going further to the point of adding 500 training instances per language (for a total of 4500 examples) leads to even larger improvements for an average F1 score of 76.7. That is, using less than $10\%$ of the available training data we can reduce the average F1 score performance gap by more than $82\%$ . For a few languages the gap reduction is even more notable, e.g., more than $92\%$ for Finnish. + +Data Augmentation through Translation Generating translations of English dataset to train systems in other languages has a long history and has been successful in the QA context as well (Yarowsky et al., 2001; Xue et al., 2020, inter alia). We follow the same approach, translating all SQuAD paragraphs, questions, and answers to all TyDi QA languages using Google Translate. For each language, we keep between $20 - 50\%$ of the question-answer pairs where the translated answer has an exact match in the translated paragraph, + +which becomes the target span. $^{5}$ Details of the resulting dataset (which we refer to as tSQuAD) are in Table 3 in Appendix B. A second approach translates the question of a training instance into one language, but keeps the answer and context into the original language. The result is a modified training set (which we name mSQuAD) that requires better cross-lingual modeling, as the question and contexts are in different languages. + +Both approaches improve over the zero-shot baseline with F1 score of 61.4 (+3) and 66.7 (+8). Notably, though, they are not as effective as few-shot training even with just 50 instances per languages. This further strengthens the discussion of Clark et al. (2020) on the qualitative differences between the SQuAD and TyDi QA dataset. Nevertheless, combining tSQuAD (or mSQuAD) with a few examples from the TyDi QA dataset leads to our best-performing methods. In particular, augmentation through translation leads to an 1-2 percentage point improvements over the multilingual few-shot approach (cf. 76.7 to 78.1/78.7 F1 score in Table 1; full results in Appendix C). Now, using only 500 new training examples per language we are almost (98%) at similar performance levels as the skyline. + +
Results (F1-score)Overall (w/o eng)Δl(max-min)avg seen unseen
engarabenfinindswarustel
Baseline: no budget for additional data (zero-shot except for eng)
74.259.057.355.763.260.365.644.658.0±6.329.674.2 58.0
Monolingual budget allocation (max 4500 per language; 7 experiments)
76.0±1.874.0±3.969.1±5.075.8±2.778.4±4.171.7±4.175.7±6.361.3±12.372.3±5.317.177.1 71.3
Tri-lingual budget allocation (1500 per language; 7 random language selection experiments)
76.7±1.277.2±2.868.6±4.877.9±1.680.9±3.381.5±3.372.7±2.362.9±13.374.5±6.318.678.9 68.5
Uniform budget allocation (500 per language)
77.978.880.079.582.883.672.573.578.7±3.911.178.6 -
Ideal Few-Shot (4500 in each language; in-language results)
78.481.877.779.783.984.075.778.279.9±3.08.379.9 -
+ +Table 2: A more egalitarian budget allocation leads to better and more equitable performance across languages (avg±std: higher average, lower std. deviation) reducing the gap $(\Delta_l)$ between best and worst performing languages. + +# 4 How to Spend the Annotation Budget? + +In the previous section we show that the combination of data augmentation techniques with a few new annotations can reach almost $98\%$ of the performance one would obtain by training on 10x more data. In this section we explore how one should allocate a fixed annotation budget, in order to achieve not only higher average but also more equitable performance across languages. + +Keeping our budget fixed to 4500 instances, we study 3 scenarios. The first is monolingual allocation, where the whole budget is consumed by collecting training examples on a single language. We repeat the study over all 8 languages of our test set, randomly sampling training instances from the TyDi QA training set. Second, we study a tri-lingual budget allocation scheme, where we equally split the budget across 3 languages for 1500 training instances per language. We repeat this experiment 7 times, each time randomly selecting 3 languages. Last, the third and more egalitarian scenario splits the budget equally across all 8 languages, matching our previously analyzed few-shot scenario where we only have 500 additional training examples per language. In all experiments, we use our best-performing approach from the previous section, also utilizing tSQuAD for pre-training. + +Our findings are summarized in Table 2. For the repeated monolingual and tri-lingual scenarios we report average performance across our experiment repetitions (full results in Appendix E). We can conclusively claim that a uniform budget allocation leads to not only better average performance, but also to more equitable performance. We report two straightforward measures for the equitability of the average accuracy across languages. First, + +we report the standard deviation of the accuracy across languages; the lower the standard deviation, the more equitable the performance. We also report the difference between the best and the worst performing language for each experiment, as well as the averages for the languages that are seen and unseen during fine-tuning. + +Having no budget for additional annotation (essentially, attempting the task in zero-shot fashion) leads to the most inequitable performance. The monolingual scenario typically leads to the highest accuracy when evaluating on the same language as the new training examples (the ideal section of Table 2) but the zero-shot performance on all other languages is generally significantly worse, leading to inequity. The tri-lingual scenarios follow similar patterns, with performance close to state-of-the-art for the four languages (three plus English) that have been included in the fine-tuning process, but with the rest of the languages lagging behind: the difference between seen and unseen languages is on average 10.4 points. In our experiments we randomly sampled (without replacement) three of the seven languages, but one could potentially use heuristics or a meta-model like that of Xia et al. (2020) to find or suggest the best subset of candidate languages for transfer learning; we leave such an investigation for future work. + +Encouragingly, the uniform budget allocation scenario leads to higher average performance, while also reducing the gap between worst and best performing languages from around 30 percentage points to less than 12 points (60% reduction). Note that a 8x larger budget (ideal scenario) with 4500 instances per language would further improve downstream accuracy and equitability. Note that in this case where some resources are available, + +simple multilingual fine-tuning might not be the best approach for some languages, e.g. compared to monolingual fine-tuning or meta-learning approaches (Wang et al., 2020; Muller et al., 2021, inter alia). We leave an investigation of such settings for future work. + +# 5 Discussion + +We show that data augmentation through translation along with few-shot fine-tuning on new languages with a uniform budget allocation leads to a performance close to $98\%$ of an approach using 10x more data, while producing more equitable models than other budget-constrained alternatives. + +The implications of our findings become clear with a counter-factual exploration. The Gold Passage portion of the TyDi QA dataset includes around 87,000 annotated examples (50k for training across 9 languages and about 37k development and test samples). Consider the scenario where, given this annotation budget, we maintain the same evaluation standards collecting 4k development and test examples per language, but we only collect 500 training examples per language. In that case, we could have created a much more diverse resource that would include at least 19 languages! Now consider the expectation of the downstream accuracy in our counterfactual scenario: uniform budget allocation on 19 languages would lead to an average accuracy (F1 score) of around $78\%$ (similar to our experiments). Instead, under the (currently factual) scenario where we only have training data for 9 languages, the average accuracy for these 9 languages is around $80\%$ , but the zero-shot expected average on the other 10 languages is 10 points worse – in that case, the overall average accuracy would be around $74\%$ , 4 points lower than that of the egalitarian allocation scenario. Hence, as long as the ideal scenario of collecting a lot of data for a lot of languages remains infeasible, we suggest that the community puts an additional focus on the linguistic diversity of our evaluation sets and use other techniques to address the lack of training data. + +# Acknowledgements + +This work is supported by NSF Award 2040926. The authors also want to thank Fahim Faisal for helpful discussions on setting up the experiments. Most experiments were run on ARGO, $^{6}$ a research + +computing cluster provided by the Office of Research Computing at George Mason University, VA, and a few experiments were run on Amazon Web Services instances donated through the AWS Educate program. + +# References + +Chris Alberti, Kenton Lee, and Michael Collins. 2019. A BERT baseline for the natural questions. arXiv:1901.08634. +Mikel Artetxe, Sebastian Ruder, and Dani Yogatama. 2020. On the Cross-lingual Transferability of Monolingual Representations. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4623–4637, Online. Association for Computational Linguistics. +Eunsol Choi, He He, Mohit Iyyer, Mark Yatskar, Wentau Yih, Yejin Choi, Percy Liang, and Luke Zettle-moyer. 2018. QuAC: Question Answering in Context. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2174-2184. Association for Computational Linguistics. +Christopher Clark and Matt Gardner. 2018. Simple and Effective Multi-Paragraph Reading Comprehension. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 845–855. Association for Computational Linguistics. +Jonathan H. Clark, Eunsol Choi, Michael Collins, Dan Garrette, Tom Kwiatkowski, Vitaly Nikolaev, and Jennimaria Palomaki. 2020. TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages. Transactions of the Association for Computational Linguistics, 8:454-470. +Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettle-moyer, and Veselin Stoyanov. 2020. Unsupervised Cross-lingual Representation Learning at Scale. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 8440-8451. Association for Computational Linguistics. +Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Vol 1, pages 4171-4186. Association for Computational Linguistics. +David M Eberhard, Gary F Simons, and Charles D. (eds.) Fennig. 2019. Ethnologue: Languages of the world. 2019. online. Dallas, Texas: SIL International. + +Fahim Faisal, Sharlina Keshava, Md Mahfuz ibn Alam, and Antonios Anastasopoulos. 2021. SD-QA: Spoken Dialectal Question Answering for the Real World. Preprint. +Harald Hammarström. 2015. "ethnologue" 16/17/18th editions: A comprehensive review. Language, pages 723-737. +Hugging Face - mBERT. 2020. Hugging Face - bert-base-multilingual-cased. [Online; accessed 01-Novemberr-2020]. +Pratik Joshi, Sebastin Santy, Amar Budhiraja, Kalika Bali, and Monojit Choudhury. 2020. The State and Fate of Linguistic Diversity and Inclusion in the NLP World. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6282-6293. Association for Computational Linguistics. +Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, et al. 2019. Natural Questions: A Benchmark for Question Answering Research. Transactions of the Association for Computational Linguistics, 7. +Patrick Lewis, Barlas Oguz, Rudy Rinott, Sebastian Riedel, and Holger Schwenk. 2020. MLQA: Evaluating Cross-lingual Extractive Question Answering. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7315-7330, Online. Association for Computational Linguistics. +Benjamin Muller, Antonios Anastasopoulos, Benoit Sagot, and Djame Seddah. 2021. When being unseen from mBERT is just the beginning: Handling new languages with multilingual language models. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 448-462, Online. Association for Computational Linguistics. +Tri Nguyen, Mir Rosenberg, Xia Song, Jianfeng Gao, Saurabh Tiwary, Rangan Majumder, and Li Deng. 2016. MS MARCO: A human generated machine reading comprehension dataset. In CoCo@ NIPS. +Sebastian Nordhoff and Harald Hammarström. 2012. Glottolog/Langdoc: Increasing the visibility of grey literature for low-density languages. In Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12), pages 3289-3294. European Language Resources Association (ELRA). +Ankur Parikh, Oscar Tackström, Dipanjan Das, and Jakob Uszkoreit. 2016. A Decomposable Attention Model for Natural Language Inference. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 2249-2255. Association for Computational Linguistics. + +Edoardo Maria Ponti, Goran Glavaš, Olga Majewska, Qianchu Liu, Ivan Vulić, and Anna Korhonen. 2020. XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 2362-2376. Association for Computational Linguistics. +Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016. SQuAD: 100,000+ Questions for Machine Comprehension of Text. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 2383-2392. Association for Computational Linguistics. +Siva Reddy, Danqi Chen, and Christopher D Manning. 2019. CoQA: A Conversational Question Answering Challenge. Transactions of the Association for Computational Linguistics, 7:249-266. +Zirui Wang, Zachary C. Lipton, and Yulia Tsvetkov. 2020. On negative interference in multilingual models: Findings and a meta-learning treatment. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 4438-4450, Online. Association for Computational Linguistics. +Thomas Wolf, Julien Chaumont, Lysandre Debut, Victor Sanh, Clement Delangue, Anthony Moi, Pierric Cistac, Morgan Funtowicz, Joe Davison, Sam Shleifer, et al. 2020. Transformers: State-of-the-Art Natural Language Processing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 38-45. Association for Computational Linguistics. +Mengzhou Xia, Antonios Anastasopoulos, Ruochen Xu, Yiming Yang, and Graham Neubig. 2020. Predicting performance for natural language processing tasks. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 8625-8646, Online. Association for Computational Linguistics. +Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, and Colin Raffel. 2020. mT5: A massively multilingual pre-trained text-to-text transformer. arXiv:2010.11934. +Yi Yang, Wen-tau Yih, and Christopher Meek. 2015. WikiQA: A Challenge Dataset for Open-Domain Question Answering. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 2013-2018. Association for Computational Linguistics. +David Yarowsky, Grace Ngai, and Richard Wicentowski. 2001. Inducing multilingual text analysis tools via robust projection across aligned corpora. In Proceedings of the First International Conference on Human Language Technology Research. + +# A Experimental Settings + +For the experiments, we've used "bert-multi-lingual-base-uncased" (mBERT) (Hugging Face - mBERT, 2020) as mentioned as the main baseline on TyDi QA paper (Clark et al., 2020). It is a pre-trained model on the top 102 languages with the largest Wikipedia using a masked language modeling (MLM) objective (Devlin et al., 2019). From preliminary experiments, we realized that the optimum trade-off between the highest F1 score and the least computational cost is achieved by training for 3 epochs, using batch size of 24, and learning rate of 3e-5. Therefore, we applied these hyperparameter settings for our experiments. The main script we used was a module under the Huggingface library (Wolf et al., 2020) (called run_squad), which is being used widely for fine-tuning transformers for multi-lingual question answering datasets. + +# B SQuAD Translation Details + +We augmented the English SQuAD with translated SQuAD (tSQuAD) instances for each language. Here, the contexts, questions and answers from SQuAD instances are translated to the target languages using Google Translate (with the google-trans-new API) and only the instances where an exact match of translated answer is found in the translated context, are kept for augmentation. The total number of instances per language, we ended up with after translation is listed in Table 3. + +# C Complete Few-Shot Experiments + +Provided in Table 4. + +# D Mix-and-Match Experiments + +Provided in Table 5. + +# E Budget Allocation Experiments + +The complete results for our experiments are presented in Table 6. + +
SQuADtArtBntFintIndtKotRustSwatTel
no of paragraphs18.916.613.512.416.211.211.615.316.6
no of QAs87.639.124.121.436.118.119.231.239.7
+ +Table 3: Number (in 1000s) of paragraphs and QA pairs present in the original SQuAD and translated SQuAD + +
ModelResults (F1-score)Overall (without eng)
engarabenfinindswarustel
Baseline: SQuAD zero-shot (Clark et al., 2020) (ours)73.460.357.356.260.852.964.449.357.3±4.7
74.259.057.355.763.260.365.644.658.0±6.3
Monolingual Few-Shot (+10)73.764.762.868.269.359.965.650.763.0±5.8
Monolingual Few-Shot (+20)74.763.560.566.672.163.966.863.065.2±3.4
Monolingual Few-Shot (+50)73.964.966.470.973.370.166.362.567.8±3.5
Multilingual Few-Shot (+10/lang, 90 total)73.764.662.966.567.063.165.959.664.2±2.4
(+20/lang, 180 total)73.965.966.869.072.564.266.963.767.0±2.8
(+50/lang, 450 total)73.469.265.869.073.468.867.266.268.5±2.4
(+100/lang, 900 total)74.272.570.971.975.572.369.369.371.7±2.0
(+200/lang, 1800 total)73.974.870.574.177.776.469.870.073.3±3.0
(+500/lang, 4500 total)76.176.374.578.281.479.273.373.776.7±2.8
Data Augmentation + Multilingual Few-Shot
+tSQuAD(50/lang)73.864.062.468.469.759.766.848.162.7±6.8
+tSQuAD(100/lang)72.462.266.668.468.664.967.147.563.6±6.9
+tSQuAD(200/lang)74.462.764.268.870.766.166.248.363.9±6.8
+tSQuAD(500/lang)73.763.269.567.970.969.866.749.165.3±7.0
+tSQuAD(all)74.965.458.466.765.269.460.244.761.4±7.7
+mSQuAD +500/lang77.678.775.078.583.582.573.275.378.1±3.6
+tSQuAD +500/lang (mBERT)77.978.880.079.582.883.672.573.578.7±3.9
+tSQuAD +500/lang (XLM-R)*73.272.878.378.584.780.375.078.178.2±3.5
Skyline: Full training on TyDi QA train (Clark et al., 2020) (ours)76.881.775.479.484.881.976.283.380.4±3.3
77.582.478.980.185.483.876.578.380.8±3.0
+ +Table 4: Complete few-shot and data augmentation results. *: Results with XLM-Roberta-Large (Conneau et al., 2020) are generally worse than using mBERT so all other experiments use mBERT. + +
Change language of Question onlyChange all; Context & answers the same
Modified SquadSquad + Modified SquadSquad + Modified Squad + 500 instancesModified SquadSquad + Modified SquadSquad + Modified Squad + 500 instances
English66.5975.0677.5665.4073.4978.21
Arabic62.1765.6278.7060.5165.9877.96
Bengali67.3368.5575.0058.6062.4476.16
Finnish67.4271.6778.5562.9867.5879.51
Indonesian66.4570.3383.4661.8966.4484.10
Kiswahili70.3275.4882.5162.6668.5580.01
Russian64.7166.1673.1661.0165.6473.28
Telugu48.3249.3675.2843.6251.8174.95
Avg63.8266.7478.0958.7664.0778.00
SD6.747.753.606.335.313.35
+ +Table 5: Mix-and-Match scheme detailed results. + +
Results (F1-score)Overall (w/o eng)Avg seen unseen
engarabenfinindswarustel
Baseline: no budget for additional data (zero-shot excelt in eng)
74.259.057.355.763.260.365.644.660.0±8.574.2 58.0
Monolingual budget allocation (max 4500 per language; 7 experiments)
Arabic78.481.862.077.679.272.868.050.570.2±10.380.1 68.4
Bengali74.466.377.771.672.878.166.552.069.3±8.376.1 67.9
Finnish77.975.572.679.781.070.678.552.272.9±9.178.8 71.7
Indonesian76.876.767.477.083.970.277.352.272.1±9.580.4 70.1
Kiswahili76.472.567.175.077.466.484.075.073.9±5.671.4 75.2
Russian75.274.566.776.381.075.778.869.474.6±4.777.0 73.9
Telugu73.470.670.273.673.768.177.178.273.1±3.475.8 72.2
76.0±1.874.0±3.969.1±5.075.8±2.778.4±4.171.7±4.175.7±6.361.3±12.372.3±5.377.1 71.3
Tri-lingual budget allocation (1500 per language; 7 random language selection experiments)
ben-rus-tel75.872.279.075.674.877.174.576.875.7±2.076.5 74.9
tel-ind-swa76.175.765.576.783.284.771.277.276.3±6.180.3 72.3
fin-rus-swa78.576.466.379.680.384.874.953.473.7±9.879.5 69.1
ara-rus-tel75.779.366.878.079.279.974.377.076.4±4.376.6 60.8
ara-rus-fin76.580.568.979.280.677.574.353.673.5±9.077.6 70.2
swa-ind-fin76.177.268.579.784.283.071.251.573.6±10.580.8 67.1
ara-ind-swa78.379.565.476.883.983.568.950.672.7±11.181.3 65.4
76.7±1.277.2±2.868.6±4.877.9±1.680.9±3.381.5±3.372.7±2.362.9±13.374.5±6.378.9 68.5
Uniform budget allocation (500 per language)
77.978.880.079.582.883.672.573.578.7±3.978.6 -
+ +Table 6: Complete budget allocation experiments. \ No newline at end of file diff --git a/towardsmoreequitablequestionansweringsystemshowmuchmoredatadoyouneed/images.zip b/towardsmoreequitablequestionansweringsystemshowmuchmoredatadoyouneed/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..f2c564b472b829c10c0e2690f0392b178ed82b4d --- /dev/null +++ b/towardsmoreequitablequestionansweringsystemshowmuchmoredatadoyouneed/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:217cf3944bb865840d187bbc775c3b095dff3b010d5d331a6fdf81e776df838b +size 686646 diff --git a/towardsmoreequitablequestionansweringsystemshowmuchmoredatadoyouneed/layout.json b/towardsmoreequitablequestionansweringsystemshowmuchmoredatadoyouneed/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..1cccc77490d98e32c8b244494367fbcc5ba26bcf --- /dev/null +++ b/towardsmoreequitablequestionansweringsystemshowmuchmoredatadoyouneed/layout.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1b2b41d43397bd739a81b6d810787962e75eecdf9609ecb580ac8fef10659f18 +size 202197 diff --git a/trainingadaptivecomputationforopendomainquestionansweringwithcomputationalconstraints/86e588e3-e6f8-4b92-9621-6b38420d10eb_content_list.json b/trainingadaptivecomputationforopendomainquestionansweringwithcomputationalconstraints/86e588e3-e6f8-4b92-9621-6b38420d10eb_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..f2a97a84f21492a324f8312fd7a870a8267b1c9d --- /dev/null +++ b/trainingadaptivecomputationforopendomainquestionansweringwithcomputationalconstraints/86e588e3-e6f8-4b92-9621-6b38420d10eb_content_list.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:69b5d3f8ff380b577fb8345c7bd6fca1c008b878897d388d8c61f07f17cdc663 +size 38844 diff --git a/trainingadaptivecomputationforopendomainquestionansweringwithcomputationalconstraints/86e588e3-e6f8-4b92-9621-6b38420d10eb_model.json b/trainingadaptivecomputationforopendomainquestionansweringwithcomputationalconstraints/86e588e3-e6f8-4b92-9621-6b38420d10eb_model.json new file mode 100644 index 0000000000000000000000000000000000000000..320e34d3c3c27b436d34f2848063176ef27b2421 --- /dev/null +++ b/trainingadaptivecomputationforopendomainquestionansweringwithcomputationalconstraints/86e588e3-e6f8-4b92-9621-6b38420d10eb_model.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7e746e6defac6e5534f537c59ffaff0cdb5fc59d776143e8562faefe9e2d459e +size 47504 diff --git a/trainingadaptivecomputationforopendomainquestionansweringwithcomputationalconstraints/86e588e3-e6f8-4b92-9621-6b38420d10eb_origin.pdf b/trainingadaptivecomputationforopendomainquestionansweringwithcomputationalconstraints/86e588e3-e6f8-4b92-9621-6b38420d10eb_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..defabd2cf953456c7a1f66537685d2a229678cf4 --- /dev/null +++ b/trainingadaptivecomputationforopendomainquestionansweringwithcomputationalconstraints/86e588e3-e6f8-4b92-9621-6b38420d10eb_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:783d0df096330f65f79cd0602987cd28cbaecd4bc2a38a8dbfef00a7dbdc546c +size 282999 diff --git a/trainingadaptivecomputationforopendomainquestionansweringwithcomputationalconstraints/full.md b/trainingadaptivecomputationforopendomainquestionansweringwithcomputationalconstraints/full.md new file mode 100644 index 0000000000000000000000000000000000000000..908bdacaa126f307194c6bb927a83f91be8b298e --- /dev/null +++ b/trainingadaptivecomputationforopendomainquestionansweringwithcomputationalconstraints/full.md @@ -0,0 +1,157 @@ +# Training Adaptive Computation for Open-Domain Question Answering with Computational Constraints + +Yuxiang Wu Pasquale Minervini Pontus Stenetorp Sebastian Riedel University College London + +{yuxiang.wu,p.minervini,p.stenetorp,s.riedel}@cs.ucl.ac.uk + +# Abstract + +Adaptive Computation (AC) has been shown to be effective in improving the efficiency of Open-Domain Question Answering (ODQA) systems. However, current AC approaches require tuning of all model parameters, and training state-of-the-art ODQA models requires significant computational resources that may not be available for most researchers. We propose Adaptive Passage Encoder, an AC method that can be applied to an existing ODQA model and can be trained efficiently on a single GPU. It keeps the parameters of the base ODQA model fixed, but it overrides the default layer-by-layer computation of the encoder with an AC policy that is trained to optimise the computational efficiency of the model. Our experimental results show that our method improves upon a state-of-the-art model on two datasets, and is also more accurate than previous AC methods due to the stronger base ODQA model. All source code and datasets are available at https://github.com/uclnlp/APE. + +# 1 Introduction + +Open-Domain Question Answering (ODQA) requires finding relevant information for a given question and aggregating the information to produce an answer. The retriever-reader architecture, popularised by Chen et al. (2017), has shown great success in this task. The retriever acquires a set of documents from external sources (e.g., Wikipedia) and the reader extracts the answer spans from these documents (Clark and Gardner, 2018; Yang et al., 2019; Wang et al., 2019; Min et al., 2019; Asai et al., 2020). Recently, Min et al. (2020); Lewis et al. (2020b); Izacard and Grave (2020b) showed that generative reader models that exploit an encoder-decoder architecture can significantly outperform previous extractive models, thanks to + +![](images/9b8462ea4c1e9c237cdb2ee33db094d040a55398476f4a3e39df2eafe05cd27c.jpg) +Figure 1: Overview of our approach. The adaptive passage encoder overrides the layer-by-layer computation of the encoder with an adaptive computation policy (indicated in blue dash arrows). + +their better capability in aggregating and combining evidence from multiple passages. However, these generative models are much more computationally expensive than extractive models, and often need to be trained with a large number of passages, making it hard to train these models for most researchers (Schwartz et al., 2020a). + +Wu et al. (2020) show that Adaptive Computation (AC) can significantly improve the efficiency of extractive ODQA models at inference time. However, it requires fine-tuning all model parameters with a multitask learning objective, making it computationally challenging to apply this method to current state-of-the-art models. + +In this work, we explore an efficient approach to apply adaptive computation to large generative ODQA models. We introduce the Adaptive Passage Encoder (APE), a module that can be added to the encoder of an existing ODQA model, which has the following features: $I$ it efficiently reuses the encoder's hidden representations for calculating + +the AC priorities; 2) it does not require tuning of the base model and hence allows efficient training under limited resource; 3) it does not require confidence calibration. Our experimental results on NaturalQuestions and TriviaQA show that our method improves the performance of the state-of-the-art model FiD (Izacard and Grave, 2020b), while also producing more accurate results (12.4% EM) than the AC method proposed by Wu et al. (2020). + +# 2 Related Work + +Open Domain Question Answering ODQA is a task that aims to answer a factoid question given a document corpus. Most works in this domain follow a retriever-reader design first proposed by Chen et al. (2017). The retriever collects a set of relevant passages, then the reader comprehends and aggregates the information from multiple passages to produce the answer. Depending on the design of the reader model, these systems could be further categorised into extractive models and generative models. Extractive models (Min et al., 2019; Yang et al., 2019; Wang et al., 2019; Asai et al., 2020; Karpukhin et al., 2020) exploit an answer extraction model to predict the probabilities of answer spans, and use global normalisation (Clark and Gardner, 2018) to aggregate the answer probabilities across multiple passages. + +However, thanks to recent advances in sequence-to-sequence pretrained language models (Raffel et al., 2020; Lewis et al., 2020a), generative ODQA models (Min et al., 2020; Lewis et al., 2020b; Izacard and Grave, 2020b) achieve significant improvement upon extractive models, demonstrating stronger capability in combining evidence from multiple passages. We focus on generative models in this work. + +Passage Retrieval and Re-Ranking Passage retrievers in ODQA systems are initially based on sparse vector representations. Chen et al. (2017) use TF-IDF, whereas Yang et al. (2019); Karpukhin et al. (2020); Wang et al. (2019) rely on BM25 for ranking passages (Robertson, 2004). Recently, Karpukhin et al. (2020); Lewis et al. (2020b); Izacard and Grave (2020a) achieved substantial increase in retrieval performance using dense representations. Our work is based on the retrieval results from a dense retriever (Izacard and Grave, 2020b), but we show that the proposed method can still improve the quality of the support passages despite the strong retrieval performance. + +Nogueira and Cho (2019); Qiao et al. (2019); Mao et al. (2021) show that adding a separate cross-encoder re-ranker can improve the performance, but that comes with a significant increase of the computation at train or inference time. Despite that our proposed adaptive passage encoder can be viewed as an encoder with an integrated re-ranker, the focus of our work is to improve the computational efficiency, namely, enhancing the performance without a substantial increase in computation. + +Adaptive Computation Adaptive computation allows the model to condition the computation cost on the input. For example, Schwartz et al. (2020b); Liu et al. (2020); Xin et al. (2020) propose models that can dynamically decide to early exit at intermediate layers when the confidence at the layer exceeds a threshold. They show that adaptively early exiting can significantly reduce the computational cost for various sequence classification tasks. Closest to our work, Wu et al. (2020) introduced adaptive computation for extractive ODQA models. We extend adaptive computation to generative ODQA models, and our approach can be incorporated in existing generative ODQA models without finetuning the base model. + +# 3 Method + +In this section, we will introduce the base model and how our proposed adaptive passage encoder works with it. + +# 3.1 Base Model + +Large generative ODQA models (Lewis et al., 2020b; Izacard and Grave, 2020b) share a similar encoder-decoder architecture. They first concatenate the question with all retrieved passages. Then the encoder encodes all passages and produces their hidden representations $h_1^L, \dots, h_N^L$ , where $L$ is the number of encoder layers and $N$ is the number of retrieved passages. We denote the hidden representation of the $i$ -th passage at its $j$ -th encoder layer as $h_i^j$ . The decoder will attend to these hidden representations and generate the answer tokens sequentially. + +# 3.2 Adaptive Passage Encoder + +As shown in Fig. 1, the adaptive passage encoder overrides the layer-by-layer computation of the encoder of the base model with an adaptive computation policy. It adds two components on top of the + +base encoder to define the policy: an answerability prediction model HasAnswer and a scheduler. + +The HasAnswer model predicts the probability that a passage contains an answer to the question, given its hidden representation $h_i^j$ . It first pools hidden representation $h_i^j$ into a vector, then feeds the pooled representation to a multi-layer perceptron to produce the probability $p_i^j$ . + +The scheduler is then responsible for the selection and prioritisation of passages that are likely to contain the answer (Wu et al., 2020). As shown by the blue arrows in Fig. 1, the scheduler learns a scheduling policy to allocate encoder layer computation to passages. The scheduler will exit in early layers for those spurious passages while allocating more layers to the ones that it finds promising. + +To achieve this goal, the scheduler produces a priority score $q_{n}$ for each passage: + +$$ +q _ {n} = \sigma \left(g \left(p _ {n} ^ {l _ {n}}, n, l _ {n}\right)\right) p _ {n} ^ {l _ {n}} + f \left(p _ {n} ^ {l _ {n}}, n, l _ {n}\right) \tag {1} +$$ + +where $n$ is the passage rank by the retriever, $l_{n}$ is the index of its current encoder layer, $g$ and $f$ are two multi-layer perceptrons that learn the weight and bias respectively. Starting at the initial layer for all passages, the scheduler will select a passage with the maximum priority, forward one encoder layer for it $l_{n}^{\prime} = l_{n} + 1$ , and updates its priorities $q_{n}$ with its new hidden representation $h_{n}^{l_{n}^{\prime}}$ and has-answer probability $p_{n}^{l_{n}^{\prime}}$ . This process will iterate for $B$ (budget) steps, and only $k$ passages with the most layers computed are retained in the end. + +# 3.3 Training the Adaptive Passage Encoder + +Differently from Wu et al. (2020), our method does not require tuning the underlying base model. Since the number of parameters introduced by the HasAnswer model and the scheduler is less than $4\%$ of the base model, APE can be trained very efficiently. The HasAnswer model is first trained with cross-entropy loss, supervised by the has-answer labels of the passages. Then we fix HasAnswer and train the scheduler with REINFORCE algorithm (Williams, 1992) to maximise the expected return, which is defined to encourage selection and prioritisation of passages that contain the answer. The selection action gains a positive reward $(1 - c)$ if it selects a relevant passage, otherwise a negative reward $-c$ . Since the weight $g$ and bias $f$ in Eq. (1) are automatically learned during the training of the scheduler, our method does not require confidence + +
TrainValidationTest
NaturalQuestions79,1688,7573,610
TriviaQA78,7858,83711,313
+ +Table 1: Number of samples of the evaluated datasets. + +calibration of the HasAnswer model, unlike the method proposed by Wu et al. (2020). + +# 4 Experiments + +# 4.1 Experimental Setup + +Datasets Following (Lee et al., 2019; Izacard and Grave, 2020b), we evaluate our method on NaturalQuestions (Kwiatkowski et al., 2019) and TriviaQA (Joshi et al., 2017) whose statistics are shown in Table 1. + +Evaluation Metrics Following Wu et al. (2020), we conduct the evaluation under different computational costs at inference time. Since the number of passages $k$ is almost linearly correlated with memory consumption and number of operations, we evaluate the performances with various number of passages $k \in \{5, 10, 20\}$ . To evaluate the end performance of ODQA models, we use the standard Exact Match (EM) score, which is the proportion of questions whose predicted answer matches exactly with the ground truth. We also include the unrestricted setting to compare the best performances of different models. + +Technical Details We use FiD (Izacard and Grave, 2020b) as our base model. FiD-base and FiD-large contain $L = 12$ and 24 layers respectively, and we set the budget $B = Lk$ . For the pooling operation in the HasAnswer model, we found max-pooling works better than mean-pooling and the [CLS] token, so max-pooling is used in all our experiments. We use discount factor $\gamma = 0.8$ and step penalty $c = 0.1$ during the REINFORCE training of the scheduler. More hyperparameters are presented in Appendix A.1. + +Computational Feasibility Tuning a FiD-base model with $k = 20$ or a FiD-large model with $k = 10$ (batch size=1) would yield out-of-memory errors on a V100 (16GB) GPU. Hence, it is infeasible to train FiD with the previous AC method (Wu et al., 2020) in our setting. However, training with our proposed approach can be done in the same setting with a batch size 4 or larger within 8-15 + +
NaturalQuestionsTriviaQA
Top-5Top-10Top-20UnrestrictedTop-5Top-10Top-20Unrestricted
SkylineBuilder (Wu et al., 2020)34.434.2-34.2----
DPR (Karpukhin et al., 2020)-40.8-41.5---57.9
DPR (our implementation)38.440.240.240.2----
RAG (Lewis et al., 2020b)43.544.144.144.5---56.1
FiD-base (Izacard and Grave, 2020b)39.542.945.348.253.957.960.765.0
Ours (APE+FiD-base)40.343.746.048.255.4*59.0*62.0*65.0
FiD-large (Izacard and Grave, 2020b)42.545.848.351.457.260.663.767.6
Ours (APE+FiD-large)43.446.649.151.457.961.4*64.1*67.6
+ +Table 2: Exact match scores on NaturalQuestions and TriviaQA test sets. * indicates statistical significance. + +
NaturalQuestionsTriviaQA
Top-5Top-10Top-20Top-100Top-5Top-10Top-20Top-100
BM25 (Lee et al., 2019)--59.173.7--66.976.7
DPR (Karpukhin et al., 2020)67.1-78.485.4--79.485.0
FiD (Izacard and Grave, 2020b)66.273.979.286.169.874.978.984.8
Ours (APE+FiD-base)67.4*75.1*80.4*86.170.8*75.8*79.584.8
Ours (APE+FiD-large)67.275.4*80.2*86.170.475.6*79.284.8
+ +Table 3: Top-k retrieval accuracy scores on NaturalQuestions and TriviaQA test sets. * indicates statistical significance. + +hours. + +# 4.2 Experimental Results + +As shown in Table 2 under restricted top- $k$ , our proposed method improves upon the FiD model on both datasets, and by a statistically significant margin on TriviaQA. It also outperforms the previous AC method (Wu et al., 2020) by $12.4\%$ when $k = 10$ due to the stronger base model. The addition of APE allows FiD to significantly outperform RAG (Lewis et al., 2020b) on NaturalQuestions when $k \in \{10, 20\}$ . + +Previous adaptive computation methods (Wu et al., 2020; Schwartz et al., 2020b) was reported to have plateaued or degraded performances in the unrestricted setting. However, Table 2 shows that our approach does not have this issue. + +# 4.3 Analysis of Passage Quality + +To understand how APE outperforms the baselines, we analyse the quality of the final top- $k$ passages retained by APE. Table 3 reports the top- $k$ retrieval accuracy of the top- $k$ passages. The results show that the top- $k$ accuracy of the selected collection of documents by APE is significantly better than BM25, DPR, and FiD, which are strong retrieval + +belines for ODQA. Combined with Table 2, it indicates that the better passage quality yielded by APE helps to improve the end ODQA performance of the model. + +# 5 Conclusions + +In this work, we explore an adaptive computation method that can be efficiently applied to an existing generative ODQA model. We find that, by replacing the encoder of generative ODQA models with our proposed adaptive passage encoder, we can train an effective adaptive computation policy without tuning the base model. This allows applying adaptive computation to large state-of-the-art generative models, which was previously challenging computation-wise. Our experimental results show that our method produces more accurate results than a state-of-the-art generative model on both NaturalQuestions and TriviaQA, and it outperforms the previous AC method by a large margin. The analysis also shows that our approach achieves better passage quality that leads to improvements in ODQA performance. + +# Acknowledgments + +The first author would like to thank his wife Jane for her love and support throughout the years. We would also like to thank Gautier Izacard and Edouard Grave for their help with using FiD. This research was supported by the European Union's Horizon 2020 research and innovation programme under grant agreement no. 875160. + +# References + +Akari Asai, Kazuma Hashimoto, Hannaneh Hajishirzi, Richard Socher, and Caiming Xiong. 2020. Learning to retrieve reasoning paths over wikipedia graph for question answering. In ICLR. OpenReview.net. +Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes. 2017. Reading wikipedia to answer open-domain questions. In ACL (1), pages 1870-1879. Association for Computational Linguistics. +Christopher Clark and Matt Gardner. 2018. Simple and effective multi-paragraph reading comprehension. In ACL (1), pages 845-855. Association for Computational Linguistics. +Gautier Izacard and Edouard Grave. 2020a. Distilling knowledge from reader to retriever for question answering. CoRR, abs/2012.04584. +Gautier Izacard and Edouard Grave. 2020b. Leveraging passage retrieval with generative models for open domain question answering. CoRR, abs/2007.01282. +Mandar Joshi, Eunsol Choi, Daniel S. Weld, and Luke Zettlemoyer. 2017. Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension. In ACL (1), pages 1601-1611. Association for Computational Linguistics. +Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick S. H. Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020. Dense passage retrieval for open-domain question answering. In EMNLP (1), pages 6769-6781. Association for Computational Linguistics. +Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur P. Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, Kristina Toutanova, Llion Jones, Matthew Kelcey, Ming-Wei Chang, Andrew M. Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov. 2019. Natural questions: a benchmark for question answering research. Trans. Assoc. Comput. Linguistics, 7:452-466. +Kenton Lee, Ming-Wei Chang, and Kristina Toutanova. 2019. Latent retrieval for weakly supervised open domain question answering. In ACL (1), pages 6086-6096. Association for Computational Linguistics. + +Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020a. BART: denoising sequence-to-sequence pretraining for natural language generation, translation, and comprehension. In ACL, pages 7871-7880. Association for Computational Linguistics. +Patrick S. H. Lewis, Ethan Perez, Aleksandra Pik-tus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Kuttler, Mike Lewis, Wen-tau Yih, Tim Roektaschel, Sebastian Riedel, and Douwe Kiela. 2020b. Retrieval-augmented generation for knowledge-intensive NLP tasks. In NeurIPS. +Weijie Liu, Peng Zhou, Zhiruo Wang, Zhe Zhao, Haotang Deng, and Qi Ju. 2020. Fastbert: a self-distilling BERT with adaptive inference time. In ACL, pages 6035-6044. Association for Computational Linguistics. +Yuning Mao, Pengcheng He, Xiaodong Liu, Ye-long Shen, Jianfeng Gao, Jiawei Han, and Weizhu Chen. 2021. Reader-guided passage reranking for open-domain question answering. CoRR, abs/2101.00294. +Sewon Min, Danqi Chen, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2019. A discrete hard EM approach for weakly supervised question answering. In EMNLP/IJCNLP (1), pages 2851-2864. Association for Computational Linguistics. +Sewon Min, Julian Michael, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2020. Ambigqa: Answering ambiguous open-domain questions. In EMNLP (1), pages 5783-5797. Association for Computational Linguistics. +Rodrigo Nogueira and Kyunghyun Cho. 2019. Passage re-ranking with BERT. CoRR, abs/1901.04085. +Yifan Qiao, Chenyan Xiong, Zhenghao Liu, and Zhiyuan Liu. 2019. Understanding the behaviors of BERT in ranking. CoRR, abs/1904.07531. +Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020. Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res., 21:140:1-140:67. +Stephen Robertson. 2004. Understanding inverse document frequency: on theoretical arguments for IDF. Journal of Documentation, 60(5):503-520. +Roy Schwartz, Jesse Dodge, Noah A. Smith, and Oren Etzioni. 2020a. Green AI. Commun. ACM, 63(12):54-63. +Roy Schwartz, Gabriel Stanovsky, Swabha Swayamdipta, Jesse Dodge, and Noah A. Smith. 2020b. The right tool for the job: Matching model and instance complexities. In ACL, pages 6640-6651. Association for Computational Linguistics. + +Zhiguo Wang, Patrick Ng, Xiaofei Ma, Ramesh Nallapati, and Bing Xiang. 2019. Multi-passage BERT: A globally normalized BERT model for open-domain question answering. In EMNLP/IJCNLP (1), pages 5877-5881. Association for Computational Linguistics. +R. J. Williams. 1992. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learning, 8:229-256. +Yuxiang Wu, Sebastian Riedel, Pasquale Minervini, and Pontus Stenetorp. 2020. Don't read too much into it: Adaptive computation for open-domain question answering. In EMNLP (1), pages 3029-3039. Association for Computational Linguistics. +Ji Xin, Raphael Tang, Jaejun Lee, Yaoliang Yu, and Jimmy Lin. 2020. Deebert: Dynamic early exiting for accelerating BERT inference. In ACL, pages 2246-2251. Association for Computational Linguistics. +Wei Yang, Yuqing Xie, Aileen Lin, Xingyu Li, Luchen Tan, Kun Xiong, Ming Li, and Jimmy Lin. 2019. End-to-end open-domain question answering with bertserini. In NAACL-HLT (Demonstrations), pages 72-77. Association for Computational Linguistics. + +# A Experimental Details + +# A.1 Hyper-parameters + +
Hyper-parameterValue
learning rate1e-4
batch size24
epoch2
optimiserAdam
Adam ε1e-6
Adam (β1, β2)(0.9, 0.999)
max sequence length256
poolingmax-pooling
number of passages5/10/20
deviceNvidia V100
+ +Table 4: Hyper-parameters for the HasAnswer model training. + +
Hyper-parameterValue
learning rate0.01
batch size24
epoch1
optimiserAdam
max number of steps240
step cost c0.1
discount factor γ0.8
hidden size of MLPs64
number of passages20/30/50
+ +Table 5: Hyper-parameters for scheduler model REINFORCE training. \ No newline at end of file diff --git a/trainingadaptivecomputationforopendomainquestionansweringwithcomputationalconstraints/images.zip b/trainingadaptivecomputationforopendomainquestionansweringwithcomputationalconstraints/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..cc022bf8d7e8974b2aa92e746c26098aab07458b --- /dev/null +++ b/trainingadaptivecomputationforopendomainquestionansweringwithcomputationalconstraints/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bdc13aea0b52e36fefe2b37076a81e5bd370b7ff94a8d7566bd9759c031ac184 +size 209097 diff --git a/trainingadaptivecomputationforopendomainquestionansweringwithcomputationalconstraints/layout.json b/trainingadaptivecomputationforopendomainquestionansweringwithcomputationalconstraints/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..bf768e7b3deccdaa5bf3f16037a93ea532c291ec --- /dev/null +++ b/trainingadaptivecomputationforopendomainquestionansweringwithcomputationalconstraints/layout.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ca35f468e5d96c9d8614f548faaad0aa53151a773dcc8e5637e44597f521d1bb +size 186513 diff --git a/umicanunreferencedmetricforimagecaptioningviacontrastivelearning/db7287e3-5637-49ca-986d-142be1c84005_content_list.json b/umicanunreferencedmetricforimagecaptioningviacontrastivelearning/db7287e3-5637-49ca-986d-142be1c84005_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..e475360eea64d04d0ff073523cd81c22a4242ac8 --- /dev/null +++ b/umicanunreferencedmetricforimagecaptioningviacontrastivelearning/db7287e3-5637-49ca-986d-142be1c84005_content_list.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1b1d3b41c8cfa9b08c4604981c13c46aec66f932c19dc30bb26ca2fd5ab06723 +size 49460 diff --git a/umicanunreferencedmetricforimagecaptioningviacontrastivelearning/db7287e3-5637-49ca-986d-142be1c84005_model.json b/umicanunreferencedmetricforimagecaptioningviacontrastivelearning/db7287e3-5637-49ca-986d-142be1c84005_model.json new file mode 100644 index 0000000000000000000000000000000000000000..c9acd259e96dc3c76c8dcc7b9fb050388835a1ae --- /dev/null +++ b/umicanunreferencedmetricforimagecaptioningviacontrastivelearning/db7287e3-5637-49ca-986d-142be1c84005_model.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f68ee1a71214f945778530c1162c04956f37061347abd2997998148da5e910c1 +size 60311 diff --git a/umicanunreferencedmetricforimagecaptioningviacontrastivelearning/db7287e3-5637-49ca-986d-142be1c84005_origin.pdf b/umicanunreferencedmetricforimagecaptioningviacontrastivelearning/db7287e3-5637-49ca-986d-142be1c84005_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..6ecaca3c1af3af85c847d17a89177a1a8534b9c1 --- /dev/null +++ b/umicanunreferencedmetricforimagecaptioningviacontrastivelearning/db7287e3-5637-49ca-986d-142be1c84005_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f36f396f72fae4376b6772129d99e317d6f9deb5a1875e56ab510503c59ae503 +size 1419630 diff --git a/umicanunreferencedmetricforimagecaptioningviacontrastivelearning/full.md b/umicanunreferencedmetricforimagecaptioningviacontrastivelearning/full.md new file mode 100644 index 0000000000000000000000000000000000000000..e6e8c4b6edbeba9572b462002c326bfa379739eb --- /dev/null +++ b/umicanunreferencedmetricforimagecaptioningviacontrastivelearning/full.md @@ -0,0 +1,231 @@ +# UMIC: An Unreferenced Metric for Image Captioning via Contrastive Learning + +Hwanhee Lee $^{1}$ , Seunghyun Yoon $^{2}$ , Franck Dernoncourt $^{2}$ +Trung Bui $^{2}$ and Kyomin Jung $^{1}$ + +$^{1}$ Dept. of Electrical and Computer Engineering, Seoul National University, Seoul, Korea + $^{2}$ Adobe Research, San Jose, CA, USA, {wanted1007, kjung}@snu.ac.kr {syoon, franck.dernoncourt, bui}@adobe.com + +# Abstract + +Despite the success of various text generation metrics such as BERTScore, it is still difficult to evaluate the image captions without enough reference captions due to the diversity of the descriptions. In this paper, we introduce a new metric UMIC, an Unreferenced Metric for Image Captioning which does not require reference captions to evaluate image captions. Based on Vision-and-Language BERT, we train UMIC to discriminate negative captions via contrastive learning. Also, we observe critical problems of the previous benchmark dataset (i.e., human annotations) on image captioning metric, and introduce a new collection of human annotations on the generated captions. We validate UMIC on four datasets, including our new dataset, and show that UMIC has a higher correlation than all previous metrics that require multiple references. We release the benchmark dataset and pre-trained models to compute the UMIC1. + +# 1 Introduction + +Image captioning is a task that aims to generate a description that explains the given image in a natural language. While there have been many advances for caption generation algorithms (Vinyals et al., 2015; Anderson et al., 2018) and target datasets (Fang et al., 2015; Sharma et al., 2018), few studies (Vedantam et al., 2015; Anderson et al., 2016; Cui et al., 2018; Lee et al., 2020) have focused on assessing the quality of the generated captions. Especially, most of the evaluation metrics only use reference captions to evaluate the caption although the main context is an image. However, as shown in Figure 1, since there are many possible reference captions for a single image, a candidate caption can receive completely different scores depending on the type of reference (Yi + +![](images/da0891911459732ba3fb84b11d1a005684f4352e1a0b7334991786f0ca1b8918.jpg) + +Ref 1: A dog standing in the snow with a stick in its mouth. +Ref 2: A little dog holding sticks in its mouth. Candidate: A dog standing on the snow with a dog +CIDEr with Ref 1: 3.166 +CIDEr with Ref 2: 0.281 + +Human Judgments : 1.875 out of 5 + +Figure 1: An example where the metric score for a given candidate caption varies significantly depending on the reference type. + +et al., 2020). Because of this diverse nature of image captions, reference-based metrics usually use multiple references which are difficult to obtain. To overcome this limitation, we propose UMIC, an Unreference Metric for Image Captioning, which is not dependent on the reference captions and use an image-caption pair to evaluate a caption. We develop UMIC upon UNITER (Chen et al., 2020) which is a state-of-the-arts pre-trained representation for vision-and-language tasks. Since UNITER is pre-trained to predict the alignment for large amounts of image-text pairs, we consider that UNITER can be a strong baseline for developing an unreferenced metric. We fine-tune UNITER via contrastive learning, where the model is trained to compare and discriminate the ground-truth captions and diverse synthetic negative samples. We carefully prepare the negative samples that can represent most of the undesirable cases in captioning, such as grammatically incorrect, irrelevant to the image, or relevant but have wrong keyword. + +When evaluating the metric's performance, it is required to compare the correlations between human judgments and the metric's evaluation score for given datasets. We choose three standard benchmark datasets (i.e., Composite (Aditya et al., 2015), Flickr8k (Hodosh et al., 2013), PASCAL-50s (Vedantam et al., 2015)) and further analyze the quality of the dataset. Interestingly, we found that there exist critical issues in the benchmark datasets, + +such as poor-label or polarized-label. To perform a rigorous evaluation as well as stimulate the research in this area, we collect new 1,000 human judgments for the model-generated caption. Finally, we evaluate our proposed metric on four benchmark datasets, including our new dataset. Experimental results show that our proposed unreferenced metric is highly correlated with human judgments than all of the previous metrics that use reference captions. + +# 2 Related Work + +Image Captioning Metrics Following other text generation tasks such as dialogue systems and machine translation, n-gram similarity metrics such as BLEU (Papineni et al., 2002), ROUGE (Lin, 2004) and METEOR (Banerjee and Lavie, 2005) are widely used to evaluate an image caption. Especially, CIDEr (Vedantam et al., 2015), which weights each n-gram using TF-IDF, is widely used. SPICE (Anderson et al., 2016) is a captioning metric based on scene graph. BERTScore (Zhang et al., 2019), which computes the similarity of the contextualized embeddings, are also used. BERT-TBR (Yi et al., 2020) focuses on the variance in multiple hypothesis and ViLBERTScore (VBTScore) (Lee et al., 2020) utilizes ViLBERT (Lu et al., 2019) to improve BERTScore. + +Different from these metrics, VIFIDEL (Madhyastha et al., 2019) computes the word mover distance (Kusner et al., 2015) between the object labels in the image and the candidate captions, and it does not require reference captions. Similar to VIFIDEL, our proposed UMIC does not utilize the reference captions. However, UMIC directly uses image features and evaluates a caption in various perspectives compared to VIFIDEL. + +Quality Estimation Quality Estimation (QE) is a task that estimates the quality of the generated text without using the human references and this task is same as developing an unreferenced metric. QE is widely established in machine translation (MT) tasks (Specia et al., 2013; Martins et al., 2017; Specia et al., 2018). Recently, (Levinboim et al., 2021) introduces a large scale human ratings on image-caption pairs for training QE models in image captioning tasks. Our work also trains caption QE model, (i.e. unreferenced captioning metric) but we do not use human ratings to train the metric. Instead, we create diverse synthetic negative samples and train the metric with these samples via ranking loss. + +![](images/61cc7d33b2deb9d4dc28e0e6afd263b3fe5f0bf8b28dabf5164ffc76d906c134.jpg) +A person on bike going through green light with red bus nearby in a sunny day. UNITER $S_{x}$ Ranking Loss $\rightarrow$ UNITER $S_{\hat{x}}$ A person on bike going through green light with red truck nearby in a sunny day. +Figure 2: Overall training procedure of UMIC. Given an image $I$ , a positive caption $x$ and a negative caption $\hat{x}$ , we compute the score of each image-caption pair $S_{x}$ and $S_{\hat{x}}$ using UNITER respectively. Then, we fine-tune UNITER using raking loss that $S_{x}$ is higher than $S_{\hat{x}}$ . + +# 3 UMIC + +We propose UMIC, an unreferenced metric for image captioning using UNITER. We construct negative captions using the reference captions through the pre-defined rules. Then, we fine-tune UNITER to distinguish the reference captions and these synthetic negative captions to develop UMIC. + +# 3.1 Modeling + +Since UNITER is pre-trained to predict the alignment of large amounts of image-text pairs, we use the output of the layer that predicts this alignment as the baseline of UMIC to be fine-tuned. Specifically, we compute the score of a caption $\mathbf{S}(I,X)$ for given image $I = (i_{1},\dots,i_{N})$ and $X = (x_{1},\dots,x_{T})$ as follows. + +We first compute the contextual embedding for $I$ and $X$ using UNITER to get the joint representation of image and text as follows. + +$$ +i _ {[ C L S ]}, i _ {1}, \dots , i _ {N}, x _ {1}, \dots , x _ {T} = \text {U N I T E R} (I, X), \tag {1} +$$ + +where $i_{[CLS]}$ is a joint representation of the input image and input caption. Then we feed it into a single fully-connected layer to get a score as follows. + +$$ +\mathrm {S} (I, X) = \operatorname {s i g m o i d} \left(W i _ {[ C L S ]} + b\right), \tag {2} +$$ + +where $W$ and $b$ are trainable parameters. + +# 3.2 Negative Samples + +To model negative captions, we observe the captions' common error types in the model-generated captions. Specifically, we pick 100 bad captions in the order of whose human judgments are low in Composite and Flickr8k, respectively. Then, we categorize the main errors into three types:relevant but have wrong keywords, totally irrelevant to the image, grammatically incorrect. To model most + +![](images/a93cff1bb9da915f3314a1f8d9161118fe6ca1795a73631bd248b838fa236a95.jpg) +Original: a woman hugging a girl who is holding a suitcase +Substitution: a boy hugging a girl who is holding a suitcase +Random(Hard Negative): a very small cute child by a suitcase +Repetition & Removal: a woman hugging a girl is holding a suitcase suitcase +Figure 3: An example of the generated negative captions for the left image to train UMIC. Hard negative caption is one of the reference captions for the right image which is similar to the left image. + +imperfect captions including these frequent type errors, we prepare negative captions as follows. + +Substituting Keywords To mimic the captions that are relevant but have wrong keywords, as in the example of Figure 2, we randomly substitute $30\%$ of the words in the reference captions and use them as negative samples like Figure 3. The motivation we choose $30\%$ is that the average length of the generated caption is about 10 words and the number of keywords is usually around three. We only substitute verb, adjective, and noun, which are likely to be keywords since they are usually visual words. Also, we substitute them with the words with the same POS-Tags using the pre-defined dictionaries for the captions in the training set to conserve the sentence structure. + +Random Captions We randomly sample captions from other images and use them as negative samples to generate totally irrelevant captions for the given image. Also, similar to the imagedtext retrieval task, we use hard-negative captions, which are difficult to be discerned, with a probability of $50\%$ . Specifically, we utilize the captions of the images similar to the given images using the pre-trained image retrieval model. We get negative captions that are the captions of the similar image sets computed by image-text retrieval model VSE++ (Faghri et al., 2018) as in (Wang et al., 2020). Then, we sample the captions in the reference captions of the Top-3 similar image sets like the example in Figure 3. + +Repetition & Removal We find that some of the captions have repeated words or have incomplete sentences. Hence, we randomly repeat or remove + +some words in the reference captions with a probability of $30\%$ in the captions to generate these kinds of captions. Specifically, we choose to repeat or remove with a probability of $50\%$ for the sampled word. + +Word Order Permutation We further generate negative samples by randomly changing the word order of the reference captions, so that the model sees the overall structure of the sentence, not just the specific visual words. + +# 3.3 Contrastive Learning + +Using the negative captions generated by the above rules, we fine-tune UNITER via contrastive loss for positive caption $X$ and negative caption $\hat{X}$ as follows. + +$$ +L o s s = \max (0, M - (\mathrm {S} (I, X) - \mathrm {S} (I, \hat {X}))), \tag {3} +$$ + +where $M$ is the margin for the ranking loss, which is a hyperparameter. We make each batch composed of one positive caption and four negative captions that are made by each negative sample generation technique. + +# 4 Dataset + +We briefly explain the previous benchmark datasets for captioning metrics and analyze the problems for two of these datasets, Flickr8k and Composite. Also, we introduce a new benchmark dataset to alleviate the addressed problems. + +# 4.1 Commonly Used Datasets + +Composite consists of 11,985 human judgments for each candidate caption generated from three models and image pair. This dataset's human judgments range from 1 to 5, depending on the relevance between candidate caption and image. + +Flickr8k provides three expert annotations for each image and candidate caption on 5,822 images. The score ranges from 1 to 4, depending on how well the caption and image match. All of the captions in this dataset are reference captions or captions from other images. + +PASCAL50s contains 1,000 images from UIUC PASCAL Sentence Dataset with 50 reference captions for each image. Different from other datasets, this dataset provides 4,000 caption triplet $< A$ , $B$ , $C>$ composed of 50 reference captions $(A)$ and two candidate captions $(B, C)$ for the given image. There + +![](images/d0991823ffbcc96ee6eccc58305719661748021796a54c1d32cba20662ef0f1a.jpg) +Figure 4: Score distributions of human judgments in Composite, Flickr8k and our proposed CapEval1k dataset. All scores were normalized from 0 to 1. + +are human annotated answers to which is more similar to “A”, “B” or “C”. + +# 4.2 Problems in Flickr8k and Composite + +We investigate the human judgments in Flickr8k and Composite, and visualize the distributions of judgment scores for two datasets, Flickr8k and Composite in Figure 4, and find several problems. + +For the Flickr8k, most of the scores are less than 0.2 since the candidate captions were sampled by an image retrieval system from a reference caption pool, not model-generated captions. Therefore, most captions are not related to images and differ significantly from the model-generated captions. We argue that this naive configuration is not enough to distinguish the performance of the metric precisely. + +For the Composite, most of the scores are placed near 0 or 1. We explain this because only a single annotator annotates each sample's score resulting in biased output. We also manually investigated the captions and found that the captions are coarsely generated. Note that the captions for this dataset were generated by the old model (Karpathy and Fei-Fei, 2015; Aditya et al., 2015). For these reasons, we conclude that additional benchmark dataset is necessary to evaluate the captioning metrics. + +# 4.3 CapEval1k Dataset + +To alleviate the addressed issues in Flickr8k and Composite, we introduce a new dataset CapEval1k, which is composed of human judgments for the model-generated captions from four recently proposed models: Att2in (Rennie et al., 2017), Transformer (Vaswani et al., 2017), BUTD (Anderson et al., 2018) and AoANet (Huang et al., 2019). Different from Flickr8k and Composite, we ask each + +
MetricFlickr8kCompositeCapEval1kPASCAL50s
BLEU-10.2740.4060.23374.3
BLEU-40.2860.4390.23873.4
ROUGE-L0.3000.4170.22074.9
METEOR0.4030.4660.28878.5
CIDEr0.4190.4730.30776.1
SPICE0.4570.4860.27973.6
BERTScore0.3960.4560.27379.5
BERT-TBR0.4670.4390.25780.1
VBTScore0.5250.5140.35279.6
VIFIDEL0.3360.1910.14370.0
UMIC0.4680.5610.32885.1
UMICc0.4310.5540.29984.7
+ +Table 1: Columns 1 to 3 represent Kendall Correlation between human judgments and various metrics on Flickr8k, Composite and CapEval1k. All p-values in the results are $< 0.01$ . The last column shows the accuracy of matches between human judgments in PASCAL50s. + +annotator to evaluate the captions by considering three dimensions: fluency, relevance, descriptiveness. We hire 5 workers who are fluent in English for each assignment from Amazon Mechanical Turk and use the average score. We also provide the full instructions and details in Appendix. + +Since our CapEval1k dataset is composed of annotations via recently proposed models, the overall scores are relatively higher than other datasets as shown in Figure 4. Compared to other datasets, CapEval1k contains the annotators' comprehensive judgment across multiple dimensions in evaluating the quality of the generated captions, so we can see that the score distribution score is not concentrated in a particular area. + +# 5 Experiments + +# 5.1 Implementation Details + +We use the pre-trained UNITER-base with 12 layers in the official code provided by the authors (Chen et al., 2020). We use the COCO dataset (Fang et al., 2015) to fine-tune UNITER through ranking loss. We use the train and validation split of COCO dataset in (Chen et al., 2020). The number of the training set is $414\mathrm{k}$ , and the validation set is $25\mathrm{k}$ . We set the batch size of 320, learning rate of 2e-6, and fine-tune UNITER for a maximum of 4k steps. We select the model that shows the minimum loss in the validation set. We set margin $M$ as 0.2 in the ranking loss. We repeat training 5 times for each best-performing model. + +# 5.2 Performance Comparison + +We compute caption-level Kendall's correlation coefficient with human judgments for the Composite, + +Flickr8k, and our proposed CapEval1k. For the PASCAL50s, we compute the number of matches between human judgments for each candidate caption pair. For all of the reference based metrics, we use five reference captions and then get average score among the five references except for BERTScore where we use maximum. + +We present the experimental results for all four datasets in Table 1. We show that although UMIC does not utilize any reference captions, UMIC outperforms the baseline metrics except for VBTScore in all of the datasets that depend on multiple references. We also report the strong unreferenced baseline UMIC_C, which is directly using the pretrained weights from UNITER without contrastive learning. Interestingly, UMIC_C shows a higher performance than most of the metrics. This high performance shows that pre-trained image-text matching layer of UNITER already has a good representation for evaluating image captions. Especially for Composite, both UMIC and UMIC_C significantly outperform baseline metrics. We explain this in the polarized distribution of human judgments as we explained in Section 4.2. In other words, the relevance of most image-caption pairs in this dataset is too obvious so that UNITER can easily distinguish them. However, while UMIC shows higher performance on all datasets, UMIC_C shows relatively low performance on Flickr8k and CapEval1k. And this demonstrates the effectiveness and generalization ability of our contrastive learning objective to develop UMIC. + +Also, we can observe that the performance of each metric is relatively low and the rank of each metric changes in our proposed CapEval1k dataset. We explain that this is because the captions in CapEval1k are relatively difficult to be evaluated since the score distribution is not biased as explained in Section 4.3. + +# 5.3 Case Study + +We visualize one sample each showing the strengths and weaknesses of UMIC in Figure 5. In the above example, the candidate caption is partially relevant to the image, but the single word "three" in the caption is totally incorrect since there are only "two" giraffes in the image. And this leads to a low human judgment of 0.2. Nevertheless, unlike our UMIC, widely used metrics and UMIC_C give this caption a high score due to the many words overlaps or missing the keywords. The bot + +![](images/50833b529d7769e052abaaa9eae6cf8548268fa5b6bab409bac3880444319995.jpg) + +# References + +- two giraffes standing next to each other in a field. +- two giraffes are climbing a hill with mountains in the background. + +# Candidate + +- three giraffes standing in a field of grass + +
BLEU1: 0.324ROUGE-L: 0.320METEOR: 0.173CIDER: 0.866
SPICE: 0.289UMIC: 0.352UMIC_/−c: 0.770Human: 0.200
+ +![](images/1825f7aa36d561f4e538b6c498f8cc8c2660965d778e23eb549fceb6bdb0b1c0.jpg) +Figure 5: Case study for the various metrics on candidate captions in CapEval1k Dataset. Human judgments are normalized from 0 to 1. + +# References + +- a person breadking a bottle with a baseball bat +- a boy in yellow shirt swinging a baseball bat + +# Candidate + +- a man swinging a baseball bat at a ball + +
BLEU1: 0.360ROUGE-L: 0.354METEOR: 0.176CIDER: 1.205
SPICE: 0.192UMIC: 0.094UMIC/−c: 0.062Human: 0.450
+ +tom example shows one of the error cases and the limitations of our proposed method. Since the detection model in UMIC could not recognize the important object like the "baseball bat", UMIC outputs very low score. + +# 6 Conclusion + +In this paper, we propose UMIC, an unreferenced metric that does not require any reference captions for image captioning task through contrastive learning in UNITER. Also, we propose a new benchmark dataset for image captioning that relieve the issues in previous datasets. Experimental results on four benchmark datasets, including our new dataset, show that UMIC outperforms previous metrics. + +# Acknowledgements + +We thank anonymous reviewers for their constructive and insightful comments. K. Jung is with ASRI, Seoul National University, Korea. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (No. 2021R1A2C2008855). This work was partially funded by gifts from Adobe Research. + +# Ethical Considerations + +We compensate the annotators with competitive pay, which is above hourly USD $10 for collecting human annotated judgments for the model generated captions. Specifically, we pay$ 0.2 for each task that is composed of evaluating four candidate captions for a single image, where each task can be usually done in a minute. And we use public datasets to train the models. + +# References + +Somak Aditya, Yezhou Yang, Chitta Baral, Cornelia Fermuller, and Yiannis Aloimonos. 2015. From images to sentences through scene description graphs using commonsense reasoning and knowledge. arXiv preprint arXiv:1511.03292. +Peter Anderson, Basura Fernando, Mark Johnson, and Stephen Gould. 2016. Spice: Semantic propositional image caption evaluation. In European Conference on Computer Vision, pages 382-398. Springer. +Peter Anderson, Xiaodong He, Chris Buehler, Damien Teney, Mark Johnson, Stephen Gould, and Lei Zhang. 2018. Bottom-up and top-down attention for image captioning and visual question answering. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 6077-6086. +Satanjeev Banerjee and Alon Lavie. 2005. Meteor: An automatic metric for mt evaluation with improved correlation with human judgments. In Proceedings of the acl workshop on intrinsic and extrinsic evaluation measures for machine translation and/or summarization, pages 65-72. +Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, and Jingjing Liu. 2020. Uniter: Universal image-text representation learning. In European Conference on Computer Vision, pages 104-120. Springer. +Yin Cui, Guandao Yang, Andreas Veit, Xun Huang, and Serge Belongie. 2018. Learning to evaluate image captioning. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5804-5812. +Fartash Faghri, David J Fleet, Jamie Ryan Kiros, and Sanja Fidler. 2018. Vse++: Improving visual-semantic embeddings with hard negatives. +Hao Fang, Saurabh Gupta, Forrest Iandola, Rupesh K Srivastava, Li Deng, Piotr Dolkar, Jianfeng Gao, Xiaodong He, Margaret Mitchell, John C Platt, et al. 2015. From captions to visual concepts and back. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1473-1482. +Micah Hodosh, Peter Young, and Julia Hockenmaier. 2013. Framing image description as a ranking task: Data, models and evaluation metrics. Journal of Artificial Intelligence Research, 47:853-899. +Lun Huang, Wenmin Wang, Jie Chen, and Xiao-Yong Wei. 2019. Attention on attention for image captioning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4634-4643. +Andrej Karpathy and Li Fei-Fei. 2015. Deep visual-semantic alignments for generating image descriptions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3128-3137. + +Matt Kusner, Yu Sun, Nicholas Kolkin, and Kilian Weinberger. 2015. From word embeddings to document distances. In International conference on machine learning, pages 957-966. +Hwanhee Lee, Seunghyun Yoon, Franck Dernoncourt, Doo Soon Kim, Trung Bui, and Kyomin Jung. 2020. Vilbertscore: Evaluating image caption using vision-and-language bert. In Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems, pages 34-39. +Tomer Levinboim, Ashish V. Thapliyal, Piyush Sharma, and Radu Soricut. 2021. Quality estimation for image captions based on large-scale human evaluations. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 3157-3166, Online. Association for Computational Linguistics. +Chin-Yew Lin. 2004. Rouge: A package for automatic evaluation of summaries. In Text summarization branches out, pages 74-81. +Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee. 2019. Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. In Advances in Neural Information Processing Systems, pages 13-23. +Pranava Swaroop Madhyastha, Josiah Wang, and Lucia Specia. 2019. Vifidel: Evaluating the visual fidelity of image descriptions. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 6539-6550. +Andre FT Martins, Marcin Junczys-Dowmunt, Fabio N Kepler, Ramón Astudillo, Chris Hokamp, and Roman Grundkiewicz. 2017. Pushing the limits of translation quality estimation. Transactions of the Association for Computational Linguistics, 5:205-218. +Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pages 311-318. +Steven J Rennie, Etienne Marcheret, Youssef Mroueh, Jerret Ross, and Vaibhava Goel. 2017. Self-critical sequence training for image captioning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 7008-7024. +Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut. 2018. Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2556-2565. + +Lucia Specia, Frédéric Blain, Varvara Logacheva, Ramón Astudillo, and André FT Martins. 2018. Findings of the wmt 2018 shared task on quality estimation. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pages 689-709. +Lucia Specia, Kashif Shah, José GC De Souza, and Trevor Cohn. 2013. Quest-a translation quality estimation framework. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 79-84. +Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in neural information processing systems, pages 5998-6008. +Ramakrishna Vedantam, C Lawrence Zitnick, and Devi Parikh. 2015. Cider: Consensus-based image description evaluation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4566-4575. +Oriol Vinyals, Alexander Toshev, Samy Bengio, and Dumitru Erhan. 2015. Show and tell: A neural image caption generator. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3156-3164. +Jiuniu Wang, Wenjia Xu, Qingzhong Wang, and Antoni B Chan. 2020. Compare and reweight: Distinctive image captioning using similar images sets. In ECCV. +Yanzhi Yi, Hangyu Deng, and Jinglu Hu. 2020. Improving image captioning evaluation by considering inter references variance. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 985-994. +Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. 2019. Bertscore: Evaluating text generation with bert. arXiv preprint arXiv:1904.09675. \ No newline at end of file diff --git a/umicanunreferencedmetricforimagecaptioningviacontrastivelearning/images.zip b/umicanunreferencedmetricforimagecaptioningviacontrastivelearning/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..7a26ec79f820578b60f7c548953596b52b81f982 --- /dev/null +++ b/umicanunreferencedmetricforimagecaptioningviacontrastivelearning/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f3028e40f22c8d4f974d5e590be94c75a31152662b01b41a9c1511511806468d +size 147681 diff --git a/umicanunreferencedmetricforimagecaptioningviacontrastivelearning/layout.json b/umicanunreferencedmetricforimagecaptioningviacontrastivelearning/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..306241a76e6730365779c434e1443d82539c7c13 --- /dev/null +++ b/umicanunreferencedmetricforimagecaptioningviacontrastivelearning/layout.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4654bf802cfb9708ae7f8f6af522b812c79fc2210713fb8b32b85da8b11306db +size 238164 diff --git a/uncertaintyandsurprisaljointlydeliverthepunchlineexploitingincongruitybasedfeaturesforhumorrecognition/a368dd0e-5751-4527-a465-b9f0821eddb7_content_list.json b/uncertaintyandsurprisaljointlydeliverthepunchlineexploitingincongruitybasedfeaturesforhumorrecognition/a368dd0e-5751-4527-a465-b9f0821eddb7_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..5572cd1749e58ec0778793ea806b262a8e899bf2 --- /dev/null +++ b/uncertaintyandsurprisaljointlydeliverthepunchlineexploitingincongruitybasedfeaturesforhumorrecognition/a368dd0e-5751-4527-a465-b9f0821eddb7_content_list.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3f1bea3a6118d4847f4844eec6811cf4eff686e2ef74142f1b7e9e7d1a5b8da4 +size 46271 diff --git a/uncertaintyandsurprisaljointlydeliverthepunchlineexploitingincongruitybasedfeaturesforhumorrecognition/a368dd0e-5751-4527-a465-b9f0821eddb7_model.json b/uncertaintyandsurprisaljointlydeliverthepunchlineexploitingincongruitybasedfeaturesforhumorrecognition/a368dd0e-5751-4527-a465-b9f0821eddb7_model.json new file mode 100644 index 0000000000000000000000000000000000000000..dfb5863453c78437d9d50f98a490c967a6e638b1 --- /dev/null +++ b/uncertaintyandsurprisaljointlydeliverthepunchlineexploitingincongruitybasedfeaturesforhumorrecognition/a368dd0e-5751-4527-a465-b9f0821eddb7_model.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:31946a8505507c57585b80cc41deb1833e8653d5d918ff50bc0bb0078fb377c1 +size 56374 diff --git a/uncertaintyandsurprisaljointlydeliverthepunchlineexploitingincongruitybasedfeaturesforhumorrecognition/a368dd0e-5751-4527-a465-b9f0821eddb7_origin.pdf b/uncertaintyandsurprisaljointlydeliverthepunchlineexploitingincongruitybasedfeaturesforhumorrecognition/a368dd0e-5751-4527-a465-b9f0821eddb7_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..5f46ab34c1cfac80b80dc99c641be5a26c7fd737 --- /dev/null +++ b/uncertaintyandsurprisaljointlydeliverthepunchlineexploitingincongruitybasedfeaturesforhumorrecognition/a368dd0e-5751-4527-a465-b9f0821eddb7_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2be32e6c86b603c08b4659d7ccb5285909a3ec7cbcc88b8d82fbb4fa412752fa +size 299836 diff --git a/uncertaintyandsurprisaljointlydeliverthepunchlineexploitingincongruitybasedfeaturesforhumorrecognition/full.md b/uncertaintyandsurprisaljointlydeliverthepunchlineexploitingincongruitybasedfeaturesforhumorrecognition/full.md new file mode 100644 index 0000000000000000000000000000000000000000..260d009a6a2fede680e21fb1bfa74700f64747ff --- /dev/null +++ b/uncertaintyandsurprisaljointlydeliverthepunchlineexploitingincongruitybasedfeaturesforhumorrecognition/full.md @@ -0,0 +1,202 @@ +# Uncertainty and Surprisal Jointly Deliver the Punchline: Exploiting Incongruity-Based Features for Humor Recognition + +Yubo Xie, Junze Li, and Pearl Pu +School of Computer and Communication Sciences +École Polytechnique Fédérale de Lausanne +Lausanne, Switzerland +{yubo.xie, junze.li, pearl.pu}@epfl.ch + +# Abstract + +Humor recognition has been widely studied as a text classification problem using data-driven approaches. However, most existing work does not examine the actual joke mechanism to understand humor. We break down any joke into two distinct components: the set-up and the punchline, and further explore the special relationship between them. Inspired by the incongruity theory of humor, we model the setup as the part developing semantic uncertainty, and the punchline disrupting audience expectations. With increasingly powerful language models, we were able to feed the set-up along with the punchline into the GPT-2 language model, and calculate the uncertainty and surprisal values of the jokes. By conducting experiments on the SemEval 2021 Task 7 dataset, we found that these two features have better capabilities of telling jokes from non-jokes, compared with existing baselines. + +# 1 Introduction + +One of the important aspects of computational humor is to develop computer programs capable of recognizing humor in text. Early work on humor recognition (Mihalcea and Strapparava, 2005) proposed heuristic-based humor-specific stylistic features, for example alliteration, antonymy, and adult slang. More recent work (Yang et al., 2015; Chen and Soo, 2018; Weller and Seppi, 2019) regarded the problem as a text classification task, and adopted statistical machine learning methods and neural networks to train models on humor datasets. However, only few of the deep learning methods have tried to establish a connection between humor recognition and humor theories. Thus, one research direction in humor recognition is to bridge the disciplines of linguistics and artificial intelligence. + +In this paper, we restrict the subject of investigation to jokes, one of the most common humor types + +![](images/4ee3945b83e31e8ea34b3c5ff7ed95e72aa6f008945c39e4f59ebc7cfdfeca27.jpg) +Figure 1: A joke example consisting of a set-up and a punchline. A violation can be observed between the punchline and the expectation. + +in text form. As shown in Figure 1, these jokes usually consist of a set-up and a punchline. The set-up creates a situation that introduces the hearer into the story framework, and the punchline concludes the joke in a succinct way, intended to make the hearer laugh. Perhaps the most suitable humor theory for explaining such humor phenomenon is the incongruity theory, which states that the cause of laughter is the perception of something incongruous (the punchline) that violates the hearer's expectation (the set-up). + +Based on the incongruity theory, we propose two features for humor recognition, by calculating the degree of incongruity between the set-up and the punchline. Recently popular pre-trained language models enable us to study such relationship based on large-scale corpora. Specifically, we fed the set-up along with the punchline into the GPT-2 language model (Radford et al., 2019), and obtained the surprisal and uncertainty values of the joke, indicating how surprising it is for the model to generate the punchline, and the uncertainty while generating it. We conducted experiments on a manually labeled humor dataset, and the results showed that + +these two features could better distinguish jokes from non-jokes, compared with existing baselines. Our work made an attempt to bridge humor theories and humor recognition by applying large-scale pre-trained language models, and we hope it could inspire future research in computational humor. + +# 2 Related Work + +Humor Data Mihalcea and Strapparava (2005) created a one-liner dataset with humorous examples extracted from webpages with humor theme and non-humorous examples from Reuters titles, British National Corpus (BNC) sentences, and English Proverbs. Yang et al. (2015) scraped puns from the Pun of the Day website1 and negative examples from various news websites. There is also work on the curation of non-English humor datasets (Zhang et al., 2019; Blinov et al., 2019). Hasan et al. (2019) developed UR-FUNNY, a multimodal humor dataset that involves text, audio and video information extracted from TED talks. + +Humor Recognition Most of the existing work on humor recognition in text focuses on one-liners, one type of jokes that delivers the laughter in a single line. The methodologies typically fall into two categories: feature engineering and deep learning. Mihalcea and Strapparava (2005) designed three human-centric features (alliteration, antonymy and synonym) for recognizing humor in the curated one-liner dataset. Mihalcea et al. (2010) approached the problem by calculating the semantic relatedness between the set-up and the punchline (they evaluated 150 one-liners by manually splitting them into "setup" and "punchline"). Shahaf et al. (2015) investigated funny captions for cartoons and proposed several features including perplexity to distinguish between funny and less funny captions. Morales and Zhai (2017) proposed a probabilistic model and leveraged background text sources (such as Wikipedia) to identify humorous Yelp reviews. Liu et al. (2018) proposed to model sentiment association between elementary discourse units and designed features based on discourse relations. Cattle and Ma (2018) explored the usage of word associations as a semantic relatedness feature in a binary humor classification task. With neural networks being popular in recent years, some deep learning structures have been developed for the recognition of humor in text. Chen and Lee (2017) and + +Chen and Soo (2018) adopted convolutional neural networks, while Weller and Seppi (2019) used a Transformer architecture to do the classification task. Fan et al. (2020) incorporated extra phonetic and semantic (ambiguity) information into the deep learning framework. In addition to these methodological papers, there are also some tasks dedicated to computational humor in recent years. SemEval 2020 Task 7 (Hossain et al., 2020) aims at assessing humor in edited news headlines. SemEval 2021 Task 7 (Meaney et al., 2021) involves predicting the humor rating of the given text, and if the rating is controversial or not. In this task, Xie et al. (2021) adopted the DeBERTa architecture (He et al., 2020) with disentangled attention mechanism to predict the humor labels. + +Although the work of Mihalcea et al. (2010) is the closest to ours, we are the first to bridge the incongruity theory of humor and large-scale pretrained language models. Other work (Bertero and Fung, 2016) has attempted to predict punchlines in conversations extracted from TV series, but their subject of investigation should be inherently different from ours—punchlines in conversations largely depend on the preceding utterances, while jokes are much more succinct and self-contained. + +# 3 Humor Theories + +The attempts to explain humor date back to the age of ancient Greece, where philosophers like Plato and Aristotle regarded the enjoyment of comedy as a form of scorn, and held critical opinions towards laughter. These philosophical comments on humor were summarized as the superiority theory, which states that laughter expresses a feeling of superiority over other people's misfortunes or shortcomings. Starting from the $18^{\text{th}}$ century, two other humor theories began to challenge the dominance of the superiority theory: the relief theory and the incongruity theory. The relief theory argues that laughter serves to facilitate the relief of pressure for the nervous system (Morreall, 2020). This explains why laughter is caused when people recognize taboo subjects—one typical example is the wide usage of sexual terms in jokes. The incongruity theory, supported by Kant (1790), Schopenhauer (1883), and many later philosophers and psychologists, states that laughter comes from the perception of something incongruous that violates the expectations. This view of humor fits well the types of jokes commonly found in stand-up comedies, + +where the set-up establishes an expectation, and then the punchline violates it. As an expansion of the incongruity theory, Raskin (1979) proposed the Semantic Script-based Theory of Humor (SSTH) by applying the semantic script theory. It posits that, in order to produce verbal humor, two requirements should be fulfilled: (1) The text is compatible with two different scripts; (2) The two scripts with which the text is compatible are opposite. + +# 4 Methodology + +The incongruity theory attributes humor to the violation of expectation. This means the punchline delivers the incongruity that turns over the expectation established by the set-up, making it possible to interpret the set-up in a completely different way. With neural networks blooming in recent years, pretrained language models make it possible to study such relationship between the set-up and the punchline based on large-scale corpora. Given the set-up, language models are capable of writing expected continuations, enabling us to measure the degree of incongruity, by comparing the actual punchline with what the language model is likely to generate. + +In this paper, we leverage the GPT-2 language model (Radford et al., 2019), a Transformer-based architecture trained on the WebText dataset. We chose GPT-2 because: (1) GPT-2 is already pretrained on massive data and publicly available online, which spares us the training process; (2) it is domain independent, thus suitable for modeling various styles of English text. Our goal is to model the set-up and the punchline as a whole piece of text using GPT-2, and analyze the probability of generating the punchline given the set-up. In the following text, we denote the set-up as $x$ , and the punchline as $y$ . Basically, we are interested in two quantities regarding the probability distribution $p(y|x)$ : uncertainty and surprisal, which are elaborated in the next two sections. + +# 4.1 Uncertainty + +The first question we are interested in is: given the set-up, how uncertain it is for the language model to continue? This question is related to SSTH, which states that, for a piece of text to be humorous, it should be compatible with two different scripts. To put it under the framework of set-up and punchline, this means the set-up could have multiple ways of interpretation, according to the following punchline. Thus, one would expect a higher uncertainty + +![](images/b4b39bbce169f3c1f6124fdd63d46181e57922f098a22146b36b72bfcb0d6fe6.jpg) +Figure 2: The set-up $x$ and the punchline $y$ are concatenated and fed into GPT-2 for predicting the next token. $v_{i}$ 's are probability distributions on the vocabulary. + +value when the language model tries to continue the set-up and generate the punchline. + +We propose to calculate the averaged entropy of the probability distributions at all token positions of the punchline, to represent the degree of uncertainty. As shown in Figure 2, the set-up $x$ and the punchline $y$ are concatenated and then fed into GPT-2 to predict the next token. While predicting the tokens of $y$ , GPT-2 produces a probability distribution $v_{i}$ over the vocabulary. The averaged entropy is then defined as + +$$ +U (x, y) = - \frac {1}{| y |} \sum_ {i = 1} ^ {n} \sum_ {w \in V} v _ {i} ^ {w} \log v _ {i} ^ {w}, \tag {1} +$$ + +where $V$ is the vocabulary. + +# 4.2 Surprisal + +The second question we would like to address is: how surprising it is when the language model actually generates the punchline? As the incongruity theory states, laughter is caused when something incongruous is observed and it violates the previously established expectation. Therefore, we expect the probability of the language model generating the actual punchline to be relatively low, which indicates the surprisal value should be high. Formally, the surprisal is defined as + +$$ +\begin{array}{l} S (x, y) = - \frac {1}{| y |} \log p (y | x) \\ = - \frac {1}{| y |} \sum_ {i = 1} ^ {n} \log v _ {i} ^ {y _ {i}}. \tag {2} \\ \end{array} +$$ + +# 5 Experiments + +We evaluated and compared the proposed features with several baselines by conducting experiments + +in two settings: predicting using individual features, and combining the features with a content-based text classifier. + +# 5.1 Baselines + +Similar to our approach of analyzing the relationship between the set-up and the punchline, Mihalcea et al. (2010) proposed to calculate the semantic relatedness between the set-up and the punchline. The intuition is that the punchline (which delivers the surprise) will have a minimum relatedness to the set-up. For our experiments, we chose two relatedness metrics that perform the best in their paper as our baselines, plus another similarity metric based on shortest paths in WordNet (Miller, 1995): + +- Leacock & Chodorow similarity (Leacock and Chodorow, 1998), defined as + +$$ +\operatorname {S i m} _ {l c h} = - \log \frac {\text {l e n g t h}}{2 * D}, \tag {3} +$$ + +where length is the length of the shortest path between two concepts using node-counting, and $D$ is the maximum depth of WordNet. + +- Wu & Palmer similarity (Wu and Palmer, 1994) calculates similarity by considering the depths of the two synsets in WordNet, along with the depth of their LCS (Least Common Subsumer), which is defined as + +$$ +\operatorname {S i m} _ {w u p} = \frac {2 * \operatorname {d e p t h} (L C S)}{\operatorname {d e p t h} \left(C _ {1}\right) + \operatorname {d e p t h} \left(C _ {2}\right)}, \tag {4} +$$ + +where $C_1$ and $C_2$ denote synset 1 and synset 2 respectively. + +- Path similarity (Rada et al., 1989) is also based on the length of the shortest path between two concepts in WordNet, which is defined as + +$$ +\operatorname {S i m} _ {\text {p a t h}} = \frac {1}{1 + \text {l e n g t h}}. \tag {5} +$$ + +In addition to the metrics mentioned above, we also consider the following two baselines related to the phonetic and semantic styles of the input text: + +- Alliteration. The alliteration value is computed as the total number of alliteration chains and rhyme chains found in the input text (Mihalcea and Strapparava, 2005). + +- Ambiguity. Semantic ambiguity is found to be a crucial part of humor (Miller and Gurevych, 2015). We follow the work of Liu et al. (2018) to compute the ambiguity value: + +$$ +\log \prod_ {w \in s} \operatorname {n u m} _ {\text {o f}} \operatorname {s e n s e s} (w), \tag {6} +$$ + +where $w$ is a word in the input text $s$ . + +# 5.2 Dataset + +We took the dataset from SemEval 2021 Task 7. The released training set contains 8,000 manually labeled examples in total, with 4,932 being positive, and 3,068 negative. To adapt the dataset for our purpose, we only considered positive examples with exactly two sentences, and negative examples with at least two sentences. For positive examples (jokes), the first sentence was treated as the set-up and the second the punchline. For negative examples (non-jokes), consecutive two sentences were treated as the set-up and the punchline, respectively. After splitting, we cleaned the data with the following rules: (1) We restricted the length of set-ups and punchlines to be under 20 (by counting the number of tokens); (2) We only kept punchlines whose percentage of alphabetical letters is greater than or equal to $75\%$ ; (3) We discarded punchlines that do not begin with an alphabetical letter. As a result, we obtained 3,341 examples in total, consisting of 1,815 jokes and 1,526 non-jokes. To further balance the data, we randomly selected 1,526 jokes, and thus the final dataset contains 3,052 labeled examples in total. For the following experiments, we used 10-fold cross validation, and the averaged scores are reported. + +# 5.3 Predicting Using Individual Features + +To test the effectiveness of our features in distinguishing jokes from non-jokes, we built an SVM classifier (parameters can be found in Appendix A) for each individual feature (uncertainty and surprisal, plus the baselines). The resulted scores are reported in Table 1. Compared with the baselines, both of our features (uncertainty and surprisal) achieved higher scores for all the four metrics. In addition, we also tested the performance of uncertainty combined with surprisal (last row + +
PRF1Acc
Random0.49730.49730.49580.4959
Simlch0.52910.51790.46800.5177
Simwup0.52890.52170.49190.5190
Simpath0.54350.52980.49030.5291
Alliteration0.53530.53490.53430.5354
Ambiguity0.54610.53650.51270.5337
Uncertainty0.58400.57380.55930.5741
Surprisal0.56170.55650.54550.5570
U+S0.59530.58340.56950.5832
+ +Table 1: Performance of individual features. Last row $(\mathrm{U} + \mathrm{S})$ is the combination of uncertainty and surprisal. P: Precision, R: Recall, F1: F1-score, Acc: Accuracy, P, R, and F1 are macro-averaged, and the scores are reported on 10-fold cross validation. + +
PRF1Acc
GloVe0.82330.82320.82290.8234
GloVe+Simlch0.82550.82510.82470.8250
GloVe+Simwup0.82640.82600.82540.8257
GloVe+Simpath0.82520.82440.82390.8244
GloVe+Alliter.0.82990.82920.82910.8297
GloVe+Amb.0.82110.82030.81980.8201
GloVe+U0.83550.83590.83530.8359
GloVe+S0.83310.83260.83210.8326
GloVe+U+S0.83680.83680.83630.8365
+ +Table 2: Performance of the features when combined with a content-based classifier. U denotes uncertainty and S denotes surprisal. P: Precision, R: Recall, F1: F1-score, Acc: Accuracy. P, R, and F1 are macro-averaged, and the scores are reported on 10-fold cross validation. + +of the table), and the resulting classifier shows a further increase in the performance. This suggests that, by jointly considering uncertainty and surprisal of the set-up and the punchline, we are better at recognizing jokes. + +# 5.4 Boosting a Content-Based Classifier + +Now that we have shown the advantage of our features when used individually in prediction, we would like to validate their effectiveness when combined with the commonly used word embeddings. Thus, we evaluated our features as well as the baselines under the framework of a content-based classifier. The idea is to see if the features could further boost the performance of existing text classifiers. To create a starting point, we encoded each set-up and punchline into vector representations by aggregating the GloVe (Pennington et al., 2014) embeddings of the tokens (sum up and then normalize by the length). We used the GloVe embeddings + +![](images/2206a0057953b613b450221fbe47e885b5da241de4f1bfc9949b67b1b31096c0.jpg) +Figure 3: Histograms of uncertainty (left) and surprisal (right), plotted separately for jokes and non-jokes. Mdn stands for Median. + +![](images/99f4c8d202b6b3bfa6bc8d8ee6c94ef48afd8563c848f5b24ddbc2ecee427fb4.jpg) + +with dimension 50, and then concatenated the setup vector and the punchline vector, to represent the whole piece of text as a vector of dimension 100. For each of the features (uncertainty and surprisal, plus the baselines), we appended it to the GloVe vector, and built an SVM classifier to do the prediction. Scores are reported in Table 2. As we can see, compared with the baselines, our features produce larger increases in the performance of the content-based classifier, and similar to what we have observed in Table 1, jointly considering uncertainty and surprisal gives further increase in the performance. + +# 6 Visualizing Uncertainty and Surprisal + +To get a straightforward vision of the uncertainty and surprisal values for jokes versus non-jokes, we plot their histograms in Figure 3 (for all 3,052 labeled examples). It can be observed that, for both uncertainty and surprisal, jokes tend to have higher values than non-jokes, which is consistent with our expectations in Section 4. + +# 7 Conclusion + +This paper makes an attempt in establishing a connection between the humor theories and the nowadays popular pre-trained language models. We proposed two features according to the incongruity theory of humor: uncertainty and surprisal. We conducted experiments on a humor dataset, and the results suggest that our approach has an advantage in humor recognition over the baselines. The proposed features can also provide insight for the task of two-line joke generation—when designing the text generation algorithm, one could exert extra constraints so that the set-up is chosen to be compatible with multiple possible interpretations, and the punchline should be surprising in a way that violates the most obvious interpretation. We hope our work could inspire future research in the community of computational humor. + +# References + +Dario Bertero and Pascale Fung. 2016. A long short-term memory framework for predicting humor in dialogues. In Proceedings of NAACL-HLT 2016, pages 130-135. +Vladislav Blinov, Valeria Bolotova-Baranova, and Pavel Braslavski. 2019. Large dataset and language model fun-tuning for humor recognition. In Proceedings of ACL 2019, pages 4027-4032. +Andrew Cattle and Xiaojuan Ma. 2018. Recognizing humour using word associations and humour anchor extraction. In Proceedings of COLING 2018, pages 1849-1858. +Lei Chen and Chong Min Lee. 2017. Convolutional neural network for humor recognition. CoRR, abs/1702.02584. +Peng-Yu Chen and Von-Wun Soo. 2018. Humor recognition using deep learning. In Proceedings of NAACL-HLT 2018, Volume 2 (Short Papers), pages 113-117. +Xiaochao Fan, Hongfei Lin, Liang Yang, Yufeng Diao, Chen Shen, Yonghe Chu, and Tongxuan Zhang. 2020. Phonetics and ambiguity comprehension gated attention network for humor recognition. Complex., 2020:2509018:1-2509018:9. +Md. Kamrul Hasan, Wasifur Rahman, AmirAli Bagher Zadeh, Jianyuan Zhong, Md. Iftekhar Tanveer, Louis-Philippe Morency, and Mohammed (Ehsan) Hoque. 2019. UR-FUNNY: A multimodal language dataset for understanding humor. In Proceedings of EMNLP-IJCNLP 2019, pages 2046-2056. +Pengcheng He, Xiaodong Liu, Jianfeng Gao, and Weizhu Chen. 2020. DeBERTa: Decoding-enhanced BERT with disentangled attention. CoRR, abs/2006.03654. +Nabil Hossain, John Krumm, Michael Gamon, and Henry A. Kautz. 2020. SemEval-2020 Task 7: Assessing humor in edited news headlines. In Proceedings of SemEval@COLING 2020, pages 746-758. +Immanuel Kant. 1790. Critique of judgment, ed. and trans. WS Pluhar; Indianapolis: Hackett. +Claudia Leacock and Martin Chodorow. 1998. Combining local context and WordNet sense similarity for word sense identification. In WordNet, An Electronic Lexical Database. The MIT Press. +Lizhen Liu, Donghai Zhang, and Wei Song. 2018. Modeling sentiment association in discourse for humor recognition. In Proceedings of ACL 2018, Volume 2 (Short Papers), pages 586-591. +J.A. Meaney, Steven R. Wilson, Luis Chiruzzo, Adam Lopez, and Walid Magdy. 2021. SemEval 2021 Task 7: HaHackathon, detecting and rating humor and offense. In Proceedings of SemEval@ACL 2021. + +Rada Mihalcea and Carlo Strapparava. 2005. Making computers laugh: Investigations in automatic humor recognition. In Proceedings of HLT/EMNLP 2005, pages 531-538. +Rada Mihalcea, Carlo Strapparava, and Stephen G. Pulman. 2010. Computational models for incongruity detection in humour. In Proceedings of CICling 2010, volume 6008 of Lecture Notes in Computer Science, pages 364-374. +George A. Miller. 1995. Wordnet: A lexical database for English. Commun. ACM, 38(11):39-41. +Tristan Miller and Iryna Gurevych. 2015. Automatic disambiguation of English puns. In Proceedings of ACL 2015, pages 719-729. +Alex Morales and Chengxiang Zhai. 2017. Identifying humor in reviews using background text sources. In Proceedings of EMNLP 2017, pages 492-501. +John Morreall. 2020. Philosophy of Humor. In Edward N. Zalta, editor, The Stanford Encyclopedia of Philosophy, fall 2020 edition. Metaphysics Research Lab, Stanford University. +Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. GloVe: Global vectors for word representation. In Proceedings of EMNLP 2014, pages 1532-1543. +Roy Rada, Hafedh Mili, Ellen Bicknell, and Maria Blettner. 1989. Development and application of a metric on semantic nets. IEEE Trans. Syst. Man Cybern., 19(1):17-30. +Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. Language models are unsupervised multitask learners. OpenAI blog, 1(8):9. +Victor Raskin. 1979. Semantic mechanisms of humor. In Annual Meeting of the Berkeley Linguistics Society, volume 5, pages 325-335. +Arthur Schopenhauer. 1883. The world as will and idea (vols. i, ii, & iii). Haldane, RB, & Kemp, J.(3 Vols.). London: Kegan Paul, Trench, Trubner, 6. +Dafna Shahaf, Eric Horvitz, and Robert Mankoff. 2015. Inside jokes: Identifying humorous cartoon captions. In Proceedings of SIGKDD 2015, pages 1065-1074. +Orion Weller and Kevin D. Seppi. 2019. Humor detection: A transformer gets the last laugh. In Proceedings of EMNLP-IJCNLP 2019, pages 3619-3623. +Zhibiao Wu and Martha Palmer. 1994. Verbs semantics and lexical selection. In Proceedings of ACL 1994, page 133-138. +Yubo Xie, Junze Li, and Pearl Pu. 2021. HumorHunter at SemEval-2021 Task 7: Humor and offense recognition with disentangled attention. In Proceedings of SemEval@ACL 2021. + +
Running Time
Simlch1.76 sec
Simwup1.71 sec
Simpath1.71 sec
Alliteration1.70 sec
Ambiguity2.94 sec
Uncertainty2.12 sec
Surprisal2.49 sec
Uncertainty + Surprisal2.26 sec
+ +Table 3: Running time of the SVM classifiers trained on individual features. + +
Running Time
GloVe7.54 sec
GloVe + Simlch14.85 sec
GloVe + Simwup15.90 sec
GloVe + Simpath13.76 sec
GloVe + Alliteration15.41 sec
GloVe + Ambiguity14.28 sec
GloVe + Uncertainty14.70 sec
GloVe + Surprisal13.84 sec
GloVe + U + S19.27 sec
+ +Table 4: Running time of the content-based SVM classifiers combined with individual features. U denotes uncertainty and S denotes surprisal. + +Diyi Yang, Alon Lavie, Chris Dyer, and Eduard H. Hovy. 2015. Humor recognition and humor anchor extraction. In Proceedings of EMNLP 2015, pages 2367-2376. + +Dongyu Zhang, Heting Zhang, Xikai Liu, Hongfei Lin, and Feng Xia. 2019. Telling the whole story: A manually annotated chinese dataset for the analysis of humor in jokes. In Proceedings of EMNLP-IJCNLP 2019, pages 6401-6406. + +# A Model Parameters + +For the SVM classifier, we set the regularization parameter $C = 1.0$ , and used the RBF kernel with the kernel coefficient $\gamma = 1 / n_{\mathrm{features}}$ . All models were trained and evaluated on a machine with Intel Core i7-6700K CPU, Nvidia GeForce GTX 1080 GPU, and 16GB RAM. The running time of each method is listed in Table 3 and Table 4. \ No newline at end of file diff --git a/uncertaintyandsurprisaljointlydeliverthepunchlineexploitingincongruitybasedfeaturesforhumorrecognition/images.zip b/uncertaintyandsurprisaljointlydeliverthepunchlineexploitingincongruitybasedfeaturesforhumorrecognition/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..f9b45386e4834c6cb922183340988352282c1736 --- /dev/null +++ b/uncertaintyandsurprisaljointlydeliverthepunchlineexploitingincongruitybasedfeaturesforhumorrecognition/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d35ac6784f3b5613bfd49cec46157cdeaafe200937acf990473d357268dc897c +size 237373 diff --git a/uncertaintyandsurprisaljointlydeliverthepunchlineexploitingincongruitybasedfeaturesforhumorrecognition/layout.json b/uncertaintyandsurprisaljointlydeliverthepunchlineexploitingincongruitybasedfeaturesforhumorrecognition/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..6638c056d09c4982acfa5fa0d96a6841b8484e5d --- /dev/null +++ b/uncertaintyandsurprisaljointlydeliverthepunchlineexploitingincongruitybasedfeaturesforhumorrecognition/layout.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e576989cf2ebc2dac3b71bc3d0e1b7d788ab8df63bfb3e5b48f8c756c29e42a7 +size 199307 diff --git a/unsupervisedcrossdomainprerequisitechainlearningusingvariationalgraphautoencoders/71e6e818-0c73-47ef-a521-c67a075769f5_content_list.json b/unsupervisedcrossdomainprerequisitechainlearningusingvariationalgraphautoencoders/71e6e818-0c73-47ef-a521-c67a075769f5_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..0e7368b4e0707e1e3a190464393b4199f7bbd1dd --- /dev/null +++ b/unsupervisedcrossdomainprerequisitechainlearningusingvariationalgraphautoencoders/71e6e818-0c73-47ef-a521-c67a075769f5_content_list.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2bfdd9a26d43385b60af36fc92f19170f3ad916c97c893686e93265e7782b240 +size 44564 diff --git a/unsupervisedcrossdomainprerequisitechainlearningusingvariationalgraphautoencoders/71e6e818-0c73-47ef-a521-c67a075769f5_model.json b/unsupervisedcrossdomainprerequisitechainlearningusingvariationalgraphautoencoders/71e6e818-0c73-47ef-a521-c67a075769f5_model.json new file mode 100644 index 0000000000000000000000000000000000000000..c6028105740b6d74296bb2369b09e6f1070d1faa --- /dev/null +++ b/unsupervisedcrossdomainprerequisitechainlearningusingvariationalgraphautoencoders/71e6e818-0c73-47ef-a521-c67a075769f5_model.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4e38aeb642257c052c44b30abf3b1764a57e8fe852e5f199e1d219d67320276a +size 52137 diff --git a/unsupervisedcrossdomainprerequisitechainlearningusingvariationalgraphautoencoders/71e6e818-0c73-47ef-a521-c67a075769f5_origin.pdf b/unsupervisedcrossdomainprerequisitechainlearningusingvariationalgraphautoencoders/71e6e818-0c73-47ef-a521-c67a075769f5_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..c433356f79739ec38c2bd3449a8bde4e244df9fa --- /dev/null +++ b/unsupervisedcrossdomainprerequisitechainlearningusingvariationalgraphautoencoders/71e6e818-0c73-47ef-a521-c67a075769f5_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2e9b5f6452d7ad02a7e360563d704beb81de43db1e5a58c4850079fb5f4a36b4 +size 562868 diff --git a/unsupervisedcrossdomainprerequisitechainlearningusingvariationalgraphautoencoders/full.md b/unsupervisedcrossdomainprerequisitechainlearningusingvariationalgraphautoencoders/full.md new file mode 100644 index 0000000000000000000000000000000000000000..535816161f7ddf1b546ca75c8ced16ce45ac4515 --- /dev/null +++ b/unsupervisedcrossdomainprerequisitechainlearningusingvariationalgraphautoencoders/full.md @@ -0,0 +1,161 @@ +# Unsupervised Cross-Domain Prerequisite Chain Learning using Variational Graph Autoencoders + +Irene Li, Vanessa Yan, Tianxiao Li, Rihao Qu and Dragomir Radev + +Yale University, USA + +{irene.li,vanessa.yan,tianxiao.li,rihao.qu,dragomir.radev} $@$ yale.edu + +# Abstract + +Learning prerequisite chains is an essential task for efficiently acquiring knowledge in both known and unknown domains. For example, one may be an expert in the natural language processing (NLP) domain but want to determine the best order to learn new concepts in an unfamiliar Computer Vision domain (CV). Both domains share some common concepts, such as machine learning basics and deep learning models. In this paper, we propose unsupervised cross-domain concept prerequisite chain learning using an optimized variational graph autoencoder. Our model learns to transfer concept prerequisite relations from an information-rich domain (source domain) to an information-poor domain (target domain), substantially surpassing other baseline models. Also, we expand an existing dataset by introducing two new domains—CV and Bioinformatics (BIO). The annotated data and resources, as well as the code, will be made publicly available. + +# 1 Introduction + +With the rapid growth of online educational resources in diverse fields, people need an efficient way to acquire new knowledge. Building a concept graph can help people design a correct and efficient study path (ALSaad et al., 2018; Yu et al., 2020). There are mainly two approaches to learning prerequisite relations between concepts: one is to extract the relations directly from course content, video sequences, textbooks, or Wikipedia articles (Yang et al., 2015b; Pan et al., 2017; Alzetta et al., 2019), but this approach requires extra work on feature engineering and keyword extraction. Our method follows a different approach of inferring the relations within a concept graph (Liang et al., 2018; Li et al., 2019, 2020). + +In a concept graph, we define $p \to q$ as the notion that learning concept $p$ is a prerequisite to learning concept $q$ . Existing methods formulate + +![](images/73ec83804efbb4b4aaf4b72db583c864c3bed75795e3301312513574df8ce2be.jpg) +Figure 1: Cross-domain prerequisite chains. + +this question as a classification task. A typical method is to encode concept pairs and train a classifier to predict if there is a prerequisite relation (Alzetta et al., 2019; Yu et al., 2020). However, this method requires annotated prerequisite pairs during training. Alternatively, others have used graph-based models to predict prerequisite relations. Gordon et al. (2016) proposed information-theoretic approaches to infer concept dependencies. Li et al. (2019) modeled a concept graph using Variational Graph Autoencoders (VGAE) (Kipf and Welling, 2016), training their model to infer unseen prerequisite relations in a semi-supervised way. While most of the previous methods were supervised or semi-supervised, Li et al. (2020) introduced RelationalVGAE, which enabled unsupervised learning on prerequisite relations. + +Existing work mainly focuses on prerequisite relations within a single domain. In this paper, we tackle the task of cross-domain prerequisite chain learning, by transferring prerequisite relations between concepts from a relatively information-rich domain (source domain) to an information-poor domain (target domain). As an example, we illustrate in Figure 1, a partial concept graph from the Natural Language Processing (NLP) domain and a partial concept graph from the Computer Vision (CV) domain. Prerequisite relations among concepts in the NLP domain are known, and we seek to infer prerequisite relations among concepts in + +the CV domain. These two domains share some concepts, such as Convolutional Neural Network. We assume that being aware of prerequisite relations among concepts in the source domain helps infer potential relations in the target domain. More specifically, in the figure, knowing that Convolutional Neural Network $\rightarrow$ Document Classification helps us determine that Convolutional Neural Network $\rightarrow$ Image Classification. + +Our contributions are two-fold. First, we propose cross-domain variational graph autoencoders to perform unsupervised prerequisite chain learning in a heterogeneous graph. Our model is the first to do domain transfer within a single graph, to the best of our knowledge. Second, we extend an existing dataset by collecting and annotating resources and concepts in two new target domains. Data and code will be made public in https://github.com/Yale-LILY/LectureBank/tree/master/LectureBankCD. + +# 2 Dataset + +LectureBank2.0 (Li et al., 2020) dataset contains 1,717 lecture slides (hereon called resources) and 322 concepts with annotated prerequisite relations, largely from NLP. We treat this dataset as our information-rich source domain (NLP). Also, we propose an expansion dataset, LectureBankCD, by introducing two new target domains in the same data format: CV and Bioinformatics (BIO). We report statistics on the dataset in Table 1. For each domain, we identify high-quality lecture slides from the top university courses, collected by domain experts, and we choose concepts by crowd-sourcing. We end up with 201 CV concepts and 100 BIO concepts. In each domain, we ask two graduate-level annotators with deep domain knowledge to add prerequisite chain annotations for every possible pair of concepts. The Cohen's kappa agreement scores (McHugh, 2012) are 0.6396 for CV and 0.8038 for BIO. Cohen's kappa between 0.61-0.80 is considered substantial, so our annotations are reliable. + +
DomainFilesPagesTks/pgCon.PosRel
NLP1,71765,028473221,551
CV1,04158,3243201871
BIO1487,13135100234
+ +Table 1: LectureBankCD statistics on NLP, CV and BIO domain: Tks/pg (Tokens per slide page), Con. (Number of concepts), PosRel (Positive Relations). + +![](images/468a05f4bbf9a78cf843d9578b40ea2138978ddae4a886e8837e2037327b8d4b.jpg) +Figure 2: Cross-Domain Concept-Resource Graph: we model the resource nodes (solid nodes) and concept nodes (hollow nodes) from two domains (in blue and orange) in a heterogeneous graph. We show a subset of nodes and edges. + +We take the union of the positive annotations for our experiments: 871 positive relations for CV and 234 positive relations for BIO. + +# 3 Methodology + +Inspired by Li et al. (2020), we build a cross-domain concept-resource graph $G = (X, A)$ that includes resource nodes and concept nodes from both the source and target domains (Figure 2). To obtain the node feature matrix $X$ , we use either BERT (Devlin et al., 2019) or Phrase2Vec (Artetxe et al., 2018) embeddings. We consider four edge types to build the adjacency matrix $A$ : $A^{c,s}$ : edges between source concept nodes; $A^{rc}$ : edges between all resource nodes and concept nodes; $A^r$ : edges between resource nodes only; and $A^{c,t}$ : edges between target concept nodes. In unsupervised prerequisite chain learning, $A^{c,s}$ ——concept relations of the source domain—are known, and the task is to predict $A^{c,t}$ ——concept relations of the target domain. For $A^{rc}$ and $A^r$ , we calculate cosine similarities based on node embeddings, consistent with previous works (Li et al., 2019; Chiu et al., 2020). + +Cross-Domain Graph Encoder VGAE (Kipf and Welling, 2016) contains a graph neural network (GCN) encoder (Kipf and Welling, 2017) and an inner product decoder. In a GCN, the hidden representation of a node $i$ in the next layer is computed using only the information of direct neighbours and the node itself. To account for cross-domain knowledge, we additionally consider the domain neighbours for each node $i$ . These domain neighbours are a set of common or semantically similar + +concepts from the other domain. We define the cross-domain graph encoder as: + +$$ +\begin{array}{l} h _ {i} ^ {(l + 1)} = \sigma \left(\sum_ {j \in N _ {i}} W ^ {(l)} h _ {j} ^ {(l)} + W ^ {(l)} h _ {i} ^ {(l)} \right. \\ \left. + \sum_ {k \in N _ {i} ^ {D}} W _ {D} ^ {(l)} h _ {k} ^ {(l)}\right) \\ \end{array} +$$ + +where $N_{i}$ denotes the set of direct neighbours of node $i$ , $N_{i}^{D}$ is the set of domain neighbours, and $W_{D}$ and $W$ are trainable weight matrices. To determine the domain neighbors, we compute cosine similarities and match the concept nodes only from source domain to target domain: $\cosine(h_s, h_t)$ . The values are then normalized into the range of [0,1], and we keep the top $10\%$ of domain neighbors. + +DistMult Decoder We optimize the original inner product decoder from VGAE. To predict the link between a concept pair $(c_i, c_j)$ , we apply the DistMult (Yang et al., 2015a) method: we take the output node features from the last layer, $\hat{X}$ , and define the following score function to recover the adjacency matrix $\hat{A}$ by learning a trainable weight matrix $R$ : $\hat{A} = \hat{X} R \hat{X}$ . A Sigmoid function is used to predict positive/negative labels from $\hat{A}$ . + +# 4 Evaluation + +We evaluate on our new corpus LectureBankCD, treating the NLP domain as the source domain and transferring to the two new target domains: NLP $\rightarrow$ CV and NLP $\rightarrow$ BIO. Consistent with Kipf and Welling (2017); Li et al. (2019), we randomly split the positive relations into $85\%$ training, $5\%$ validation, and $10\%$ testing. To account for imbalanced data, we randomly select negative relations such that the training set has the same number of positive and negative relations. We do the same for the validation and test sets. We report average scores over five different randomly seeded splits. + +To encode concepts and resources, we test BERT and P2V embeddings. For BERT, we applied a pretrained version from Google3. We trained P2V using all the resource data. Both methods only require free-text for training and encoding. + +Baseline Models We concatenate the BERT/P2V embeddings of each pair of con + +cepts and feed the result into a classifier (CLS + BERT and CLS + P2V). We train the classifier on the source domain only, then evaluate on the target domain. We report the best performance among Support Vector Machine, Logistic Regression, Gaussian Naïve Bayes, and Random Forest. In addition, we train the VGAE model Li et al. (2019) on the source domain and test on the target domain, initializing the VGAE input with BERT and P2V embeddings separately (VGAE + BERT and VGAE + P2V). Given that GAE is structurally similar to VGAE, we leave this for future work. Other graph-based methods including DeepWalk (Perozzi et al., 2014) and Node2vec (Grover and Leskovec, 2016) are not applicable in this setting as both models require training edges from the target domain in order to generate node embeddings for target concepts. + +Proposed Method We report results of our proposed model, CD-VGAE, initialized with BERT and P2V node embeddings separately. Consistent with the work from Li et al. (2019) and Li et al. (2020), P2V embeddings yield better results than BERT embeddings in general. One possible reason for this difference is that BERT embeddings have a large number of dimensions, making it very easy to overfit. The two CLS models yield a negative result, with F1 worse than random guess. A possible reason is that treating concept pairs independently from the source domain may not be beneficial for the target domains. The VGAE models have a better performance when considering the concepts in a large graph. As shown in the table, our method performs better than the chosen baselines on both accuracy and F1 score, by incorporating information from domain neighbors. In particular, it yields much higher recall than all the baseline models. We provide further analysis in a later section. + +Upper Bound Performance Finally, we conduct in-domain experiments on CV and BIO (supervised training and testing in the target domain), to show an upper bound for cross-domain performance. We test a variety of methods including traditional classifiers as well as graph-based approaches, including DeepWalk, Node2vec, and GraphSAGE (Hamilton et al., 2017). + +# 5 Analysis + +Next, we conduct quantitative analysis and case studies on the target domain concept graphs recovered by our model (CD-VGAE+P2V) and two + +
MethodNLP→CVNLP→BIO
F1AccPreRecF1AccPreRec
Baseline Models
CLS + BERT0.42770.54800.57430.34190.39300.60000.74810.2727
CLS + P2V0.48810.57570.61060.40700.22220.53330.60000.1364
VGAE + BERT (Li et al., 2019)0.58850.54770.53980.64880.60110.60910.61850.5909
VGAE + P2V (Li et al., 2019)0.62020.55000.53680.73490.61770.62730.65210.6091
Proposed Method
CD-VGAE + BERT0.63910.55930.54410.78840.62890.62730.64250.6364
CD-VGAE + P2V0.67540.57590.54680.88370.65120.65910.66670.6364
Supervised Performance - Upper Bound
CLS + Node2vec (Grover and Leskovec, 2016)0.81720.81970.82230.81400.80600.79560.75470.8727
+ +Table 2: Evaluation results on two target domains. Underlined scores are the best among the baseline models. + +![](images/985c620defce6b7759e33af9f670bbf677025bd976a8b856508f6cd9301684b7.jpg) +Figure 3: Case Study in BIO: direct neighbors of BLAST, including successors and prerequisites, from the ground truth, VGAE, and our proposed CD-VGAE model. SVD stands for Singular Value Decomposition. Correct nodes are marked in blue, incorrect nodes are marked in red. (Best viewed in color!) + +![](images/71d48ee74abb1a1417c68b0bec4fb49aeb8b89251961ac929dec8af28b7382ce.jpg) + +![](images/6584f964c3d17a951c80ff35c72576a0e23712d78fd5c6750960dd527b86d4fe.jpg) + +baseline models (CLS + P2V, VGAE + P2V), to take a closer look at the results. + +Quantitative Analysis We first apply the three trained models to recover the concept graph in the CV domain. Compared to the ground truth with 871 positive relations, the baseline model predicts 527, VGAE predicts 963, and our model predicts 1,209. Similarly, in the BIO domain with 234 positive relations, the baseline model predicts only 128 positive edges, VGAE predicts 261, and our model predicts 303. Since our model tends to predict more positive edges, it has a higher recall. A higher recall is preferred in real-world applications as a system should not miss any relevant concepts when designing a user's study path. + +Concept Graph Recovery We now provide case studies of the recovered concept graphs. In Table 3, we show successors of the concept Image Processing from the CV domain, i.e. concepts for which Image Processing is a prerequisite. Both the baseline model and VGAE miss many successor concepts, whereas our model can recover a correct list without any missing concepts. + +We illustrate another case study from the BIO domain in Figure 3 using the concept BLAST (short for "basic local alignment search tool"), an algorithm for comparing primary biological sequence information. In the ground truth, BLAST has + +three prerequisite concepts (Dynamic Programming, DNA and Sequence Alignment), and one successor concept (Homology Model). We observe that VGAE predicts only one prerequisite, DNA, and misses all the others. In contrast, our model successfully includes all the ground truth relations, although it predicts some extra ones compared to VGAE. A closer look at the extra predictions reveals that these are still relevant topics, even though they are not direct prerequisites. For example, Sequence Alignment, BLAST and Graph Theory are all associated with sequence analysis and share some common algorithms (i.e. De Bruijn Graph). + +We provide a case study in the CV domain, shown in Figure 4, by selecting concept node Object Localization. The ground truth shows that it has 14 direct neighbors. The VGAE model only predicts five neighbors, while our model predicts more. Our model has two wrong predictions, but it gets 12 correct ones. In contrast, the VGAE model misses up to 10 neighbors, which is not acceptable in an application scenario of an educational platform leading students to miss very useful information. + +# 6 Conclusion + +In this paper, we proposed the CD-VGAE model to solve the task of cross-domain prerequisite chain + +![](images/c4f6fb641e4a6143dc0cbf8459cfa5d4b814fc2f0446c5d50da6fb993d42aba9.jpg) +Figure 4: Case Study in CV: direct neighbors of Object Localization. + +![](images/33baf33a74d77614bf42d898ab1c5834db6d2fe3464f4fff77dfadb259d5c246.jpg) + +![](images/7c0740937a8fef82ce52059819649ecc67f37099585b1854026e3b410c053b4d.jpg) + +
BaseVGAE
Image RepresentationOCRImage RepresentationComputer graphicsEye Tracking
CD-VGAEGround Truth
Video/Image augmentationVideo/Image augmentation
Image RepresentationFace DetectionImage RepresentationFace detection
Emotion RecognitionEmotion Recognition
Feature ExtractionFeature Extraction
Feature LearningOCRFeature LearningOCR
Computer GraphicsEye TrackingComputer GraphicsEye Tracking
+ +Table 3: Successors of the concept Image Processing, i.e. concepts for which Image Processing is a prerequisite (OCR stands for Optical Character Recognition). + +learning. Results show that our model outperforms previous unsupervised graph-based models by a large margin, especially with respect to the F1 and recall scores. In addition, we created a new dataset that contains resources and concepts from two domains along with annotated prerequisite relations. + +# References + +Fareedah ALSaad, Assma Boughoula, Chase Geigle, Hari Sundaram, and ChengXiang Zhai. 2018. Mining mooc lecture transcripts to construct concept dependency graphs. International Educational Data Mining Society. +Chiara Alzetta, Alessio Miaschi, Giovanni Adorni, Felice Dell'Orletta, Frosina Koceva, Samuele Passalacqua, and Ilaria Torre. 2019. Prerequisite or not prerequisite? that's the problem! an nlp-based approach for concept prerequisite learning. In CLiC-it. +Mikel Artetxe, Gorka Labaka, and Eneko Agirre. 2018. Unsupervised statistical machine translation. In Proceedings of the 2018 Conference on Empirical Meth + +ods in Natural Language Processing, pages 3632-3642, Brussels, Belgium. Association for Computational Linguistics. +Billy Chiu, Sunil Kumar Sahu, Derek Thomas, Neha Sengupta, and Mohammady Mahdy. 2020. Autoencoding keyword correlation graph for document clustering. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3974-3981, Online. Association for Computational Linguistics. +Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171-4186, Minneapolis, Minnesota. Association for Computational Linguistics. +Jonathan Gordon, Linhong Zhu, Aram Galstyan, Prem Natarajan, and Gully Burns. 2016. Modeling concept dependencies in a scientific corpus. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 866-875, Berlin, Germany. Association for Computational Linguistics. +Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, August 13-17, 2016, pages 855-864. ACM. +William L. Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA, pages 1024-1034. + +Thomas N Kipf and Max Welling. 2016. Variational graph auto-encoders. arXiv preprint arXiv:1611.07308. +Thomas N. Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In 5th International Conference on Learning Representations, ICLR 2017, Toulouse, France, April 24-26, 2017, Conference Track Proceedings. OpenReview.net. +Irene Li, Alexander Fabbri, Swapnil Hingmire, and Dragomir Radev. 2020. R-VGAE: Relational variational graph autoencoder for unsupervised prerequisite chain learning. In Proceedings of the 28th International Conference on Computational Linguistics, pages 1147-1157, Barcelona, Spain (Online). International Committee on Computational Linguistics. +Irene Li, Alexander R. Fabbri, Robert R. Tung, and Dragomir R. Radev. 2019. What should I learn first: Introducing lecturebank for NLP education and prerequisite chain learning. In The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference, IAAI 2019, The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, Hawaii, USA, January 27 - February 1, 2019, pages 6674-6681. AAAI Press. +Chen Liang, Jianbo Ye, Shuting Wang, Bart Pursel, and C. Lee Giles. 2018. Investigating active learning for concept prerequisite learning. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018, pages 7913-7919. AAAI Press. +Mary L McHugh. 2012. Interrater reliability: the kappa statistic. Biochemia medica, 22(3):276-282. +Liangming Pan, Chengjiang Li, Juanzi Li, and Jie Tang. 2017. Prerequisite relation learning for concepts in MOOCs. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1447-1456, Vancouver, Canada. Association for Computational Linguistics. +Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014. Deepwalk: online learning of social representations. In The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '14, New York, NY, USA - August 24 - 27, 2014, pages 701-710. ACM. +Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng. 2015a. Embedding entities and relations for learning and inference in knowledge bases. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings. + +Yiming Yang, Hanxiao Liu, Jaime G. Carbonell, and Wanli Ma. 2015b. Concept graph learning from educational data. In Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, WSDM 2015, Shanghai, China, February 2-6, 2015, pages 159-168. ACM. +Jifan Yu, Gan Luo, Tong Xiao, Qingyang Zhong, Yuquan Wang, Wenzheng Feng, Junyi Luo, Chenyu Wang, Lei Hou, Juanzi Li, Zhiyuan Liu, and Jie Tang. 2020. MOOCCube: A large-scale data repository for NLP applications in MOOCs. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3135-3142, Online. Association for Computational Linguistics. + +# A Supervised Results + +
MethodAccF1PreRec
GS+BERT0.74910.75130.74040.7628
GS+P2V0.74570.74230.74860.7372
CLS+P2V0.76420.7570.77540.7395
CLS+BERT0.75720.74950.76770.7326
DeepWalk0.79880.7910.81820.7674
Node2vec0.81970.81720.82230.8140
+ +Table 4: Supervised evaluation results: CV→CV. GS:GraphSAGE. + +
MethodAccF1PreRec
GS+BERT0.72890.73550.71040.7727
GS+P2V0.79110.79040.77870.8091
CLS+P2V0.720.73670.68740.8091
CLS+BERT0.70670.71890.6830.7727
DeepWalk0.79110.80790.73340.9091
Node2vec0.79560.80600.75470.8727
+ +Table 5: Supervised evaluation results: BIO→BIO. GS:GraphSAGE. + +As a supplementary experiment, we present in-domain results in Table 4, 5: $\mathrm{CV} \rightarrow \mathrm{CV}$ and $\mathrm{BIO} \rightarrow \mathrm{BIO}$ respectively. While we show in the main paper that $\mathrm{CLS} + \mathrm{Node2vec}$ yields the best result, which serves as an upper bound on cross-domain performance, we additionally show our experimental results for other supervised methods: + +CLS + P2V/BERT We encode concept pairs with P2V/BERT, concatenate the embeddings of both concepts within each possible pair, and then train a binary classifier. We report the best performance among Support Vector Machine, Logistic Regression, Gaussian Naïve Bayes, and Random Forest. + +DeepWalk, Node2vec DeepWalk (Perozzi et al., 2014) randomly samples a node and traverses to a neighbor node until it reaches a maximum length, updating the latent representation of each node after each "walk" to maximize the probability of each node's neighbors given a node's representation. Node2Vec (Grover and Leskovec, 2016) improves DeepWalk by providing the additional flexibility of placing weights on random walks. For both methods, we input the training prerequisite relations and obtain concept node embeddings. After generating embeddings for each concept in the target domain, we concatenate the embeddings of both concepts in each concept pair and pass the concatenated representation into a classifier to predict + +the relation. Again, we report the best performance from the same four classifiers. + +GraphSAGE + P2V/BERT GraphSAGE (Hamilton et al., 2017) is an inductive framework to generate node embeddings for unseen data by leveraging existing node features. We first treat it as a node embedding method, as done with DeepWalk and Node2vec. After generating concept node embeddings, we train a classifier to predict concept relations and report in-domain results. In addition, we investigate GraphSAGE for the out-of-domain setting. We assume that, because there are unseen topics when transferring to new domains, such an inductive method like GraphSAGE may fit in our scenario. However, we end up with negative results as the original GraphSAGE may not fit in to this specific application. We leave further investigation for future work. \ No newline at end of file diff --git a/unsupervisedcrossdomainprerequisitechainlearningusingvariationalgraphautoencoders/images.zip b/unsupervisedcrossdomainprerequisitechainlearningusingvariationalgraphautoencoders/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..342fca566b6e084f81abd138c6a6957eb3afbdaf --- /dev/null +++ b/unsupervisedcrossdomainprerequisitechainlearningusingvariationalgraphautoencoders/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4a19c5848e335d14d052a7f77065f1915a3edcffa4a65dac0d119d911d58a746 +size 352592 diff --git a/unsupervisedcrossdomainprerequisitechainlearningusingvariationalgraphautoencoders/layout.json b/unsupervisedcrossdomainprerequisitechainlearningusingvariationalgraphautoencoders/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..c16664bb89263d138455f63444e6edcbfef452df --- /dev/null +++ b/unsupervisedcrossdomainprerequisitechainlearningusingvariationalgraphautoencoders/layout.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:684485ec7eb1347380727bed0ad07a4d8aebda49210d4ccf5df2b097bbce87c1 +size 196101 diff --git a/unsupervisedenrichmentofpersonagroundeddialogwithbackgroundstories/e116f3d7-0f6b-4ce7-9f74-26b6f9f6a552_content_list.json b/unsupervisedenrichmentofpersonagroundeddialogwithbackgroundstories/e116f3d7-0f6b-4ce7-9f74-26b6f9f6a552_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..0c49daea62fa50cf274f429be07e6dd48e00d412 --- /dev/null +++ b/unsupervisedenrichmentofpersonagroundeddialogwithbackgroundstories/e116f3d7-0f6b-4ce7-9f74-26b6f9f6a552_content_list.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5566f274f92fe87d9dec74a6b760b72a43c0e27ed0bbe58ec48f9ed64ae07753 +size 55358 diff --git a/unsupervisedenrichmentofpersonagroundeddialogwithbackgroundstories/e116f3d7-0f6b-4ce7-9f74-26b6f9f6a552_model.json b/unsupervisedenrichmentofpersonagroundeddialogwithbackgroundstories/e116f3d7-0f6b-4ce7-9f74-26b6f9f6a552_model.json new file mode 100644 index 0000000000000000000000000000000000000000..e36a3fae080158aa70c4ef61c4348b716f0a5323 --- /dev/null +++ b/unsupervisedenrichmentofpersonagroundeddialogwithbackgroundstories/e116f3d7-0f6b-4ce7-9f74-26b6f9f6a552_model.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:dd277f043c7a791eebe59355bc41d379db9849f83d0e5edc53b4bb666af72c23 +size 68353 diff --git a/unsupervisedenrichmentofpersonagroundeddialogwithbackgroundstories/e116f3d7-0f6b-4ce7-9f74-26b6f9f6a552_origin.pdf b/unsupervisedenrichmentofpersonagroundeddialogwithbackgroundstories/e116f3d7-0f6b-4ce7-9f74-26b6f9f6a552_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..0335f0922d6c07fc00f210febf0ed683cedd3c74 --- /dev/null +++ b/unsupervisedenrichmentofpersonagroundeddialogwithbackgroundstories/e116f3d7-0f6b-4ce7-9f74-26b6f9f6a552_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d1b9eb6560c426832bf7a4f832b8c89533a443f3d199e8a5ac80b943a4a10786 +size 998961 diff --git a/unsupervisedenrichmentofpersonagroundeddialogwithbackgroundstories/full.md b/unsupervisedenrichmentofpersonagroundeddialogwithbackgroundstories/full.md new file mode 100644 index 0000000000000000000000000000000000000000..f3e57caefb3907fa1b7436f9941d93a6ebe9e2ca --- /dev/null +++ b/unsupervisedenrichmentofpersonagroundeddialogwithbackgroundstories/full.md @@ -0,0 +1,254 @@ +# Unsupervised Enrichment of Persona-grounded Dialog with Background Stories + +Bodhisattwa Prasad Majumder\* Taylor Berg-Kirkpatrick\* Julian McAuley\* Harsh Jhamtani + +\*Department of Computer Science and Engineering, UC San Diego {bmajumde, tberg, jmcauley}@eng.ucsd.edu School of Computer Science, Carnegie Mellon University jharsh@cs.cmu.edu + +# Abstract + +Humans often refer to personal narratives, life experiences, and events to make a conversation more engaging and rich. While personag-. grounded dialog models are able to generate responses that follow a given persona, they often miss out on stating detailed experiences or events related to a persona, often leaving conversations shallow and dull. In this work, we equip dialog models with 'background stories' related to a persona by leveraging fictional narratives from existing story datasets (e.g. ROCStories). Since current dialog datasets do not contain such narratives as responses, we perform an unsupervised adaptation of a retrieved story for generating a dialog response using a gradient-based rewriting technique. Our proposed method encourages the generated response to be fluent (i.e., highly likely) with the dialog history, minimally different from the retrieved story to preserve event ordering and consistent with the original persona. We demonstrate that our method can generate responses that are more diverse, and are rated more engaging and human-like by human evaluators, compared to outputs from existing dialog models. + +# 1 Introduction + +Humans often rely on specific incidents and experiences while conversing in social contexts (Dunbar et al., 1997). Responses from existing chitchat dialog agents often lack such specific details. To mitigate this, some prior work has looked into assigning personas to dialog agents (Zhang et al., 2018; Majumder et al., 2020). However, persona descriptions are often shallow and limited in scope, and while they lead to improvements response specificity, they still lack the level of detail with which humans share experiences. + +In this work, we propose methods to enrich dialog personas with relevant background events us + +![](images/5a46ad57c6058c2c28dc61e3782db64e321ab97ed587cd2afeb22de9561628fa.jpg) +Figure 1: We enrich agent personas with 'background stories' from an existing corpus. We propose a gradient-based technique which encourages the generated response to be fluent with the dialog history, minimally different from the retrieved story, and consistent with the persona. The proposed approach leads to more specific and interesting responses. + +ing fictional narratives from existing story datasets such as ROCStories (Mostafazadeh et al., 2016). For example, for a persona attribute 'I have two children and a dog,' we are able to identify a relevant narrative from a story corpus (Figure 1). However, such stories may not directly fit fluently in the dialog context. Thus, retrieved stories should be adapted to construct a response that is fluent and relevant to the context. Since existing datasets (such as PersonaChat (Zhang et al., 2018)) do not contain responses with such background stories, such adaptation has to be done in an unsupervised fashion with decoders trained to generate responses conditioned only on a dialog history and persona. + +To adapt a retrieved narrative incident as a relevant background story, we use a decoding procedure which encourages the generated response to (1) be fluent with the dialog history, (2) be consistent with the original persona, and (3) be minimally different from the retrieved story. While fluency with dialog context is encouraged directly by the likelihood as per the underlying language model + +the remaining two constraints are incorporated via iterative updates to the decoder output distributions at inference time. Our inference-time decoding method is different from the only recent effort by Su et al. (2020) that leverages non-dialog data (forum comments, book snippets) as distant labels to train dialog systems with supervision. Our contributions can be summarized as follows: + +- We propose a novel approach to enrich dialog agent personas with relevant backstories, relying only on existing story datasets. +- We propose to use an unsupervised backpropagation based decoding procedure1 to adapt the relevant stories such that the resulting response is fluent with the dialog history and consistent with the dialog agent persona. Our method works with a model trained just with dialog data i.e. without access to story corpus at training time. +- Our experiments demonstrate that the proposed approach results in much more engaging and specific dialog outputs in a persona-grounded dialog setup. This fills a gap in existing dialog models which often lack the capability to generate responses about specific events and experiences relevant to persona attributes. + +# 2 Unsupervised Persona Enrichment with Background Stories + +Given dialog history $h$ and persona $C$ consisting of several (typically 3-5, example shown in Figure 1) attributes, our goal is to construct a dialog response $x$ . Our underlying model is based on the discrete persona attribute choice model from Majumder et al. (2020). To generate a dialog utterance $x$ , we first sample a persona attribute $c \sim p(c|h)$ conditioned on the dialog history $h$ . $x$ is then generated conditioned on the dialog history and the chosen persona attribute. The underlying dialog model's decoder is initialized with a pretrained GPT-2 model, and is fine-tuned on the PersonaChat dataset (Zhang et al., 2018). However, in our current setup, we also have to identify relevant background stories and use them to construct fluent responses at decoding time. Therefore, we propose a different decoding procedure. + +To generate a response, we first sample a persona attribute $c \sim p(c|h)$ . Next we retrieve stories cor + +responding to the persona attribute $c$ (Section 2.1). However, the underlying dialog model is trained to generate responses conditioned only on the dialog history and persona. To incorporate the retrieved story in the response, we perform gradient-based inference (Section 2.2), that only assumes a left-to-right language model trained on dialog context and responses, and the story is handled at decoding time in an unsupervised fashion. We refer to the proposed method as PABST (Unsupervised PersonA enrichment with Background Stories). + +# 2.1 Retrieving Relevant Stories + +For a persona attribute $c$ , we aim to identify relevant stories from a story corpus. Toward this goal, we rank the stories using the F1 component of BERT-score (Zhang et al., 2020) based retrieval using the persona attribute $c$ as the query and the highest scoring story is chosen. Note that many of the stories are written in the third person. For use as background stories, we must first transform them to first-person. Following prior work (Brahman and Chaturvedi, 2020), we identify the protagonist of such stories as the most frequently occurring character. Thereafter, we use co-reference resolution (Lee et al., 2017) to identify all words or phrases that refer to the protagonist. Finally, all words or phrases so identified are replaced with suitable first person pronouns (e.g. 'his books' to 'my books'). + +# 2.2 Gradient-based Inference + +Our underlying dialog model is not trained to condition on a retrieved story, and cannot be directly used to construct a desirable response using $s$ . To tackle this, we consider a decoding strategy which, in addition to fluency with history $h$ , encourages response $x$ to follow two soft constraints: (1) be minimally different from story $s$ , and (2) be consistent with persona $c$ . + +First, we generate an initial response based only on the dialog history. Then we perform an iterative procedure which alternates between performing a forward pass on the language model to encourage fluency, and a backward pass which updates the response via back-propagation to respect the two soft constraints. However, $x$ is discrete, and cannot be directly updated using gradients from backpropagation. Instead, we maintain and update a soft representation $o$ of $x$ , where $o_i$ corresponds to the last hidden state representation for the $i^{th}$ token position, i.e., $p(x_i) \sim \mathrm{softmax}(W o_i / \tau)$ , where $\tau$ is the temperature parameter, $W$ is the embedding + +matrix, and $W o_{i} \in \mathcal{R}^{V}$ ( $V$ is the vocabulary size). Our approach is inspired by recent works that use gradient-based decoding for text generation with soft constraints (Dathathri et al., 2020; Qin et al., 2020). Next we describe the backward and forward passes of the iterative procedure. + +# Backward Pass with Soft Constraints We define the following soft constraints on response $x$ : + +(1) Divergence from story: We want to encourage $x$ to be minimally different from the story $s$ . Following prior work (Qin et al., 2020), we compute a cross entropy loss (denoted by cross-entr henceforth) with story $s = \{s_1, \ldots, s_T\}$ tokens as labels and $W o_{1}, \ldots, W o_{T}$ as the logits. +(2) Consistency to persona: We want $x$ to be consistent with persona attribute $c$ . Consider a classifier $q_{\phi}(o, c)$ which predicts the probability of $x$ (or rather the soft representation $o$ of $x$ ) entailing $c$ . The classifier $q_{\phi}(o, c)$ is a bag-of-words classification head on decoder hidden states $o$ , fine-tuned on the Dialogue-NLI dataset (Welleck et al., 2019) to predict whether pairs of persona attributes and responses are entailed or not. The objective to maximize can be written as: + +$$ +\mathcal {L} (c, s; o) = \lambda_ {c} \log q _ {\phi} (o, c) - \lambda_ {d} \text {c r o s s - e n t r} (s, W o) +$$ + +where $\lambda_{c}$ and $\lambda_{d}$ are hyper-parameters. We update $o$ through back-propagation by computing the gradient $\nabla_{o}\mathcal{L}(c,s;o)$ , while keeping the model parameters constant. Let the resulting $o$ after the gradient-based updates be denoted by $o^b$ . + +Forward Pass to Encourage Fluency Next we perform a forward pass of the underlying dialog model, with the goal of regularizing the hidden states towards the unmodified language model values. On computing the forward pass at the $j^{th}$ token, we mix the final hidden states $o_j^f$ from the forward pass with $o_j^b$ computed in the backward pass, via weighted addition to get the resulting $o_j = \gamma \times o_j^f + (1 - \gamma) \times o_j^b$ , where $\gamma \in (0,1)$ is a hyperparameter. The resulting $o_j$ is used for computing the logits at the next time step $j + 1$ . + +We initialize the output response by performing greedy decoding from the underlying dialog model, conditioned on the dialog history and persona attribute. Then we iteratively update $o$ by alternate backward and forward passes. We sample the final response $x \sim \mathrm{softmax}(W_o / \tau)$ . In practice, we found that 5 iterations are sufficient to generate good quality outputs. + +
MethodTrainingDecodingD-1D-2ENTR
W/o Story Data
TRANSFEROPERSONANucleus0.050.111.21
DISCCHOICEPERSONANucleus0.150.251.25
DISCCHOICECS-KBNucleus0.871.072.04
With Story Data
DISCCHOICEPSEUDONucleus0.912.452.89
DISCCHOICEMULTITASKNucleus0.992.542.71
DISCCHOICEPERSONARETRIEVAL2.569.673.86
PABST (Ours)PERSONAGrad. Inf.1.563.573.21
+ +Table 1: Diversity metrics on the PersonaChat test set. D-1/2 is the $\%$ of distinct uni- and bi-grams. ENTR is the geometric mean of n-gram entropy. Grad. Inf. is the unsupervised gradient-based decoding as opposed to Nucleus sampling (Holtzman et al., 2020). + +# 3 Experiments + +We evaluate methods in terms of their capability to generate diverse, fluent and engaging responses. Hyperparameters are noted in Appendix $\S A$ . + +Datasets We experiment with the PersonaChat dialog dataset (Zhang et al., 2018) consisting of 131,438 utterances for training, 15,602 for validation, and 15,024 for testing. For stories, we use the training split of the ROCStories dataset (Mostafazadeh et al., 2016), that consists of 78,529 stories, each typically of 4 to 5 sentences. + +Baselines We consider two broad groups of models as baselines: (1) Without access to story corpus: We use finetuned GPT2 (TRANSFERO) on PersonaChat, and the discrete persona attribute choice model (DISCCHOICE) from Majumder et al. (2020). We also consider a version of DISCCHOICE which enriches personas with inferences from a commonsense knowledge base (CS-KB). (2) Baselines using story corpus: To allow DISCCHOICE models to generate story-like responses, we adapt an alternative training regime (PSEUDO) from (Su et al., 2020), where we randomly replace some of the target dialog responses with retrieved stories—treating them as pseudo labels. Finally, we also consider a MULTITASK training setup from (Su et al., 2020), wherein the decoder is trained on PersonaChat as well as with a language modeling objective on ROCStories. We additionally consider a RETRIEVAL baseline that uses the retrieved story verbatim as the dialog response. + +# 3.1 Automatic Evaluation + +We hypothesize that the proposed approach to leverage external non-dialog data can increase the diversity of the generated responses. Following + +
PABST vs.TRANSFERODISCCHOICERETRIEVALPSEUDOMULTITASKw/o DNLIGold
Aspectwinlosswinlosswinlosswinlosswinlosswinlosswinloss
Sensible83.85.671.410.172.715.794.33.192.81.581.76.829.133.6
Engaging96.71.882.66.787.25.394.72.194.12.670.914.870.215.6
+ +Table 2: Pairwise comparison (% win/loss cases, ties not reported) between responses from PABST and from other baselines as well as gold. All differences are significant with $p < 0.05$ . Cohen's Kappa (Cohen, 1960) for sensibility and engagement were 0.79 and 0.82 respectively. Complete details of the human evaluations are in Appendix §B. + +
Dialog History +Agent: How was your last trip? +User: It was great. I went to Utah and saw the Canyonlands.
Retrieved Story: +I went on a trip. I went to Florida. When we got there, there was no air conditioning. I was irritated. It was too hot.
DISCCHOICE: That's great. You must be adventurous.
RETRIEVAL: I went on a trip. I went to Florida. When we got there, there was no air conditioning. I was irritated. +It was too hot.
PSEUDO: I have a house in Florida.
MULTITASK: I like Florida. I went to the beaches.
PABST (λd=5): I went on a trip. I went to Florida. I have a house there. We do not have air condition. It was hot.
PABST (λd=1): That's great. I frequently go to Florida. I have a house there but it gets hot in summer.
+ +
Dialog History +Agent: I work as stunt double. What about you? +User: I'm an engineer. I just got back from work. How's yours?
Retrieved Story: +I was a working actor. For my new role I was a stunt double. I started working on new stunts. When I jumped off a building yesterday I got hurt. I had to be rushed to the hospital.
DISCCHOICE: That's great. I am looking for a job.
RETRIEVAL: I was a working actor. For my new role I was a stunt double. I started working on new stunts. When I jumped off a building yesterday I got hurt. I had to be rushed to the hospital.
PSEUDO: I got a new job. I am an actor.
MULTITASK: I started a new role. I got hurt.
PABST (λd=5): I was working as a stunt double. I jumped off a building. I got hurt. I had to be rushed to the hospital.
PABST (λd=1): Yesterday while I was working, I jumped off a building and I got hurt. I had to be taken to the hospital.
+ +Table 3: Generations from different models. More examples are in Appendix §C. + +prior work (Li et al., 2016), we report the percentage of distinct uni-grams and bi-grams (D-1 and D-2 respectively). Note that these values do not capture the actual frequency distribution of different word types. Therefore, we also report the geometric mean of entropy values of empirical frequency distributions of n-grams of words $(n\in \{1,2,3\})$ (Jhamtani et al., 2018), denoted by ENTR. + +We observe that methods that use story data show much higher diversity compared to methods that do not (Table 1). Among methods using story data, gradient-based decoding (PABST) performs better than DISCCHOICE trained with PSEUDO or MULTITASK. Note that just using RETRIEVAL outputs as-is leads to even more diverse outputs than PABST. However, they are much less sensible with the context, as shown in human evaluations. + +# 3.2 Human Evaluation + +Since we do not have ground truth story-like responses in the dialog dataset, we perform human evaluation with 150 test examples to investigate if PABST generates responses that are 1) sensible with the dialog history and 2) engaging. We hired two Anglophone (Lifetime HIT acceptance $\% > 85$ ) annotators for every test sample. The order of the systems present in the interface is randomized. + +A snapshot of the human evaluation interface is provided in Appendix §C. All differences in values from human evaluations are significant with $p < 0.05$ from bootstrap tests on 1000 subsets of size 50. Cohen's Kappa (Cohen, 1960) to measure inter-annotator agreement for sensibility and engagement were 0.79 and 0.82 respectively. + +From the results (shown in Table 3), we note that in comparison to responses from baselines, responses from PABST are more engaging and more sensible with respect to the dialog history. We further make following observations. Firstly, using the gradient-based decoding approach with retrieved stories (PABST) works significantly better than using distant supervision with stories data (PSEUDO and MULTITASK). Secondly, background stories provide sufficient detail for an engaging conversation compared to DIS-CCHOICE which expands persona attributes using commonsense knowledge (Majumder et al., 2020). Finally, we also observe that PABST performs worse when we do not use the consistency constraint (w/o DNLI). + +Choice of $\lambda_{d}$ We also experiment with different values of the weight for the divergence term $(\lambda_d)$ in $\mathcal{L}$ : High $(\lambda_d = 5)$ , Moderate $(\lambda_{d} = 1)$ , + +and Low $(\lambda_d = 0.05)$ . We consider 100 samples for this experiment. We attribute a high $\lambda_{d}$ to responses strictly copying the story. We find that PABST (moderate $\lambda_{d}$ ) wins wins $81.2\%$ and $69.1\%$ cases against PABST (high $\lambda_{d}$ ) on 'sensible' and 'engaging' response criteria respectively. Similarly, PABST (moderate $\lambda_{d}$ ) wins $93.2\%$ and $84.7\%$ cases against PABST (low $\lambda_{d}$ ) in terms of sensibility and engagement respectively. + +Qualitative Analysis Table 3 shows responses generated by different baselines. We observe that PABST is able to follow the retrieved story (same as output from RETRIEVAL) while modifying the response to be conversation-like and sensible with dialog history. Responses from other baselines remain verbose or incoherent. Mirroring the human evaluation, we observe that choosing a higher $\lambda_{d}$ makes the model to almost repeat the retrieved story but a lower value smooths the output to make it more sensible with the ongoing dialog. + +# 4 Related Work + +A desired impact of the proposed approach is increase in diversity of the generated responses. To tackle the issue of diversity in dialog model outputs, prior work has focused on decoding strategies such as diversity-promoting sampling (Holtzman et al., 2020); training strategies such as discouraging undesirable responses via unlikelihood training (Li et al., 2020); model changes such as using stochastic variables (Serban et al., 2017); and using external data such as forum data (Su et al., 2020) or external knowledge bases (Majumder et al., 2020). In contrast to these, our proposed method generates responses with background stories using a gradient-based decoding approach. + +One of the steps in our proposed approach is to retrieve relevant stories from an external corpus. Prior work has explored using retrieval of similar dialog instances as an initial step in improving response diversity and other human-like desiderata in dialog (Roller et al., 2020; Weston et al., 2018). Distant supervision by using retrieved text snippets as pseudo responses has been explored in prior work (Su et al., 2020; Roller et al., 2020). We use an external data source to improve dialog responses, a theme shared with some efforts in other tasks such as machine translation (Khandelwal et al.). The use of narrative text in dialog has been explored in prior work, mostly as a 'script' or template for conversation (Xu et al., 2020; Zhu et al., 2020). + +We adapted a BERT-based retrieval method (Zhang et al., 2020) in our case to retrieve relevant story given dialog context and use retrieved story in the decoding phase. + +Gradient-based for text generation with soft constraints has been explored in prior work (Dathathri et al., 2020; Qin et al., 2020). Song et al. (2020) focused on generating response which are consistent to given persona. Differently, we use a gradient-based decoding to generate a dialog response while honoring constraints such as consistency to persona and similarity to retrieved story. + +# 5 Conclusion + +We propose a method to enrich persona-grounded dialog with background stories at the inference time only using an existing corpus of non-conversational narratives—opening up new ways to generate enriched and engaging responses. One of the limitations of PABST is the assumption of the background story at every turn. As future work, we can include a decision step to decide if we need to incorporate a background story or not, given the dialog history. We can further explore ways to use retrieved stories over multiple turns instead of a single turn. + +# Acknowledgements + +We thank anonymous reviewers for providing valuable feedback. BPM is partly supported by a Qualcomm Innovation Fellowship and NSF Award #1750063. Findings and observations are of the authors only and do not necessarily reflect the views of the funding agencies. + +# Impact Statement + +In this work, we discuss ways to make a dialog system to generate more engaging responses. Since we use a finetuned version of a pretrained generative model, we inherit the general risk of generating biased or toxic language, which should be carefully filtered. Furthermore, the generations may incorporate biases that are already present in the dialog dataset and story dataset due to crowd-sourced data collection. Hence, we cautiously advise any developer who wishes to use a different story dataset for the background stories to be aware of the biases present in the dataset. Finally, we also note that experiments in this paper are limited only to English language. + +# References + +Faeze Brahman and Snigdha Chaturvedi. 2020. Modeling protagonist emotions for emotion-aware storytelling. In EMNLP, pages 5277-5294. +Jacob Cohen. 1960. A coefficient of agreement for nominal scales. Educational and psychological measurement, 20(1):37-46. +Sumanth Dathathri, Andrea Madotto, Janice Lan, Jane Hung, Eric Frank, Piero Molino, Jason Yosinski, and Rosanne Liu. 2020. Plug and play language models: A simple approach to controlled text generation. In ICLR. +Robin IM Dunbar, Anna Marriott, and Neil DC Duncan. 1997. Human conversational behavior. Human nature, 8(3):231-246. +Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2020. The curious case of neural text degeneration. In ICLR. +Harsh Jhamtani, Varun Gangal, Eduard Hovy, Graham Neubig, and Taylor Berg-Kirkpatrick. 2018. Learning to generate move-by-move commentary for chess games from large-scale social forum data. In ACL 2018. +Urvashi Khandelwal, Angela Fan, Dan Jurafsky, Luke Zettlemoyer, and Mike Lewis. Nearest neighbor machine translation. CoRR. +Kenton Lee, Luheng He, Mike Lewis, and Luke Zettlemoyer. 2017. End-to-end neural coreference resolution. In EMNLP. +Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and Bill Dolan. 2016. A diversity-promoting objective function for neural conversation models. In *NAACL HLT*. +Margaret Li, Stephen Roller, Ilia Kulikov, Sean Welleck, Y-Lan Boureau, Kyunghyun Cho, and Jason Weston. 2020. Don't say that! making inconsistent dialogue unlikely with unlikelihood training. In ACL. +Bodhisattwa Prasad Majumder, Harsh Jhamtani, Taylor Berg-Kirkpatrick, and Julian J. McAuley. 2020. Like hiking? you probably enjoy nature: Personagrounded dialog with commonsense expansions. In EMNLP. +Nasrin Mostafazadeh, Nathanael Chambers, Xiaodong He, Devi Parikh, Dhruv Batra, Lucy Vanderwende, Pushmeet Kohli, and James F. Allen. 2016. A corpus and evaluation framework for deeper understanding of commonsense stories. CoRR, abs/1604.01696. +Lianhui Qin, Vered Shwartz, Peter West, Chandra Bhagavatula, Jena D. Hwang, Ronan Le Bras, Antoine Bosselut, and Yejin Choi. 2020. Back to the future: Unsupervised backprop-based decoding for counterfactual and abductive commonsense reasoning. In EMNLP. + +Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M Smith, et al. 2020. Recipes for building an open-domain chatbot. arXiv preprint arXiv:2004.13637. +Iulian Vlad Serban, Alessandro Sordoni, Ryan Lowe, Laurent Charlin, Joelle Pineau, Aaron C. Courville, and Yoshua Bengio. 2017. A hierarchical latent variable encoder-decoder model for generating dialogues. In AAAI. +Haoyu Song, Wei-Nan Zhang, Jingwen Hu, and Ting Liu. 2020. Generating persona consistent dialogues by exploiting natural language inference. In AAAI. +Hui Su, Xiaoyu Shen, Sanqiang Zhao, Xiao Zhou, Pengwei Hu, Randy Zhong, Cheng Niu, and Jie Zhou. 2020. Diversifying dialogue generation with non-conversational text. In ACL. +Sean Welleck, Jason Weston, Arthur Szlam, and Kyunghyun Cho. 2019. Dialogue natural language inference. In ACL. +Jason Weston, Emily Dinan, and Alexander H. Miller. 2018. Retrieve and refine: Improved sequence generation models for dialogue. In SCAI@EMNLP. +Thomas Wolf, Victor Sanh, Julien Chaumond, and Clement Delangue. 2019. Transfertransfo: A transfer learning approach for neural network based conversational agents. CoRR, abs/1901.08149. +Jun Xu, Zeyang Lei, Haifeng Wang, Zheng-Yu Niu, Hua Wu, and Wanxiang Che. 2020. Enhancing dialog coherence with event graph grounded content planning. In *IJCAI*. +Saizheng Zhang, Emily Dinan, Jack Urbanek, Arthur Szlam, Douwe Kiela, and Jason Weston. 2018. Personalizing dialogue agents: I have a dog, do you have pets too? In ACL. +Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, and Yoav Artzi. 2020. Bertscore: Evaluating text generation with BERT. In ICLR. +Yutao Zhu, Ruihua Song, Zhicheng Dou, Jian-Yun Nie, and Jin Zhou. 2020. Scriptwriter: Narrative-guided script generation. In ACL. + +# A Implementation Details + +We obtain the PersonaChat dataset from ParlAI repository2. ROCStories dataset is obtained from the repository of original release3. We adapted codes from original PPLM (Dathathri et al., 2020) repository4 and modified them for our own objective function. + +Network architecture For the generator network, we use GPT2 (Transformer with 12 layers, 768 hidden size, 12 heads—gpt2-small $^5$ ) following the state-of-the-art model (Wolf et al., 2019) from Conv-AI2 competition. The decoder has total of 124 Million parameters. We used the pre-trained decoder model obtained from (Majumder et al., 2020). + +Hyperparameters PABST does not require any training since we perform gradient-based decoding at the inference time. For our best method, in objective function $\mathcal{L}$ , we use $\lambda_{d}$ as 1 and $\lambda_{c}$ as 1. We keep generation length to be 100 to encourage longer generations. We train the consistency classifier using code from PPLM repository6. The weight $\gamma$ for mixing forward and backward passes was set to 0.45. For PSEUDO, we substitute a random $30\%$ of the original target responses with retrieved stories. + +# B Human Evaluations Setup + +Figure 2 shows a sample question for the pairwise comparison between response generated by PABST and a baseline for sensibility and engagingness. A similar setup was used to measure performance between PABST variants with different $\lambda_{d}$ values (0.5, 1, 5). + +# C Qualitative Examples + +Tables 4 and 5 show generations from PABST and baselines for sampled text examples. + +```txt +$^{2}$ http://parl.ai/downloads/personachat/personachat.tgz + $^{3}$ https://www.cs.ROchester.edu/nlp/rocstories/ + $^{4}$ https://github.com/uber-research/PPLM + $^{5}$ https://github.com/huggingface/transfer-learning-conv-ai + $^{6}$ https://github.com/uber-research/PPLM/blob/master/run_pplm_discrim_train.py +``` + +# Dialog History + +Agent: I volunteer in shelters. + +User: It is a noble act. + +DISCCHOICE: Thank you. I like helping people. + +RETRIEVAL: I was at my house bored. I couldn't figure out what to do. I looked on TV for answers. I saw a commercial about homeless people. So I decided to volunteer at a homeless shelter. + +PSEUDO: Last week, I went to the local animal shelter on the pet adaptation day. + +MULTITASK: Thank you! What about you? + +PABST ( $\lambda_d = 5.0$ ): I was bored. I wanted to help people. I looked on TV about homeless people. So I decided to volunteer at a homeless shelter. + +PABST $(\lambda_d = 1.0)$ : Thank you. I was inspired by a TV commercial and I volunteered at the homeless shelter. + +Table 4: Generations from different models + +# Dialog History + +Agent: I enjoy American sports. What sports do you like? +User: I love playing volleyball. I was in the university team. + +DISCCHOICE: That's great. I like adventure sports. + +RETRIEVAL: I always thought golf was for old people. One day, my dad invited me to play golf with him. I reluctantly accepted his invitation. On the golf range, I saw lots of people of my age. I realized golf was a sport for everyone to enjoy regardless of age. + +PSEUDO: Last week, I went to play golf. My dad invited me. I was reluctant. + +MULTITASK: That's great! I play volleyball during weekend. + +PABST ( $\lambda_d = 5.0$ ): I like playing golf. I always thought it is for old people. Recently I went to a golf range. But I saw lots of people of my age. I realized golf is a sports for everyone. + +PABST ( $\lambda_d = 1.0$ ): That's great. I like playing golf. I always thought it is for old people. Recently I went to a golf range. But I saw lots of people of my age. I realized golf is a sports for everyone. + +Table 5: Generations from different models + +# Instructions (Click to collapse) + +This task requires basic English language understanding. + +For each instance, you will have to read the dialog history between two people A and B. We expect you to respond on the following for the candidates shown for A's response: + +1) Sensibile: Which response do you think is more sensible with the dialog history? +2) Engaging: Which response do you think is more engaging/interesting? + +# 1. Dialog History: + +A's turn: How was your last trip? +B's turn: It was great. I went to Utah and saw the Canyonlands. + +Candidates for A's next turn: + +Response R1: That's great. I frequently go to Florida. I have a house there but it gets hot in summer. + +Response R2: I have a house in Florida. + +1.1 Which response do you think is more sensible with the dialog history? +$\bigcirc \mathbb{R}1$ is better Both have similar fluency $\bigcirc \mathbb{R}1$ is worse +1.2 Which response do you think is more engaging/interesting? +$\bigcirc$ R1 is more engaging $\bigcirc$ Both have similar engagement level $\bigcirc$ R1 is less engaging + +Figure 2: Human evaluation setup for pairwise comparison between PABST and another baseline \ No newline at end of file diff --git a/unsupervisedenrichmentofpersonagroundeddialogwithbackgroundstories/images.zip b/unsupervisedenrichmentofpersonagroundeddialogwithbackgroundstories/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..1ca88ace97c915c1d8ab093b02ebeb9640a105dd --- /dev/null +++ b/unsupervisedenrichmentofpersonagroundeddialogwithbackgroundstories/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b33d77f57829f545525ea2c425326d8ae949262cab7eb9e139808b5c1da0c1c7 +size 286736 diff --git a/unsupervisedenrichmentofpersonagroundeddialogwithbackgroundstories/layout.json b/unsupervisedenrichmentofpersonagroundeddialogwithbackgroundstories/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..2d9d5388d5a6bf1b4c33545a4d48987df4cfeb7f --- /dev/null +++ b/unsupervisedenrichmentofpersonagroundeddialogwithbackgroundstories/layout.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ac6bbf93f5c66f58976f89a5645166d5cb5504596baeaa4e969afd4bcef9bfab +size 305249 diff --git a/unsupervisedpronounresolutionviamaskednounphraseprediction/ca8c97e3-0780-438a-a1d8-5a7900e88ffe_content_list.json b/unsupervisedpronounresolutionviamaskednounphraseprediction/ca8c97e3-0780-438a-a1d8-5a7900e88ffe_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..8c7cf46dfd92c1c3d6833f2882d3b91c4f831653 --- /dev/null +++ b/unsupervisedpronounresolutionviamaskednounphraseprediction/ca8c97e3-0780-438a-a1d8-5a7900e88ffe_content_list.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:12dc53ae0464c3f65b1bb4fa77cada66d1fbc32f0e4d83e7a69b2821483f6a4d +size 60541 diff --git a/unsupervisedpronounresolutionviamaskednounphraseprediction/ca8c97e3-0780-438a-a1d8-5a7900e88ffe_model.json b/unsupervisedpronounresolutionviamaskednounphraseprediction/ca8c97e3-0780-438a-a1d8-5a7900e88ffe_model.json new file mode 100644 index 0000000000000000000000000000000000000000..2a7f30618e0b96d92ee9f886813823de31920ea1 --- /dev/null +++ b/unsupervisedpronounresolutionviamaskednounphraseprediction/ca8c97e3-0780-438a-a1d8-5a7900e88ffe_model.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:dffae3370314d0fae6c8eb57022545397baada19c11480ff296c11e311e68e3e +size 75741 diff --git a/unsupervisedpronounresolutionviamaskednounphraseprediction/ca8c97e3-0780-438a-a1d8-5a7900e88ffe_origin.pdf b/unsupervisedpronounresolutionviamaskednounphraseprediction/ca8c97e3-0780-438a-a1d8-5a7900e88ffe_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..dfcc49fab80fa2725e61745d8bd65528cd0cf082 --- /dev/null +++ b/unsupervisedpronounresolutionviamaskednounphraseprediction/ca8c97e3-0780-438a-a1d8-5a7900e88ffe_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ab42e89d81e6542bf9e0b500e4e1e05c67d610246c7ae7220b45cc5155ac31b9 +size 392016 diff --git a/unsupervisedpronounresolutionviamaskednounphraseprediction/full.md b/unsupervisedpronounresolutionviamaskednounphraseprediction/full.md new file mode 100644 index 0000000000000000000000000000000000000000..841ab3b711f67fb9c3ddf9405b66f239af5810e1 --- /dev/null +++ b/unsupervisedpronounresolutionviamaskednounphraseprediction/full.md @@ -0,0 +1,201 @@ +# Unsupervised Pronoun Resolution via Masked Noun-Phrase Prediction + +Ming Shen* Pratyay Banerjee* Chitta Baral + +Arizona State University + +mshen16, pbanerj6, chitta@asu.edu + +# Abstract + +In this work, we propose Masked Noun-Phrase Prediction (MNPP), a pre-training strategy to tackle pronoun resolution in a fully unsupervised setting. Firstly, We evaluate our pretrained model on various pronoun resolution datasets without any finetuning. Our method outperforms all previous unsupervised methods on all datasets by large margins. Secondly, we proceed to a few-shot setting where we finetune our pre-trained model on WinoGrande-S and XS separately. Our method outperforms RoBERTa-large baseline with large margins, meanwhile, achieving a higher AUC score after further finetuning on the remaining three official splits of WinoGrande. + +# 1 Introduction + +Co-reference resolution is an important NLP task that aims to find all expressions that refer to the same entity in a text. The resolution of an ambiguous pronoun, known as pronoun resolution, is a longstanding challenge for the NLU community and an essential step for various high-level NLP tasks such as natural language inference (Bowman et al., 2015; Williams et al., 2018), question answering (Rajpurkar et al., 2016), and relation extraction (Zhang et al., 2017). + +The most successful approach to pronoun resolution is first fine-tuning a large pre-trained language model such as BERT (Devlin et al., 2019) or RoBERTa (Liu et al., 2019) on a human-labeled pronoun resolution dataset such as Definite Pronoun Resolution Dataset (DPR) (Rahman and Ng, 2012) or WinoGrande (WG) (Sakaguchi et al., 2020), and then either directly transferring to a smaller dataset such as Winograd Schema Challenge (WSC) (Levesque et al., 2012) or Pronoun Disambiguation Problems (PDP) (Morgenstern + +
WSC SentencesCandidate Choices
The trophy doesn’t fit in the suitcase because it is too small.A. the trophy B. the suitcase
The trophy doesn’t fit in the suitcase because it is too big.A. the trophy B. the suitcase
+ +Table 1: Above are two WSC examples. A system is required to resolve the bold pronoun "it" to "the suitcase" in the first sentence and to "the trophy" in the second sentence. + +et al., 2016) or further finetuning on a downstream dataset such as SuperGLUE-WSC (Wang et al., 2019a). However, all the pipelines above can not avoid the phase of pre-training on a large human-labeled pronoun resolution dataset. Crowd-sourced "unbiased" labels that do not introduce annotation-artifacts (Gururangan et al., 2018) are shown to be costly and challenging to collect, requiring a well-designed annotation interface and dedicated annotators. To this end, we propose the unsupervised Masked Noun-Phrase Prediction task to pre-train a language model without any pronoun resolution training signal and directly transfer the pre-trained model to downstream datasets such as WSC. Two examples of WSC are listed in Table 1. Our work improves on all previous unsupervised methods by large margins and even outperforms several strong supervised methods on all datasets we study. + +We then proceed to the few-shot setting where we finetune our best zero-shot model on WinoGrande-S and XS respectively. MNPP gives a large margin of improvements over strong baselines including CSS (Klein and Nabi, 2020), RoBERTa-large (Sakaguchi et al., 2020), and UnifiedQA-BART-large (Khashabi et al., 2020). We further finetune on the remaining three data splits and achieve a higher AUC score on all five splits of WinoGrande over RoBERTa-large baseline. + +In summary, our main contributions in this work are threefold. + +- First, we propose the MNPP pre-training task and study how different synthetic dataset properties affect zero-shot performances. +- Second, we show MNPP outperforms all previous fully unsupervised methods and even several strong supervised baselines on all pronoun resolution datasets we study. +- Finally, we show that under few-shot settings, MNPP pre-training gives a significant performance boost on WinoGrande-S and XS and furthermore achieves a higher AUC score over all five splits of WinoGrande. + +# 2 Related Works + +In this work, we mainly compare with unsupervised methods. On WSC, Zhang and Song (2018) propose the first unsupervised model where they modify Skip-Gram (Mikolov et al., 2013) objective to predict semantic dependencies then use this additional information during testing. Wang et al. (2019b) propose Unsupervised Deep Structured Semantic Models (UDSSM), which utilizes BiLSTM (Hochreiter and Schmidhuber, 1997) to compute contextual word embedding and uses models ensemble. Klein and Nabi (2019) directly explore the inner attention layers of BERT. Ye et al. (2019) adapt a masking and predicting strategy, called align, mask, and select (AMS), where entities that are connected with ConceptNet (Speer and Havasi, 2012) are masked and the model is required to select from a given list of candidate entities. An ensemble of large pre-trained models is first utilized by Trinh and Le (2018). GPT-2 is directly evaluated on WSC in Radford et al. (2019). Prakash et al. (2019) extend a language model with a knowledge hunting strategy. Kocijan et al. (2019b) and Kocijan et al. (2019a) are the most similar works to us and we will discuss the details in Section 3.1. Most recently, Klein and Nabi (2020) study a contrastive self-supervised learning approach (CSS) for WSC and DPR and also establish the first unsupervised baseline for KnowRef (Emami et al., 2019). On WinoGrande, knowledge hunting (Prakash et al., 2019) and language models ensemble (Sakaguchi et al., 2020) have been studied. + +# 3 Masked Noun-Phrase Prediction + +We treat MNPP as a binary classification task. Given the sentence: "She put the cup on the chair, but he knocked over the chair, and the cup fell," the underlined "the chair" will be masked and a pair of replacement phrases for this masked position is given as {"the cup", "the chair". One of the candidates is the masked phrase, "the chair", and the other candidate is a different phrase in the sentence, "the cup" extracted from "She put the cup on the chair". The constraint we impose is that both the ground-truth noun-phrase and the alternative candidate need to appear before the masked phrase location, which mimics the pronoun resolution task. We sample sentences following the above constraint to create our synthetic datasets for pre-training. + +We convert the sentence into the format of $\{[\mathrm{CLS}]$ first-half option second-half [SEP] where first-half refers to "She put the cup on the chair but he knocked over" and second-half refers to ", and the cup fell". The option is replaced by candidates, "the cup" or "the chair". We compute P(the chair|sentence, $\theta$ ) and P(the cup|sentence, $\theta$ ) and optimize $\theta$ , the parameters of the model, using cross-entropy loss. We use the final layer [CLS] vector from transformer-based language models and pass it through a single layer feed-forward network to calculate the logits. + +# 3.1 Discussion + +The intuition behind MNPP is that given sufficient samples that mimic pronoun resolution task, the model can learn rich knowledge to perform well on human-annotated pronoun resolution datasets. Such idea is also in-line with recent advances in unsupervised QA (Lewis et al., 2019; Li et al., 2020; Banerjee and Baral, 2020; Banerjee et al., 2020, 2021), where synthetic QA datasets are created from unannotated corpora to perform unsupervised pre-training. Strictly speaking, MNPP is even more unsupervised since our synthetic datasets are not created with true pronoun resolution signals, whereas synthetic QA datasets in works cited above contain true question-answer pairs. + +As mentioned in previous Section 2, similar to our work, Kocijan et al. (2019b) studied such pretraining strategy by constructing a synthetic dataset, called MaskedWiki, which is crawled from English Wikipedia. However, our work is significantly different from theirs in the following ways. First, their + +
Dataset \ SourceCNNQUOREFGutenbergKnowledgeTotal
Hybrid Source100,55651,4516,381-158,388
Hybrid Source w/ Knowledge189,37698,84419,42475,993383,637
+ +Table 2: Number of instances from each source of two hybrid-source synthetic datasets in the first group. + +
Synth. Dataset \ DownstreamWinoGrande (AUC)WSCDPRKnowRefCOPA
Hybrid Source (160k)58.08 (0.6961)79.4882.2779.8371.29
Hybrid Source w/ Know. (380k)58.56 (0.6821)78.3983.8879.0473.27
Gutenberg-10k57.93 (-)75.0981.2177.1579.21
Gutenberg-50k57.40 (-)76.1977.8475.1074.26
Gutenberg-100k58.56 (-)72.5375.0074.4075.25
Gutenberg-300k57.38 (-)75.8281.5676.4478.22
Gutenberg-500k59.19 (0.6748)76.5680.5079.1285.51
Gutenberg-Easy (33k)56.43 (-)69.6070.9275.1077.23
Gutenberg-Medium (33k)57.00 (-)75.1080.3278.1779.21
Gutenberg-Hard (33k)57.54 (-)75.8280.6779.9874.36
+ +Table 3: Zero-shot transfer performances (%) on downstream datasets. AUC scores of WinoGrande are calculated after finetuning on all 5 splits of WinoGrande training sets. Difficulty level is decided using cosine similarity between the two candidate word vectors. Hard samples are the top $33\%$ of samples when they are sorted in descending order using similarity score. Easy are bottom $33\%$ , with Medium in-between. + +pipeline requires further finetuning on another pronoun resolution task before transferring to downstream datasets, whereas our method can be directly evaluated on downstream datasets. Second, the size of MaskedWiki is 2.4 millions, which is 15 times the size of our best performing synthetic dataset. Third, we study how different properties of synthetic datasets affect zero-shot performances. Finally, they use a masked token prediction loss, and we model it as a classification task. Kocijan et al. (2019a) also construct another synthetic dataset called WikiCREM following the same masking principle but with only personal names masked. + +# 4 Experiments and Results + +# 4.1 Synthetic Dataset + +We study three properties of synthetic dataset: source style, size, and difficulty level. The sources we choose include various styles of texts, including CNN stories (See et al., 2017), Wikipedia, and PG-19 language modeling benchmark (Rae et al., 2020). We study 3 groups and a total of 10 different synthetic datasets. The first group contains two synthetic datasets collected from all sources with and without knowledge hunting strategy (Prakash et al., 2019). The second group contains five synthetic datasets collected only from PG-19 but with varying sizes from $10\mathrm{k}$ to $500\mathrm{k}$ . The third group contains three synthetic datasets collected from PG-19 but with easy, medium, and hard samples with + +the same size of 33k each. $^{3}$ Datasets' names are listed in the first column of Table 3 and statistics of the first group are described in Table 2. + +# 4.2 Unsupervised Pronoun Resolution + +The downstream datasets we test on are the Wino-Grande test set (17k instances), DPR test set (564 instances), KnowRef test set (12k instances), and COPA validation set (101 instances). Although COPA (Wang et al., 2019a) is a cause and effect identification dataset, Sakaguchi et al. (2020) show that directly transferring from a WinoGrande-finetuned RoBERTa-large model to COPA already achieves a good performance, indicating that finetuning on WinoGrande can serve as a resource for common sense knowledge. We also investigate whether learning through MNPP can serve as a resource for common sense. Note that we also provide evaluation on the GAP dataset (Webster et al., 2018) in Table 5 for reference although the authors of GAP explicitly mention in their paper that they urge the community to not treat GAP as a Winograd-style task but a co-reference resolution task without gold mention provided. + +# 4.2.1 Results + +We report our experiment results in Table 3 and Table 4. Table 3 shows that different downstream + +
WSC (Levesque et al., 2012)
Bi-LSTM-DPR (2018)56.0
BERT_NSP-DPR (2019)71.1
CorefBERTLarge (2020)71.4
BERT-WIKICREM-DPR (2019a)71.8
BERT-MASKEDWIKI-DPR (2019b)72.5
UDSSM-MASKEDWIKI-DPR (2019)75.1
AMS-CSQA-DPR (2019)75.5
RoBERTa-DPR (2020)83.1
CorefRoBERTaLarge (Ye et al., 2020)83.2
RoBERTa-WG (2020)90.1
Modified Skip-Gram (2018)60.3
BERT Inner Attention (2019)60.3
BERT-MASKEDWIKI (2019b)61.9
UDSSM (2019b)62.4
BERT-WIKICREAM (2019a)63.4
Ensemble LMs (2018)63.7
CSS (2020)69.6
GPT-2 (2019)70.7
WSC Know. Hunting (2019)71.1
MNPP (this work)79.5
+ +
WinoGrande (Sakaguchi et al., 2020)AUC
RoBERTa (local context) (2020)50.0-
BERT-DPR (2020)51.0-
BERT (local context) (2020)51.9-
RoBERTa-DPR (2020)58.9-
BERT (2020)64.90.5289
CSS (2020)65.00.6046
UnifiedQA-Bart-large (2020)73.30.6358
CorefRoBERTaLARGE (2020)77.9-
RoBERTa-large (2020)79.10.6641
CorefBERTLARGE (2020)80.8-
TTTTT (2020)84.60.7673
UnifiedQA-T5-11B (2020)89.40.8571
Wino Know. Hunting (2020)49.6-
Ensemble LMs (2020)50.9-
MNPP (this work)59.20.6706
+ +dataset benefits from different property of the synthetic dataset. The hybrid-source synthetic dataset of size 160k outperforms PG-500k by a large margin on both WSC and DPR. It shows that pretraining on text of various styles instead of larger size is probably a better guarantee for better zero-shot performance on WSC and DPR. However, on WinoGrande and KnowRef, text style and dataset size both seem to impact zero-shot performance. On WinoGrande, larger size matters slightly more, whereas on KnowRef, synthetic dataset with various styles of texts gives better performance. On COPA, it is clear that using books as the source and with larger size at the same time is the key, probably because fictional event descriptions describing day-to-day activities in books contain more common sense, whereas CNN or Wikipedia articles contain precise, factual, non-fictional event descriptions. Finally, pre-training on more challenging examples helps on all tasks except COPA. + +
DPR (Rahman and Ng, 2012)
Bi-LSTM (2018)63.0
FeatureEng+Ranking (2012)73.0
BERT-WIKICREM-DPR (2019a)80.0
BERT-DPR (2019a)83.3
BERT-MASKEDWIKI-DPR (2019b)84.8
BERT-WG (2020)84.9
CorefBERTLARGE (Ye et al., 2020)85.1
RoBERTa-DPR (2020)91.7
CorefRoBERTaLARGE (Ye et al., 2020)92.2
RoBERTa-WG (2020)92.5
RoBERTa-WG-DPR (2020)93.1
BERT-WIKICREAM (2019a)67.4
CSS (2020)80.1
MNPP (this work)83.9
KnowRef (Emami et al., 2019)
E2E-CoNLL (2019)60.0
E2E-KnowRef (2019)61.0
BERT (2019)65.0
E2E-KnowRef+CoNLL (2019)65.0
RoBERTa-DPR (2020)84.2
RoBERTa-WG (2020)85.6
CSS (2020)65.5
MNPP (this work)80.0
COPA (Wang et al., 2019a)
RoBERTa-WG (2020)84.4
MNPP (this work)85.5
+ +Table 4: Comparisons of zero-shot transfer performance (\%) among baselines and MNPP. Works highlighted with gray are supervised methods either directly finetuned on downstream datasets or additionally finetuned on another pronoun resolution dataset. Works highlighted with cyan are fully unsupervised methods. Best performances are in bold. We also underline supervised methods that our method outperforms. Note that AUC score for MNPP is obtained after finetuning on all WinoGrande data splits. (Model-A-B stands for model finetuned on A and B sequentially.) + +Compared with previous methods in Table 4, MNPP outperforms all unsupervised methods on all datasets and is comparable with several strong supervised methods. Current best unsupervised methods on WinoGrande is either random guess or below it, however, MNPP outperforms all of them by a margin of at least $8\%$ . Even compared with a supervised baseline where BERT is first finetuned on DPR, our method outperforms it by $8\%$ . On WSC, MNPP also outperforms all SOTA unsupervised methods by more than $8\%$ and outperforms most supervised methods by at least $4\%$ except RoBERTa-large finetuned on another pronoun resolution dataset. On DPR, our method outperforms the SOTA unsupervised baseline over $3\%$ and also achieves only $1\%$ behind the strong supervised baseline that finetunes BERT on MaskedWiki and DPR sequentially or only on WinoGrande. On KnowRef, MNPP outperforms the only unsuper + +
MFBO
BERT(Kocijan et al., 2019a)75.375.11.0075.2
CorefBERTLARGE(Ye et al., 2020)---76.8
BERT-WIKICREM-GAP(Kocijan et al., 2019a)76.478.41.0377.4
CorefRoBERTaLARGE(Ye et al., 2020)---77.8
BERT-WIKICREM-ALL-GAP(Kocijan et al., 2019a)76.779.41.0478.0
BERT-WIKICREM(Kocijan et al., 2019a)60.557.50.9559.0
MNPP (this work)71.375.21.0573.3
+ +Table 5: Performance comparisons among previous works and MNPP on GAP measured in F1. M stands for male, F stands for female, B stands for bias, and O stands for overall. Works highlighted with lightgray are supervised methods and works highlighted with cyan are fully un-supervised methods. + +vised baseline by nearly $15\%$ and achieves only $5\%$ behind SOTA supervised model. Finally, on COPA, we show that MNPP gives models better common sense knowledge than finetuning on WinoGrande. + +Meanwhile, we are not surprised that SOTA supervised methods still outperform unsupervised methods, including ours, considering the supervision itself and huge models with billions of parameters such as T5-11B. + +# 4.3 Few-Shot Pronoun Resolution + +We further proceed to the few-shot setting on WinoGrande-S and XS. We take the top three performance zero-shot models on WinoGrande development set and finetune them on WinoGrande-XS (160 instances) and S (640 instances) separately. After few-shot evaluation, we also finetune on the remaining three data splits, which are WinoGrande-M, L, and XL. Best performances on all 5 data splits are reported in Fig. 1 and AUC scores are reported in thrid column of WinoGrande section in Table 4. + +# 4.3.1 Results + +As indicated in Figure 1, MNPP outperforms CCS, UnifiedQA-BART-large, and RoBERTa-large on WinoGrande-S and XS with a large margin, and more importantly, achieves a higher AUC score as indicated in Table 4. It is clear that MNPP pretraining gives the model crucial additional information in the few-shot setting where only minimal data is available. We also notice that in the AUC column of Table 3, there is a negative correlation between zero-shot performance and AUC score, which means higher zero-shot performance does + +![](images/e8399eac0d2535a404257e01d01cfb77b3e47a31cdd064680470dfebe5f4f0c1.jpg) +Figure 1: Performances $(\%)$ on WinoGrande test set after finetuning on 5 sizes of WinoGrande training set. + +not guarantee better finetuning results. + +Again we need to mention that we are not comparing with SOTA performances from billions-parameters models such as UnifiedQA-T5-11B from Khashabi et al. (2020) or T5-3B from Lin et al. (2020). + +# 5 Conclusion + +In this work, we propose MNPP pre-training to tackle unsupervised pronoun resolution and study how different properties of the synthetic pretraining dataset impact zero-shot performance on downstream datasets. Without finetuning on any pronoun resolution signal, MNPP outperforms all previous fully unsupervised methods on all tasks we study and even several strong supervised baselines. In the few-shot case where we finetune the zero-shot transfer model on WinoGrande-S and XS respectively, our model outperforms baselines by large margins, and further achieves a higher AUC score. + +This work shows the effectiveness of unsupervised task definitions on text-based pronoun-resolution and common sense reasoning tasks. It would be interesting to design such tasks for multimodal common sense reasoning (Zellers et al., 2019; Fang et al., 2020). + +# Acknowledgements + +The authors acknowledge support from the DARPA SAIL-ON program W911NF2020006, ONR award N00014-20-1-2332, and NSF grant 1816039; and thank Yulong Chen for proofreading and the anonymous reviewers for their insightful discussion. + +# References + +Pratyay Banerjee and Chitta Baral. 2020. Self-supervised knowledge triplet learning for zero-shot question answering. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 151-162, Online. Association for Computational Linguistics. +Pratyay Banerjee, Tejas Gokhale, and Chitta Baral. 2021. Self-supervised test-time learning for reading comprehension. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1200-1211, Online. Association for Computational Linguistics. +Pratyay Banerjee, Tejas Gokhale, Yezhou Yang, and Chitta Baral. 2020. Self-supervised vqa: Answering visual questions using images and captions. arXiv preprint arXiv:2012.02356. +Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015. A large annotated corpus for learning natural language inference. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 632-642, Lisbon, Portugal. Association for Computational Linguistics. +Pradeep Dasigi, Nelson F. Liu, Ana Marasovic, Noah A. Smith, and Matt Gardner. 2019. Quoref: A reading comprehension dataset with questions requiring coreferential reasoning. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5925-5932, Hong Kong, China. Association for Computational Linguistics. +Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171-4186, Minneapolis, Minnesota. Association for Computational Linguistics. +Ali Emami, Paul Trichelair, Adam Trischler, Kaheer Suleman, Hannes Schulz, and Jackie Chi Kit Cheung. 2019. The KnowRef coreference corpus: Removing gender and number cues for difficult pronominal anaphora resolution. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3952-3961, Florence, Italy. Association for Computational Linguistics. +Zhiyuan Fang, Tejas Gokhale, Pratyay Banerjee, Chitta Baral, and Yezhou Yang. 2020. Video2Commonsense: Generating commonsense descriptions to enrich video captioning. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages + +840-860, Online. Association for Computational Linguistics. +Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel Bowman, and Noah A. Smith. 2018. Annotation artifacts in natural language inference data. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 107-112, New Orleans, Louisiana. Association for Computational Linguistics. +Pengcheng He, Xiaodong Liu, Weizhu Chen, and Jianfeng Gao. 2019. A hybrid neural network model for commonsense reasoning. arXiv preprint arXiv:1907.11983. +Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation, 9(8):1735-1780. +Daniel Khashabi, Sewon Min, Tushar Khot, Ashish Sabharwal, Oyvind Tafjord, Peter Clark, and Hannaneh Hajishirzi. 2020. UNIFIEDQA: Crossing format boundaries with a single QA system. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1896-1907, Online. Association for Computational Linguistics. +Tushar Khot, Peter Clark, Michal Guerquin, Peter Jansen, and Ashish Sabharwal. 2020. Qasc: A dataset for question answering via sentence composition. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 8082-8090. +Tassilo Klein and Moin Nabi. 2019. Attention is (not) all you need for commonsense reasoning. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4831-4836, Florence, Italy. Association for Computational Linguistics. +Tassilo Klein and Moin Nabi. 2020. Contrastive self-supervised learning for commonsense reasoning. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7517-7523, Online. Association for Computational Linguistics. +Vid Kocijan, Oana-Maria Camburu, Ana-Maria Cretu, Yordan Yordanov, Phil Blunsom, and Thomas Lukasiewicz. 2019a. WikiCREM: A large unsupervised corpus for coreference resolution. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 4303-4312, Hong Kong, China. Association for Computational Linguistics. +Vid Kocijan, Ana-Maria Cretu, Oana-Maria Camburu, Yordan Yordanov, and Thomas Lukasiewicz. 2019b. A surprisingly robust trick for the Winograd schema challenge. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, + +pages 4837-4842, Florence, Italy. Association for Computational Linguistics. +Vid Kocijan, Thomas Lukasiewicz, Ernest Davis, Gary Marcus, and Leora Morgenstern. 2020. A review of winograd schema challenge datasets and approaches. arXiv preprint arXiv:2004.13831. +Guokun Lai, Qizhe Xie, Hanxiao Liu, Yiming Yang, and Eduard Hovy. 2017. RACE: Large-scale ReAding comprehension dataset from examinations. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 785-794, Copenhagen, Denmark. Association for Computational Linguistics. +Kenton Lee, Luheng He, and Luke Zettlemoyer. 2018. Higher-order coreference resolution with coarse-to-fine inference. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 687-692, New Orleans, Louisiana. Association for Computational Linguistics. +Hector Levesque, Ernest Davis, and Leora Morgenstern. 2012. The winograd schema challenge. In Thirteenth International Conference on the Principles of Knowledge Representation and Reasoning. Citeseer. +Patrick Lewis, Ludovic Denoyer, and Sebastian Riedel. 2019. Unsupervised question answering by cloze translation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4896-4910, Florence, Italy. Association for Computational Linguistics. +Zhongli Li, Wenhui Wang, Li Dong, Furu Wei, and Ke Xu. 2020. Harvesting and refining question-answer pairs for unsupervised QA. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6719-6728, Online. Association for Computational Linguistics. +Sheng-Chieh Lin, Jheng-Hong Yang, Rodrigo Nogueira, Ming-Feng Tsai, Chuan-Ju Wang, and Jimmy Lin. 2020. Tttttackling winogrande schemas. arXiv preprint arXiv:2003.08380. +Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692. +Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781. +Leora Morgenstern, Ernest Davis, and Charles L Ortiz. 2016. Planning, executing, and evaluating the winograd schema challenge. AI Magazine, 37(1):50-54. + +Juri Opitz and Anette Frank. 2018. Addressing the Winograd schema challenge as a sequence ranking task. In Proceedings of the First International Workshop on Language Cognition and Computational Models, pages 41-52, Santa Fe, New Mexico, USA. Association for Computational Linguistics. +Sameer Pradhan, Alessandro Moschitti, Nianwen Xue, Olga Uryupina, and Yuchen Zhang. 2012. CoNLL-2012 shared task: Modeling multilingual unrestricted coreference in OntoNotes. In Joint Conference on EMNLP and CoNLL - Shared Task, pages 1-40, Jeju Island, Korea. Association for Computational Linguistics. +Ashok Prakash, Arpit Sharma, Arindam Mitra, and Chitta Baral. 2019. Combining knowledge hunting and neural language models to solve the Winograd schema challenge. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 6110-6119, Florence, Italy. Association for Computational Linguistics. +Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. Language models are unsupervised multitask learners. OpenAI blog, 1(8):9. +Jack W. Rae, Anna Potapenko, Siddhant M. Jayakumar, Chloe Hillier, and Timothy P. Lillicrap. 2020. Compressive transformers for long-range sequence modelling. In International Conference on Learning Representations. +Altaf Rahman and Vincent Ng. 2012. Resolving complex cases of definite pronouns: The Winograd schema challenge. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pages 777-789, Jeju Island, Korea. Association for Computational Linguistics. +Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016. SQuAD: $100,000+$ questions for machine comprehension of text. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 2383-2392, Austin, Texas. Association for Computational Linguistics. +Yu-Ping Ruan, Xiaodan Zhu, Zhen-Hua Ling, Zhan Shi, Quan Liu, and Si Wei. 2019. Exploring unsupervised pretraining and sentence structure modelling for winograd schema challenge. arXiv preprint arXiv:1904.09705. +Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. 2020. Winogrande: An adversarial winograd schema challenge at scale. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 8732-8740. +Abigail See, Peter J. Liu, and Christopher D. Manning. 2017. Get to the point: Summarization with pointer-generator networks. In Proceedings of the 55th Annual Meeting of the Association for Computational + +Linguistics (Volume 1: Long Papers), pages 1073-1083, Vancouver, Canada. Association for Computational Linguistics. +Robert Speer and Catherine Havasi. 2012. Representing general relational knowledge in conceptnet 5. In LREC, pages 3679-3686. +Trieu H Trinh and Quoc V Le. 2018. A simple method for commonsense reasoning. arXiv preprint arXiv:1806.02847. +Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R Bowman. 2019a. Superglue: A stickier benchmark for general-purpose language understanding systems. arXiv preprint arXiv:1905.00537. +Shuohang Wang, Sheng Zhang, Yelong Shen, Xiaodong Liu, Jingjing Liu, Jianfeng Gao, and Jing Jiang. 2019b. Unsupervised deep structured semantic models for commonsense reasoning. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 882-891, Minneapolis, Minnesota. Association for Computational Linguistics. +Kellie Webster, Marta Recasens, Vera Axelrod, and Jason Baldridge. 2018. Mind the GAP: A balanced corpus of gendered ambiguous pronouns. Transactions of the Association for Computational Linguistics, 6:605-617. +Adina Williams, Nikita Nangia, and Samuel Bowman. 2018. A broad-coverage challenge corpus for sentence understanding through inference. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 1112-1122, New Orleans, Louisiana. Association for Computational Linguistics. +Deming Ye, Yankai Lin, Jiaju Du, Zhenghao Liu, Peng Li, Maosong Sun, and Zhiyuan Liu. 2020. Coreferential Reasoning Learning for Language Representation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 7170-7186, Online. Association for Computational Linguistics. +Zhi-Xiu Ye, Qian Chen, Wen Wang, and Zhen-Hua Ling. 2019. Align, mask and select: A simple method for incorporating commonsense knowledge into language representation models. arXiv preprint arXiv:1908.06725. +Rowan Zellers, Yonatan Bisk, Ali Farhadi, and Yejin Choi. 2019. From recognition to cognition: Visual commonsense reasoning. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 6713-6724. + +Hongming Zhang and Yangqiu Song. 2018. A distributed solution for winograd schema challenge. In Proceedings of the 2018 10th International Conference on Machine Learning and Computing, pages 322-326. +Yuhao Zhang, Victor Zhong, Danqi Chen, Gabor Angeli, and Christopher D. Manning. 2017. Position-aware attention and supervised data improve slot filling. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 35-45, Copenhagen, Denmark. Association for Computational Linguistics. + +# A Related Work on Supervised Methods + +WSC & DPR. Opitz and Frank (2018) is the first work to propose transfer learning from another pronoun resolution dataset such as DPR to WSC. He et al. (2019) use a hybrid model of Wang et al. (2019b) and Kocijan et al. (2019b). Ruan et al. (2019) explore BERT's next sentence prediction with finetuning on DPR. Ye et al. (2020) finetune a new language representation model called CorefBERT, which is trained with a novel task to strengthen the co-referential reasoning ability of BERT, on DPR and then test on DPR and WSC. The SOTA supervised performance is provided by Sakaguchi et al. (2020) where they finetune a RoBERTa-large model on WinoGrande or DPR and evaluate on WSC and DPR without and with further finetuning. A detailed review of WSC and WSC related dataset can be found at Kocijan et al. (2020). + +KnowRef. In Emami et al. (2019), an end-to-end neural system (Lee et al., 2018) is trained on CoNLL2012 shared task (Pradhan et al., 2012) and then tested under three settings: directly applying to KnowRef test set, retraining on KnowRef, and retraining on KnowRef plus CoNLL2012. Sakaguchi et al. (2020) transfer a WinoGrande-finetuned RoBERTa-large model and DPR-finetuned RoBERTa-large model to KnowRef test set respectively. + +WinoGrande. The authors of WinoGrande finetune a RoBERTa-large on WinoGrande training set and evaluate on the test set in standard supervised setting, and Lin et al. (2020) finetune a T5-3B model instead. Sakaguchi et al. (2020) also study finetuning BERT and RoBERTa with only local context (only tokens near the pronoun location are available instead of the whole sentence). Ye et al. (2020) finetune WinoGrande using CorefBERT. Klein and Nabi (2020) finetune their unsupervised CSS model. Finally, UnifiedQA (Khashabi et al., 2020), which is pre-trained on eight seed QA datasets spanning four different formats in a unified way, is finetuned on WinoGrande. + +# B Synthetic Datasets Construction + +For the first synthetic dataset in the first group, we choose 5000 stories in CNN stories, a small portion of Gutenberg books, and the whole training set of QUOREF (Dasigi et al., 2019), which is a reading comprehension dataset that requires resolving co + +reference among entities crawled from Wikipedia, and these sources result in the size of 160k. The second synthetic dataset in the first group comprises the same sources as above plus extra knowledge crawled by Google query using the knowledge hunting strategy introduced in Prakash et al. (2019). Following their strategy, we scrap 6531 and 69462 knowledge sentences for WSC and WinoGrande respectively. We relax the filtering process to allow longer sentences than those in the first synthetic dataset and lead to 380k samples in total. We then fix the text style and study the influence of data size on pre-training. We use 2000 books from PG-19 as the source and create five synthetic datasets with size of 500k, 300k, 100k, 50k, and 10k as the second group. We further study how difficulty levels of samples affect the downstream zero-shot performance. We select 100k samples from the PG-19 books described above and evenly split them into three synthetic datasets with low, medium, and high similarity scores between candidate choices as the third group. As a result, we create 3 groups of synthetic datasets with ten synthetic datasets in total. We used $\mathsf{spaCy}^4$ to pre-process raw text, including removing blank spaces, special characters, sentences that are too short or too long, and extracting noun-phrases. + +# C Zero-shot Experiment Details + +Recent study (Khot et al., 2020) has shown that finetuning a RACE-finetuned (Lai et al., 2017) RoBERTa model as a start point is much more stable than directly finetuning a RoBERTa model from scratch, we follow the same strategy to start finetuning a RACE-finetuned RoBERTa-large model on all synthetic datasets. We use Hugging Face Transformers5 as our codebase. We set Adam optimizer with an initial learning rate of $1e - 5$ and epsilon of $1e - 8$ , and without weight decaying for all settings. For a synthetic dataset whose size is larger or equal to $100k$ , we choose the batch size of 32 and train for 20 epochs, otherwise, we choose the batch size of 16 and train for 50 epochs. We checkpoint every X steps, with X in [50,500]. + +# D Few-shot Experiment Details + +We set Adam optimizer with an initial learning rate of $1e - 5$ and epsilon of $1e - 8$ , without weight decaying, and batch size between 16 and 32 for all + +sizes. We finetune 20 epochs for WinoGrande-XL, L, and M, 40 epochs for S, and 160 epochs for XS. We checkpoint every X steps, with X in [50,500]. \ No newline at end of file diff --git a/unsupervisedpronounresolutionviamaskednounphraseprediction/images.zip b/unsupervisedpronounresolutionviamaskednounphraseprediction/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..f4d1dbc79b0626843a57e8844e9bbceb808f2539 --- /dev/null +++ b/unsupervisedpronounresolutionviamaskednounphraseprediction/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:603bc582458db45f07e03dd248891435373e16c6d6bf581fe6dabf832f5d1044 +size 384142 diff --git a/unsupervisedpronounresolutionviamaskednounphraseprediction/layout.json b/unsupervisedpronounresolutionviamaskednounphraseprediction/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..0bc8ce761edf2812f71c4e477348f3fb04cacde7 --- /dev/null +++ b/unsupervisedpronounresolutionviamaskednounphraseprediction/layout.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9a78f3a98e34216d6775355dddf845b1aaf1da2a567666c446ae1be403767174 +size 249451 diff --git a/usingadversarialattackstorevealthestatisticalbiasinmachinereadingcomprehensionmodels/1e56eae1-3be9-4992-a805-ce09fab14354_content_list.json b/usingadversarialattackstorevealthestatisticalbiasinmachinereadingcomprehensionmodels/1e56eae1-3be9-4992-a805-ce09fab14354_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..31f1d625c4cf6e9db03cbd2137507d812441a946 --- /dev/null +++ b/usingadversarialattackstorevealthestatisticalbiasinmachinereadingcomprehensionmodels/1e56eae1-3be9-4992-a805-ce09fab14354_content_list.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9bf6d1a3f4ec8ef99490b93c0cee8cb92834f9367b2f59090dd95a8095932a05 +size 61473 diff --git a/usingadversarialattackstorevealthestatisticalbiasinmachinereadingcomprehensionmodels/1e56eae1-3be9-4992-a805-ce09fab14354_model.json b/usingadversarialattackstorevealthestatisticalbiasinmachinereadingcomprehensionmodels/1e56eae1-3be9-4992-a805-ce09fab14354_model.json new file mode 100644 index 0000000000000000000000000000000000000000..3e0bc4638f5c662b5d07062ade195b5732575e6a --- /dev/null +++ b/usingadversarialattackstorevealthestatisticalbiasinmachinereadingcomprehensionmodels/1e56eae1-3be9-4992-a805-ce09fab14354_model.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cf94c19ae1105421c933edc96af00d49bb631d3841ad61a8e0b7428b63ae4f42 +size 78537 diff --git a/usingadversarialattackstorevealthestatisticalbiasinmachinereadingcomprehensionmodels/1e56eae1-3be9-4992-a805-ce09fab14354_origin.pdf b/usingadversarialattackstorevealthestatisticalbiasinmachinereadingcomprehensionmodels/1e56eae1-3be9-4992-a805-ce09fab14354_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..09de5e8b5ead0b2b2e092ba5d48acf7380611e9e --- /dev/null +++ b/usingadversarialattackstorevealthestatisticalbiasinmachinereadingcomprehensionmodels/1e56eae1-3be9-4992-a805-ce09fab14354_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:75c6e524bf3dee2158cc17ccc1a309e649f0babd1fa7d9c778877d6eb9db2750 +size 2024969 diff --git a/usingadversarialattackstorevealthestatisticalbiasinmachinereadingcomprehensionmodels/full.md b/usingadversarialattackstorevealthestatisticalbiasinmachinereadingcomprehensionmodels/full.md new file mode 100644 index 0000000000000000000000000000000000000000..fe927ddab049b7062894bddb57ee2af16bdd40a5 --- /dev/null +++ b/usingadversarialattackstorevealthestatisticalbiasinmachinereadingcomprehensionmodels/full.md @@ -0,0 +1,300 @@ +# Using Adversarial Attacks to Reveal the Statistical Bias in Machine Reading Comprehension Models + +Jieyu Lin $^{2}$ , Jiajie Zou $^{2}$ , Nai Ding $^{1,2*}$ + +1Zhejiang Lab / Hangzhou, China + +$^{2}$ Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Sciences, Zhejiang University / Hangzhou, + +China + +{1jy5905, jiajiezou, ding_nai}@zju.edu.cn + +# Abstract + +Pre-trained language models have achieved human-level performance on many Machine Reading Comprehension (MRC) tasks, but it remains unclear whether these models truly understand language or answer questions by exploiting statistical biases in datasets. Here, we demonstrate a simple yet effective method to attack MRC models and reveal the statistical biases in these models. We apply the method to the RACE dataset, for which the answer to each MRC question is selected from 4 options. It is found that several pre-trained language models, including BERT, ALBERT, and RoBERTa, show consistent preference to some options, even when these options are irrelevant to the question. When interfered by these irrelevant options, the performance of MRC models can be reduced from human-level performance to the chance-level performance. Human readers, however, are not clearly affected by these irrelevant options. Finally, we propose an augmented training method that can greatly reduce models' statistical biases. + +# 1 Introduction + +Reading comprehension tasks are useful to quantify language ability of both humans and machines (Richardson et al., 2013; Xie et al., 2018; Berzak et al., 2020). Deep neural network (DNN) models have achieved high performance on many MRC tasks, but these models are not easily explainable (Devlin et al., 2019; Brown et al., 2020). It is also shown that DNN models are often sensitive to adversarial attacks (Jia and Liang, 2017; Ribeiro et al., 2018; Si et al., 2019, 2020). Furthermore, it has been shown DNN models can solve MRC tasks with relatively high accuracy when crucial information is removed so that the tasks are no longer solvable by humans (Gururangan et al., 2018; Si + +et al., 2019; Berzak et al., 2020). All such evidence suggests that the high accuracy DNN models achieve on MRC tasks does not solely rely on these models' language comprehension ability. At least to some extent, the high accuracy reflects exploitation of statistical biases in the datasets (Gururangan et al., 2018; Si et al., 2019; Berzak et al., 2020). + +Here, we propose a new model-independent method to evaluate to what extent models solve MRC tasks by exploiting statistical biases in the dataset. As a case study, we only focus on the classic RACE dataset (Lai et al., 2017), which requires MRC models to answer multiple-choice reading comprehension questions based on a passage. The advantage of multiple-choice questions is that its performance can be objectively evaluated. At the same time, it does not require the answer to be within the passage, allowing to test, e.g., the summarization or inference ability of models. Nevertheless, since models are trained to select the right option from 4 options, which are designed by humans and may contain statistical biases, models may learn statistical properties of the right option. Consequently, models may tend to select options with these statistical properties similar to the properties of the right option without referring to the passage and question. Our method is designed to reveal this kind of statistical bias. + +The logic of our method is straightforward: For each multiple-choice question, we gather a large number of options that are irrelevant to the question and passage. We ask the model to score how likely each irrelevant option is the right option. If a model is biased, it may always assign higher scores to some irrelevant options than others, even if all the options are irrelevant. If a model is so severely biased, which turns out to be true for all models tested here, it may assign higher scores to some irrelevant options than the true answer and select the irrelevant option as the answer. Here, the irrelevant + +options that are often selected as the answer are referred to as magnet options. + +# 2 Dataset and Pre-trained Models + +We used RACE dataset in our experiment (Lai et al., 2017), which is a large-scale reading comprehension data set covering more than 28,000 passages and nearly 100,000 questions. The task was to answer multi-choice questions based on a passage. Specifically, each question contained a triplet $(p_i, q_i, o_i)$ , where $p_i$ denoted a passage, $q_i$ denoted a question, and $o_i$ denoted a candidate set of 4 options, i.e., $o_i = \{o_{i,1}, o_{i,2}, o_{i,3}, o_{i,4}\}$ . Only one option was the correct answer, and the accuracy was evaluated by the percent of questions being correctly answered. + +We tested 3 pre-trained language models, i.e., BERT (Devlin et al., 2019), RoBERTa (Liu et al., 2019), and ALBERT (Lan et al., 2019). For each model, we separately tested the base version and large version. We built our models based on pretrained transformer models in the Huggingface (Wolf et al., 2020). We fine-tuned pre-trained models based on the RACE dataset and the parameters we used for fine-tuning were shown in Appendix A.1. + +The passage, question, and an option were concatenated as the input to models, i.e., [CLS, $p_i$ , SEP, $q_i$ , $o_{i,j}$ , SEP]. The 4 options were separately encoded. The concatenated sequence was encoded through the models and the output embedding of CLS was denoted as $C_{i,j}$ . We used a linear transformation to convert vector $C_{i,j}$ into a scalar $S(o_{i,j})$ , i.e., $S(o_{i,j}) = WC_{i,j}$ . The scalar $S(o_{i,j})$ was referred to as the score of the option $o_{i,j}$ . A score was calculated for each option, and the answer to a question was determined as the option with the highest score, i.e., $\operatorname{argmax}_j S(o_{i,j})$ . + +# 3 Adversarial Method + +# 3.1 Screen Potential Magnet Options + +We evaluated potential statistical biases in a model by giving it a large number of irrelevant options. For each question, we augmented the options using a set of irrelevant options, i.e., $O_{A} = \{o_{a1}, o_{a2}, \ldots, o_{aN}\}$ . $O_{A}$ was randomly selected from the RACE dataset with 2 constraints. First, the options belonged to questions that were not targeted at passage $p_{i}$ . Second, none of the options in $O_{A}$ was identical to any of the original options in + +$o_i$ . The augmented question was denoted as $(p_i, q_i, \{o_{i,1}, o_{i,2}, o_{i,3}, o_{i,4}, o_{a1}, \dots, o_{aj}, \dots, o_{aN}\})$ . A score was independently computed for each option using the procedure mentioned above. Since the options in $O_A$ were irrelevant, an ideal model should never select them as answers. If $\max_j S(o_{i,j}) < S(o_{ak})$ for any $k$ , however, the model would select the $k^{\text{th}}$ irrelevant option as the answer. We define an interference score $T_k$ using the following equation. + +$$ +T _ {k} = \frac {1}{N} \sum_ {i = 1} ^ {N} T _ {i, k}, \quad \text {w h e r e} +$$ + +$$ +T _ {i, k} = \left\{ \begin{array}{l l} 1, & \quad i f \quad \max _ {j} S (o _ {i, j}) < S (o _ {a k}) \\ 0, & \quad o t h e r w i s e \end{array} \right. +$$ + +For an ideal model, $T_{i,k}$ should always be 0. For a model that makes mistakes but shows no consistent bias, the interference score should be comparable for all $o_{ak}$ . If the model is biased, the interference score may be always high for some options so that the model always selects them as the answer whether they are relevant to the question or not. + +# 3.2 Adversarial Attack + +We constructed an adversary attack to the MRC models using one magnet option. For each question, we replaced a wrong option with a magnet option, i.e., $o_{ak}$ . The replaced option set was $\{o_{i,1}, o_{i,2}, o_{i,3}, o_{ak}\}$ . The passage and the question were not modified, and the answer did not change. An example was shown in Figure 1. If the model chooses the original answer even when a magnet option is introduced, it is stable, not sensitive to the attack. In contrast, if it chooses the magnet option, i.e., $o_{ak}$ , as the answer, it is successfully attacked. + +# 4 Results and Analyses + +# 4.1 Experiments Setup + +To screen potential magnet options, we constructed a large set of irrelevant options, i.e., $O_A$ , by randomly selecting 300 passages from the RACE test set, which were associated with 1064 questions. Furthermore, to test whether options in the training set can cause stronger interference, we also randomly selected 300 passages from the RACE training set, which had 1029 questions. The options from the test and training set were pooled to create $O_A$ , which had 8372 options in total. + +Passage: "...Quantum computers could be able to do what modern supercomputers are unable to do by using transistors that are able to take on many states at the same time..." + +Question: According to the text, quantum computing _ . + +# Original Options: + +$A$ can reduce the cost of computers + $B$ can make computers run by themselves +C.will work by using transistors +D. has been put in use so far + +# Model Choice: + +$C$ - correct $A,B,$ or $D$ - incorrect + +# Adversarial Options: + +$A$ . can reduce the cost of computers +B. misfortune may be an actual blessing +$C.$ will work by using transistors +$D$ has been put in use so far + +Model Choice: $B$ - incorrect, successfully attacked + +$C$ - correct, not attacked + +$A$ or $D$ - incorrect, not attacked + +![](images/6becc3e807657d7756b49d23e03a35b55ba7d40bda2e59f19ebe2744f1bba085.jpg) +Figure 1: An example of the task and adversarial attack. The option in bold is the true answer, and the option in red indicates the irrelevant option that was used for attack. +Figure 2: Interference score evaluated based on a subset of questions. + +For such a large number of irrelevant options, it was computationally challenging to evaluate the interference score of each option based on each question in the RACE test set. Therefore, as a screening procedure, we first randomly selected 100 passages from the RACE test set, which have a total of 346 questions. The interference score for each of the 8372 irrelevant options was evaluated based on the 346 questions. + +After potential magnet options were determined by the screening procedure, the interference score of magnet options were further evaluated using all questions in RACE test set. For RACE test set, the accuracy of the models ranged between about 0.6 and 0.85, with RoBERTa-large achieving the highest performance (Table 1). + +# 4.2 Screening for Magnet Options + +The interference score for 8372 options was independently calculated for each model. Results were shown in Figure 2, where the interference score was sorted for each model. It is found that most of the irrelevant options had a non-zero interference + +score, and some irrelevant options yielded high interference scores around 0.8, which meant the models would choose those irrelevant options as the answer for about $80\%$ of the questions. Irrelevant options from the training and test sets had similar interference scores (Appendix B.1). + +It was found that the options with exceptionally high interference scores around 0.8 were options that combined other options, such as "all the above", which were called the option-combination series. However, not all the magnet options were from the option-combination series. Normal statements, e.g., "The passage doesn't tell us the end of the story of the movie", could also reach an average interference score around 0.34. + +The correlation between the interference score between models were shown in Appendix B.2. We separately showed the results for options from the option-combination series and the others. The correlation coefficient between models had an average value around 0.76, which proved that the interference score was correlated across models. From another perspective, it also implied that our method could work as a model-insensitive adversarial attack method. + +# 4.3 Validate Magnet Options and Adversarial Attack + +We further evaluated the interference score of potential magnet options based on all the questions in the RACE test set. To construct a set of magnet options for this analysis, we averaged the interference score across 3 models, i.e., BERT-large, RoBERTa-large, and ALBERT-large. All options in $O_A$ were sorted based on the average score, and we selected 20 options with the highest interference scores to construct the magnet option set, with the + +
VersionBERTALBERTRoBERTa
baselargebaselargebaselarge
Original accuracy0.6140.6810.6830.7520.7380.846
Adversarial accuracy10.0940.1670.2170.0640.1660.297
Adversarial accuracy20.3810.5240.3340.5060.6560.798
+ +Table 1: Model performance on the RACE test set and model performance after being attacked. The superscript 1 meant use "A, B and C" to attack, and the superscript 2 meant use "The passage doesn't tell us the end of the story of the movie" to attack. + +![](images/ab2d891863645f39fd9571c20973a6d8b7a14a04ced9f46c80f1380d820f1074.jpg) +Figure 3: Interference score evaluated based on the whole RACE test set. + +![](images/f80f17db117b77cb4b2c67ace2ba49a5512f241d8751eead2682bed6d43208a1.jpg) +Figure 4: Interference score for the human experiment and the corresponding interference scores for the models. + +following constraint: Since options with the highest interference scores were often from the option-combination series, to increase diversity, we only included 3 options from the option-combination series. We listed all the 20 magnet options in Appendix A.2. The interference score calculated based on the whole RACE test set was shown in Figure 3, which was very similar to the results based on the subset of 346 questions in Figure 2 (comparing average-whole and average-subset in Figure 3). + +Table 1 showed the accuracy of models when attacked by 2 example magnet options. When attacked, the model performance could drop by as + +much as 0.68. + +# 4.4 Human Evaluation + +Next, we verified whether humans were also confused by the magnet options. We randomly selected 20 questions and 10 magnet options. The 10 magnet options selected were listed in Appendix A.3. Ten questions were not modified while the other 10 questions were attacked using the procedure shown in Figure 1. Twenty human evaluators answered these 20 questions online. The accuracy of humans did not reduce under attack (0.90 in the original samples and 0.94 in the adversarial samples). The interference score for humans, also the corresponding interference score for the models, was shown in Figure 4. Humans were not confused by the magnet options. + +# 4.5 Training with Adversarial Examples + +To reduce sensitivity to magnet options and to potentially reduce the statistical biases of MRC models, we proposed an augmented training method and tested the method using the base version of all models. In the augmented training method, 400 options with the highest interference scores were selected as the irrelevant option set. For each question in the RACE training set, the option set was augmented by adding an option randomly chosen from the irrelevant option set. In other words, although each original question has 4 options, during the augmented training each question has 5 options, including the 4 original options and a randomly chosen irrelevant option. We fine-tuned pre-trained models based on the training set with augmented options. + +The accuracy of models fine-tuned using augmented options were shown in Table 2, comparable to the original accuracy in Table 1. When attacked, however, the accuracy of models fine-tuned using augmented options were much higher than the adversarial accuracy in Table 1. + +The 1000 options with the highest interference + +
base versionBERTALBERTRoBERTa
Original accuracy0.6010.6890.723
Adversarial accuracy10.5760.6810.725
Adversarial accuracy20.6700.7400.778
+ +Table 2: Model performance on the RACE test set based on augmented training. + +![](images/e19ddfdc519387a0ff3147e0d774e8607b5c2058216f84463a991b1a84e447d1.jpg) +Figure 5: Interference score of 1186 randomly chosen options that are not used in augmented training. + +scores were selected to evaluate the effect of augmented training, as shown in Appendix C. Result showed that the interference score dropped for both the 400 options used for augmented training and the other 600 options that were not used for training. Therefore, the effect of augmented training could generalize to samples not used for augmented training. + +Another experiment was implemented to explore the impact of irrelevant option set selection. We separately used options with high and low interference scores for training and found that options with higher interference score were more effective at reducing statistical biases (Figure 5). + +# 4.6 Interference Score Analysis + +Did the statistical biases revealed in previous analyses originate from the pre-training process or the fine-tuning process? Without fine-tuning, the pretrained models performed poorly on RACE. However, results showed that such an imprecise model could show strong biases (Appendix B.3). Interestingly, the interference score was not correlated between the pre-trained model and the fine-tuned model, suggesting that fine-tuning overrode the biases caused by pre-training and introduced new forms of biases. + +# 5 Related Work + +Our attack strategy distinguishes from previous work in two ways. First, unlike, e.g., gradient- + +based methods (Ebrahimi et al., 2018; Cheng et al., 2020), our method does not require any knowledge about the structure of DNN models. Second, some methods manipulate the passage in a passagedependent way (Jia and Liang, 2017; Si et al., 2020; Zhao et al., 2018), while our method manipulate the options in a passage-independent way. Furthermore, we proposed a strategy to train more robust models that are insensitive to our attack. + +Here, we restricted our discussion to RACE, but our method is applicable to other tasks in which the answer is selected from a limited set of options. For example, for span extraction tasks, such as SQuAD, the method will insert a large number of irrelevant phrases into the passage and analyze which phrases are often selected as the answer. In this way, our method is similar to the trigger-based attack methods (Wallace et al., 2019), but the difference is that our method test whether the inserted irrelevant phrase is selected as the answer while the trigger-based methods test whether the content following the trigger phrase is selected. + +# 6 Conclusion + +In summary, we propose a new method to evaluate the statistical biases in MRC models. It is found that current MRC models have strong statistical biases, and are therefore sensitive to adversarial attack. When attacked using the method proposed here, model performance can drop from human-level performance to chance-level performance. To alleviate sensitivity to such attacks, we provided an augmented training procedure that effectively enhances the robustness of models. + +# Acknowledgments + +The authors would like to thank the anonymous reviewers for their helpful suggestions and comments. Work supported by Major Scientific Research Project of Zhejiang Lab 2019KB0AC02 and National Natural Science Foundation of China 31771248. + +# References + +Yevgeni Berzak, Jonathan Malmaud, and Roger Levy. 2020. STARC: Structured annotations for reading comprehension. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5726-5735, Online. Association for Computational Linguistics. +Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020. Language models are few-shot learners. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual. +Minhao Cheng, Jinfeng Yi, Pin-Yu Chen, Huan Zhang, and Cho-Jui Hsieh. 2020. Seq2sick: Evaluating the robustness of sequence-to-sequence models with adversarial examples. In The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020, pages 3601-3608. AAAI Press. +Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171-4186, Minneapolis, Minnesota. Association for Computational Linguistics. +Javid Ebrahimi, Anyi Rao, Daniel Lowd, and Dejing Dou. 2018. HotFlip: White-box adversarial examples for text classification. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 31-36, Melbourne, Australia. Association for Computational Linguistics. +Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel Bowman, and Noah A. Smith. 2018. Annotation artifacts in natural language inference data. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 107-112, New Orleans, Louisiana. Association for Computational Linguistics. +Robin Jia and Percy Liang. 2017. Adversarial examples for evaluating reading comprehension systems. + +In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2021-2031, Copenhagen, Denmark. Association for Computational Linguistics. +Guokun Lai, Qizhe Xie, Hanxiao Liu, Yiming Yang, and Eduard Hovy. 2017. RACE: Large-scale ReAding comprehension dataset from examinations. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 785-794, Copenhagen, Denmark. Association for Computational Linguistics. +Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2019. Albert: A lite bert for self-supervised learning of language representations. arXiv preprint arXiv:1909.11942. +Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692. +Qiu Ran, Peng Li, Weiwei Hu, and Jie Zhou. 2019. Option comparison network for multiple-choice reading comprehension. arXiv preprint arXiv:1903.03033. +Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2018. Semantically equivalent adversarial rules for debugging nlp models. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 856-865. +Matthew Richardson, Christopher JC Burges, and Erin Renshaw. 2013. Mctest: A challenge dataset for the open-domain machine comprehension of text. In Proceedings of the 2013 conference on empirical methods in natural language processing, pages 193-203. +Chenglei Si, Shuohang Wang, Min-Yen Kan, and Jing Jiang. 2019. What does BERT learn from multiple-choice reading comprehension datasets? CoRR, abs/1910.12391. +Chenglei Si, Ziqing Yang, Yiming Cui, Wentao Ma, Ting Liu, and Shijin Wang. 2020. Benchmarking robustness of machine reading comprehension models. CoRR, abs/2004.14004. +Eric Wallace, Shi Feng, Nikhil Kandpal, Matt Gardner, and Sameer Singh. 2019. Universal adversarial triggers for attacking and analyzing NLP. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 2153-2162, Hong Kong, China. Association for Computational Linguistics. +Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, + +Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush. 2020. Transformers: State-of-the-art natural language processing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 38-45, Online. Association for Computational Linguistics. +Qizhe Xie, Guokun Lai, Zihang Dai, and Eduard Hovy. 2018. Large-scale cloze test dataset created by teachers. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2344-2356, Brussels, Belgium. Association for Computational Linguistics. +Shuailiang Zhang, Hai Zhao, Yuwei Wu, Zhuosheng Zhang, Xi Zhou, and Xiang Zhou. 2020. DCMN+: dual co-matching network for multi-choice reading comprehension. In The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020, pages 9563-9570. AAAI Press. +Zhengli Zhao, Dheeru Dua, and Sameer Singh. 2018. Generating natural adversarial examples. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings. OpenReview.net. + +
versionBERTALBERTRoBERTa
baselargebaselargebaselarge
learning rate1.00E-051.00E-052.00E-051.00E-051.00E-051.00E-05
train epochs55//44
train steps//1200012000//
train batch size162432321616
warmup steps001000100012001200
weight decay00000.10.1
+ +![](images/2655dfe66dfed8da828c3b53cb4e18b04fc682d76b029d4aa95b1c6547fb13f2.jpg) + +![](images/18119285c9c4b13e67c9ce358ccef944fc09f1835c56f62bd4d3c79b2cc1e3e8.jpg) + +![](images/bff02364c4ee60a78e272e943c0ef1779ae3d96e27579212e3c73400b218f706.jpg) + +![](images/86e945b3d37f1459449dd6419a56acb84bc8aeaeb0414746e5aa707148569066.jpg) + +![](images/fd477605dbc54b662b062052f712364405ec884f2900d19a45ec565c157a84eb.jpg) +Figure 6: Interference score evaluated based on a subset of questions. + +![](images/b663fc942d84eb79cd2da52d7d808925198e585de2e8c265217f4ad327f3f116.jpg) +Figure 7: The scatter matrix diagram of the interference scores of the irrelevant options among models. + +Table 3: Hyperparameters for fine-tuning on RACE. We adapted these hyperparamers from Lan et al. (2019); Liu et al. (2019); Ran et al. (2019); Zhang et al. (2020). + +
BERT -base
R:0.824** R:0.375 R:0.737**BERT -large
R:0.764** R:0.777** R:0.647**R:0.751** R:0.440 R:0.598**ALBERT -base
R:0.711** R:0.459 R:0.541**R:0.734** R:0.976** R:0.507**R:0.782** R:0.477 R:0.619**ALBERT -large
R:0.768** R:0.756** R:0.642**R:0.799** R:0.739** R:0.640**R:0.760** R:0.549 R:0.576**R:0.813** R:0.766** R:0.555**RoBERTa -base
R:0.636** R:0.914** R:0.389**R:0.713** R:0.472 R:0.458**R:0.694** R:0.839** R:0.418**R:0.821** R:0.533 R:0.428**R:0.833** R:0.800** R:0.557**RoBERTa -large
+ +# A Experimental Details + +# A.1 Fine-tuning Parameters + +The parameters we used in the process of finetuning the pre-trained models were shown in Table 3. + +# A.2 Magnet Options for Validate + +The 20 magnet options used for evaluating the interference scores in Section 4.3 were shown as following. The sentences selected from the RACE training set were shown in bold. + +1. A, B and C +2. all of A, B and C +3. All of the above. +4. Not all of it can be avoided. +5. It's well beyond what the author could be responsible for. +6. The passage doesn't tell us the end of the story of the movie +7. didn't give the real answer + +8. make us know it's important to listen to people who offer a different perspective through his experience +9. give us a turning point in mind +10. not strictly stuck to +11. You should purposely go out and make these mistakes so that you can learn from them and not have them ruin your entire life. +12. what's inside a person is much more important than his/her appearance. +13. Not all of it is man-made Ming dynasty structure. +14. introduce the topic of the passage +15. The central command didn't exactly state what had caused the crash. +16. one good turn deserves another. +17. the growing population is not the real cause of the environment problem., +18. misfortune may be an actual blessing. + +
BERT-baseCorrelation coefficientaccuracyAverage interference score
Pre-trained model-0.0230.3150.0518
Partly fine-tuned model0.069*0.3150.0214
Fine-tuned model10.6130.0713
RoBERTa-baseCorrelation coefficientaccuracyAverage interference score
Pre-trained model-0.0210.2250.3553
Partly fine-tuned model0.088**0.2890.2282
Fine-tuned model10.7430.0569
ALBERT-baseCorrelation coefficientaccuracyAverage interference score
Pre-trained model-0.0130.2540.1483
Partly fine-tuned model0.231**0.390.1043
Fine-tuned model10.7020.0703
+ +Table 4: Interference score of 1000 randomly selected irrelevant options for the same model architecture before and after fine-tuning. Correlation coefficient was counted between the interference score before and after fine-tuning $(^{**}P < 0.01$ , and $^* P < 0.05$ ). + +19. may meet with difficulties sometimes +20. good answers are always coming when we think outside of the box + +# A.3 Magnet Options for Human Evaluation + +The 10 magnet options used for human evaluating in Section 4.4. + +1. all the above +2. Both B and C +3. do all of the above +4. A and B +5. not strictly stuck to +6. The passage doesn't tell us the end of the story of the movie +7. It's well beyond what the author could be responsible for. +8. You should purposely go out and make these mistakes so that you can learn from them and not have them ruin your entire life. +9. make us know it's important to listen to people who offer a different perspective through his experience +10. Not all of it is man-made Ming dynasty structure. + +# B Study of Interference Score + +# B.1 Comparison of Irrelevant Options from RACE Training and Test Set + +Different models in Figure 2 were separately shown in Figure 6. It denoted that irrelevant options from the training and test sets had similar interference score. Only in BERT-large and ALBERT-large models, the interference scores of the irrelevant options from the training set were higher than those + +![](images/6a0e906ddf24e8ad38e602cd227a79ebcdcfc2eed115eb8cbefa88d7c3545710.jpg) + +![](images/0c99b9ba730b0f553c85e44da656f24566bad7c129a64967013cf00d3cd03750.jpg) + +![](images/0e94e08f8efd320bc21df1ec63a0afd298d359b3112a40193665d3f9e221bb5f.jpg) +Figure 8: Interference score comparison of models evaluated based on a subset of questions. + +from the test set in a certain range. + +# B.2 Comparison of Interference Scores Based on Different Models + +The scatter matrix diagram of the interference scores of the irrelevant options among different models was shown in Figure 7. The detailed experimental process was described in Section 4.2. Here, text in black showed the correlation coefficient of all options; text in green showed the options of the option-combination series; text in blue showed the options except the option-combination series. + +In general, the interference scores between models had high correlation coefficients. Models from the same architecture were more likely to have similar interference scores. + +# B.3 Comparison of Interference Scores During Fine-tuning + +For each model architecture, the pre-trained model, partly fine-tuned model (fine-tuned the linear transformation mentioned in Section 2), and fully fine-tuned model were collected, and were used to evaluate the interference score of 1,000 randomly selected irrelevant options. The results were shown in Table 4. The subset of questions mentioned in Section 4.1 were used to evaluate the interference score. + +# C Augmented Training Result + +The augmented training results were shown in Figure 8. In the figures, the left side of the red line contains the irrelevant options that were used in augmented training, and the right is the irrelevant options that were not involved in augmented training. \ No newline at end of file diff --git a/usingadversarialattackstorevealthestatisticalbiasinmachinereadingcomprehensionmodels/images.zip b/usingadversarialattackstorevealthestatisticalbiasinmachinereadingcomprehensionmodels/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..b5d07a0683b6b4d850afc53548a4a58555d90a2e --- /dev/null +++ b/usingadversarialattackstorevealthestatisticalbiasinmachinereadingcomprehensionmodels/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e36e57f13829a5a71338099e816d87a57e0a5d6c3887aecc75acf792ac367d7a +size 432579 diff --git a/usingadversarialattackstorevealthestatisticalbiasinmachinereadingcomprehensionmodels/layout.json b/usingadversarialattackstorevealthestatisticalbiasinmachinereadingcomprehensionmodels/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..4b02cb49665d6de1bf8b537f74d4fd1260094ec5 --- /dev/null +++ b/usingadversarialattackstorevealthestatisticalbiasinmachinereadingcomprehensionmodels/layout.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ae48b51891da7e53b2645f473def3fbe78017ccc10c9ce2aefc3e9c676fdf9b5 +size 322450 diff --git a/vaultvariableunifiedlongtextrepresentationformachinereadingcomprehension/0c2111c9-9184-47a4-a05e-04ca576859df_content_list.json b/vaultvariableunifiedlongtextrepresentationformachinereadingcomprehension/0c2111c9-9184-47a4-a05e-04ca576859df_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..1d585484c32eb29672ede0a5145bb780fa4ccdba --- /dev/null +++ b/vaultvariableunifiedlongtextrepresentationformachinereadingcomprehension/0c2111c9-9184-47a4-a05e-04ca576859df_content_list.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6a2ccb914d8201645aca1f3bba7149ca61039fcc250ce0ed01014aa293e927c8 +size 54666 diff --git a/vaultvariableunifiedlongtextrepresentationformachinereadingcomprehension/0c2111c9-9184-47a4-a05e-04ca576859df_model.json b/vaultvariableunifiedlongtextrepresentationformachinereadingcomprehension/0c2111c9-9184-47a4-a05e-04ca576859df_model.json new file mode 100644 index 0000000000000000000000000000000000000000..768d5f9e3ab0661f94be19ac97a963616c4c2115 --- /dev/null +++ b/vaultvariableunifiedlongtextrepresentationformachinereadingcomprehension/0c2111c9-9184-47a4-a05e-04ca576859df_model.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b7b2bad700dced3a46e17f255341553b29adefb171869178de237131e8e4adbe +size 67428 diff --git a/vaultvariableunifiedlongtextrepresentationformachinereadingcomprehension/0c2111c9-9184-47a4-a05e-04ca576859df_origin.pdf b/vaultvariableunifiedlongtextrepresentationformachinereadingcomprehension/0c2111c9-9184-47a4-a05e-04ca576859df_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..c5ca6c20db8a5026e4f23f1137fda7390052ad92 --- /dev/null +++ b/vaultvariableunifiedlongtextrepresentationformachinereadingcomprehension/0c2111c9-9184-47a4-a05e-04ca576859df_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2780e1038bba66b8e4c1945a61703ee5919c1141f6b05d74c388b957cfe2bccb +size 2214952 diff --git a/vaultvariableunifiedlongtextrepresentationformachinereadingcomprehension/full.md b/vaultvariableunifiedlongtextrepresentationformachinereadingcomprehension/full.md new file mode 100644 index 0000000000000000000000000000000000000000..a3aeb8432ae7f3bacedfa20959dcb0e61857f8dd --- /dev/null +++ b/vaultvariableunifiedlongtextrepresentationformachinereadingcomprehension/full.md @@ -0,0 +1,221 @@ +# VAULT: VAriable Unified Long Text Representation for Machine Reading Comprehension + +Haoyang Wen $^{2\dagger*}$ , Anthony Ferritto $^{1\dagger}$ , Heng Ji $^{2}$ , Radu Florian $^{1}$ , Avirup Sil $^{1}$ + +1 IBM Research AI, 2 University of Illinois Urbana-Champaign + +wen17@illinois.edu, aferritto@ibm.com hengji@illinois.edu, {raduf,avi}@us.ibm.com + +# Abstract + +Existing models on Machine Reading Comprehension (MRC) require complex model architecture for effectively modeling long texts with paragraph representation and classification, thereby making inference computationally inefficient for production use. In this work, we propose VAULT: a light-weight and parallel-efficient paragraph representation for MRC based on contextualized representation from long document input, trained using a new Gaussian distribution-based objective that pays close attention to the partially correct instances that are close to the ground-truth. We validate our VAULT architecture showing experimental results on two benchmark MRC datasets that require long context modeling; one Wikipedia-based (Natural Questions (NQ)) and the other on TechNotes (TechQA). VAULT can achieve comparable performance on NQ with a state-of-the-art (SOTA) complex document modeling approach while being 16 times faster, demonstrating the efficiency of our proposed model. We also demonstrate that our model can also be effectively adapted to a completely different domain - TechQA - with large improvement over a model fine-tuned on a previously published large PLM. + +# 1 Introduction + +Machine Reading Comprehension (MRC) has seen great advances in recent years with the rise of pre-trained language models (PLM) (Devlin et al., 2019; Liu et al., 2019; Lan et al., 2019) and public leaderboards (Rajpurkar et al., 2016, 2018; Yang et al., 2018; Joshi et al., 2017; Welbl et al., 2018; Kwiatkowski et al., 2019). While some challenges (Rajpurkar et al., 2016, 2018) focus on reading comprehension with shorter contexts, many others + +(Welbl et al., 2018; Joshi et al., 2017; Kwiatkowski et al., 2019; Tanaka et al., 2021) focus on longer contexts that cannot fit into a typical 512 sub-token transformer window. Motivated by this, we focus on reading comprehension with long contexts. + +One newer approach to this task (Zheng et al., 2020) focuses on modeling document hierarchy to represent multi-grained information for answer extraction. Although this approach creates a strong representation of the text, it suffers from a significant drawback. The graph-based methods (Velicković et al., 2018) are inefficient on parallel hardware, such as GPUs, resulting in slow inference speed (Zhou et al., 2018; Zheng et al., 2020). Motivated by this, in this paper, we propose a reading comprehension model that addresses the above issue and uses a more light-weight, parallel-efficient (i.e. efficient on parallel hardware) paragraph representation based on long contextual representations for providing paragraph answers to questions. Instead of modeling document hierarchy from tokens to document pieces, we first introduce a base model that builds on top of a large "long-context" PLM (we use Longformer, Beltagy et al., 2020) to model longer contexts with lightweight representations of each paragraph. We note that while our approach could work with any PLM, we expect it to perform better with models that can support long contexts and therefore see more paragraph representations at once (Gong et al., 2020). To provide our model a notion of paragraph position relative to a text we also introduce position-aware paragraph representations (PAPR) utilizing special markup tokens and provide them as input for efficient paragraph classification. This approach allows us to encode paragraph-level position in the text and teach the model to impute information on each paragraph into the hidden outputs for these tokens that we can exploit to determine in which paragraph the answer resides. We then predict the + +answer span from this identified paragraph. + +While previous MRC methods (Chen et al., 2017; Devlin et al., 2019) use ground-truth start and end span positions exclusively as training objectives when extracting answer spans from the context and consider all other positions as incorrect instances equally. However, spans that overlap with the ground-truth should be considered as partially correct. Motivated by Li et al. (2020) which proposes a new optimization criteria based on constructing prior distribution over synonyms for machine translation, we further propose to improve the above base model by considering the start and end positions of ground-truth answer spans as Gaussian-like distributions, instead of single points, and optimize our model using statistical distance. + +We call this final model, VAULT (Viable Unified Long Text representation) as it can handle a variable number and lengths of paragraphs at any position with the same unified model structure to handle long texts. + +To evaluate the performance of VAULT, we select the new Natural Questions (NQ, Kwiatkowski et al., 2019) and TechQA (Castelli et al., 2020) datasets. NQ attempts to make Machine Reading Comprehension (MRC) more realistic by providing longer Wikipedia documents as contexts and real user search-engine queries as questions, and aims at avoiding observation bias: high lexical overlap between the question and the answer context which can happen frequently if the question is created after the user sees the paragraph (Rajpurkar et al., 2016, 2018; Yang et al., 2018; Chakravarti et al., 2020; Karpukhin et al., 2020; Lee et al., 2019; Murdock et al., 2018). The task introduces the extraction of long answers (henceforth LA; typically paragraphs) besides also requiring short answers (henceforth SA) similar to SQuAD (Rajpurkar et al., 2016). In Figure 1 we examine an example from NQ along with the answers of VAULT and (Zheng et al., 2020). We see that while VAULT can extract answers from the very bottom of a page – if relevant – the existing system suffers from positional bias. It often predicts answers from the first paragraph of Wikipedia (a region which often contains the most relevant information). We evaluate our model for domain adaptation on TechQA, a recently introduced challenging dataset for QA on technical support articles where answers are typically 3-5 times longer than standard MRC datasets (Rajpurkar et al., 2016, 2018). + +![](images/c1fef3bda94480959aa5fcec0182fbec0f6e9b9c2988917bfa0943edbe1f0d6d.jpg) +Figure 1: Example from the NQ dataset with answers from VAULT and (Zheng et al., 2020). + +Empirically we first show that VAULT achieves comparable performance on NQ with (Zheng et al., 2020)'s document modeling architecture based on graph neural networks while being 16 times faster, demonstrating the efficiency of our proposed model. Secondly, we show the generalization of our model architecture for domain adaptation on TechQA. Our experiments show that our model pre-trained on NQ can be effectively adapted to TechQA outperforming a standard fine-tuned model trained on a large PLM such as RoBERTa. To summarize, our contributions include: + +1. We introduce a novel and effective yet simple paragraph representation. +2. We introduce soft labels to leverage information from local contexts near ground-truth during training which is novel for MRC. +3. Our model provides similar performance to a SOTA system on NQ while being 16 times faster and also effectively adapts to a new domain: TechQA. + +# 2 Related Work + +Machine reading comprehension has been widely modeled as cloze-type span extraction (Chen et al., 2017; Cui et al., 2017; Devlin et al., 2019). In NQ, we need to identify answers in two levels, long and short answers. (Alberti et al., 2019a) adapt a span extraction model for short answer extraction. (Zheng et al., 2020; Liu et al., 2020) construct complex networks for paragraph-level representation to enhance long answer classification along with span extraction for short answers. In this work, we propose a more light-weight and parallel-efficient way for constructing paragraph-level representation and classification by using longer context and + +modeling the negative instance through Gaussian prior optimization. + +Using the hierarchical nature of a long document for question answering has been previously studied by (Choi et al., 2017), where they use a hierarchical approach to select candidate sentences and extract answers in those candidates. However, due to the limit of input length for large PLMs, existing methods (Alberti et al., 2019b; Zheng et al., 2020; Chakravarti et al., 2020) slice long documents into document pieces and perform prediction for each piece separately. In our work, we show that by modeling longer input with position-aware paragraph representation coupled with Gaussian prior optimization (which is novel for MRC), we can achieve comparable performance using much simpler architecture compared to previous models, which coincide with recent new PLM for long inputs on question answering (Ainslie et al., 2020) $^{1}$ . + +# 3 Model Architecture + +In this section, we introduce VAULT, our proposed model that uses a simple yet effective paragraph representation based on a longer context. VAULT starts from a base classifier that utilizes position-aware paragraph representations trained on top of a large PLM: Longformer (Beltagy et al., 2020). Next, we further introduce our Gaussian Prior-based training objective that considers partial credits for positions near the ground-truth, instead of only focusing on one ground-truth position. We show an overview of VAULT on the example from Figure 1 in Figure 2. + +# 3.1 A Base "Paragraph" Predictor Model + +SOTA methods for paragraph prediction (Zheng et al., 2020; Liu et al., 2020) represent paragraphs through expensive graph modeling, making it inefficient for "large-scale" production MRC systems. On the other hand, simply selecting the first paragraph performs poorly (Kwiatkowski et al., 2019). We hypothesize that by modeling a much longer context even simple paragraph representation can be effective for paragraph (i.e., long answers) classification. For this purpose, we employ a large-window PLM: Longformer (Beltagy et al., 2020), which has shown effectiveness in modeling long contexts for QA (Yang et al., 2018; Welbl et al., 2018; Joshi et al., 2017). Compared to conven + +tional Transformer-based PLMs e.g. RoBERTa (Liu et al., 2019) that can only take up to 512 subword tokens, Longformer provides a much larger maximum input length of 4,096. + +Position-aware Paragraph Representation (PAPR): To address the fact that many popular unstructured texts such as Wikipedia pages have relatively standard ways of displaying certain relevant information (e.g. birthdays are usually in the first paragraph vs. spouse names are in the "Personal Life" paragraph), we provide the base model with a representation of which part of the text it is reading by marking the paragraphs with special atomic markup tokens ([paragraph=i]) at the beginning of each paragraph, indicating the position of the paragraph within the text². With this input representation, we then directly perform long answer classification using the special paragraph token output embedding. Formally, for every paragraph $l_i \in P$ , where $P$ are all paragraphs in a text and the representation for the corresponding markup token $h_i^p$ , the logit of a paragraph answer $a$ it computes is as $a_i^p = W h_i^p + b$ . + +We obtain additional document-piece representation from the standard [CLS] (Devlin et al., 2019) token to model document pieces that do not contain paragraph answers. The probability of choosing the paragraph given context $c$ , is computed as the softmax over paragraph candidate (with an answer span) logits and not containing answer logit: + +$$ +p _ {l} \left(l _ {i} \mid c\right) = \operatorname {s o f t m a x} \left(a _ {i} ^ {p}\right). +$$ + +We pad the paragraph representations to ensure a rectangular tensor in a batch. Our final prediction strategy is similar to Zheng et al. (2020) as we first choose the paragraph candidate with the highest logit among all candidates. We then extract span answers within the selected paragraph answer candidate using a standard pointer network. + +# 3.2 Gaussian Prior Optimization (GPO) + +Conventional span extraction models (Chen et al., 2017; Glass et al., 2020; Liu et al., 2020) optimize the probability of predicted start and end positions of the answer spans with ground-truth spans via maximum likelihood estimation (MLE, Wilks et al., 1938). MLE methods promote the probability for the ground-truth positions while suppressing the probability for all other positions. However we hypothesize that, for all those negative instances, the positions that are near the ground-truth should + +![](images/5068558e22b33d6cb2b424485a46c7ca7c4d2357b8d2cca67eb35538f7d76848.jpg) +Figure 2: Overview of VAULT answering the example from Figure 1. The 10th paragraph containing the correct answer is underlined. The span linear layer receives hidden state outputs from all 4096 tokens in the window to create the start and end logits. The paragraph linear layer receives the orange-highlighted [CLS] and markup tokens (e.g. [Para=10]) to predict in which paragraph the answer resides. These logits are then used together to first select the best paragraph (LA) and finally select the best answer within said paragraph (SA). + +be given higher credit than farther distant positions, since the extracted answers will be partially overlapping with the ground-truth. + +To tackle this problem, we follow the intuition from Li et al. (2020) which proposes to promote the probability of generating synonyms using a Gaussian-like distribution for machine translation. We construct the distribution where it has the highest probability at ground-truth positions, and drop the probability exponentially as computed by the distance to the corresponding ground-truth positions. Specifically, for a groundtruth start or end position at $y_{s}$ , where $s \in \{\text{start}, \text{end}\}$ , we use a Gaussian distribution $\mathcal{N}(y_s, \sigma)$ , where the mean is the position $y_{s}$ and variance $\sigma$ is a hyperparameter. We consider the probability density $\varphi(y \mid y_s, \sigma)$ of the Gaussian distribution at each position $y$ as the logit for the corresponding position. We then use the softmax function with temperature $T$ to rescale the logits to get the Gaussian-like distribution $q(y \mid \hat{y}_s)$ for ground-truth distribution at position $y_{s}$ , + +$$ +q (y \mid y _ {s}) = \operatorname {s o f t m a x} (\varphi (y \mid y _ {s}, \sigma) / T). +$$ + +We augment our MLE objective with an additional KL divergence (Kullback and Leibler, 1951) term + +between constructed distribution $q(y \mid y_s)$ and model prediction $p_s(y \mid c), s \in \{\text{start}, \text{end}\}$ , so that we can guide our model to follow the Gaussian-like distribution for partial credit. + +$$ +\begin{array}{l} L _ {D} = K L \left(q (y \mid y _ {s}) \| p _ {s} (y \mid c)\right) \\ = \sum_ {y} q (y \mid y _ {s}) \log p _ {s} (y \mid c) \\ - \sum_ {y} q (y \mid y _ {s}) \log q (y \mid y _ {s}). \\ \end{array} +$$ + +We refer to this final model as VAULT. + +# 4 Experiments + +Datasets: We experiment with two challenging "natural" MRC datasets: NQ (Kwiatkowski et al., 2019) and TechQA (Castelli et al., 2020). We provide a brief summary of the datasets and direct interested readers to the corresponding papers. NQ consists of crowdsourced-annotated full Wikipedia pages which appear in Google search logs with two tasks: the start and end offsets for the short answer (SA) and long answer (LA, eg. paragraph) – if they exist. TechQA is developed from real user questions in the customer support domain where each question is accompanied by 50 documents – + +at most one of which has an answer – with answers significantly longer ( $\sim 3 - 5\mathrm{x}$ ) than standard MRC datasets like SQuAD. We report official F1 scores for each dataset. + +Results on NQ: We train VAULT on NQ - predicting the paragraph and span answers as NQ's LA and SA respectively - and compare against ROBERTA $_{DM}$ : a RoBERTa (Liu et al., 2019) variant of the SOTA document model (DM) (Zheng et al., 2020) using the base variants for a more systematic comparison. Although it may seem fair to include a Longformer DM baseline in our table, doing so would be infeasible (and unwise) due to production resource constraints. We further show the impact of VAULT by providing ablation experiments where its components (GPO and PAPR) are removed. The base LM (Longformer in our experiments) without GPO and PAPR, is implemented in the style of (Alberti et al., 2019b; Chakravarti et al., 2020) where we first predict the SA and then select the enclosing LA. We aim to show that our proposed method provides comparable results to ROBERTA $_{DM}$ while being considerably faster while decoding and displaying improved performance over experiments just using the language model. To do this we consider development set SA and LA F1 (the F1 metrics with respect to the span and paragraph answers respectively) as well as decoding time $t_{\text{decode}}$ (on a V100) as metrics. + +Table 1 shows the results on the NQ dev set. We see VAULT and ROBERTA $_{DM}$ provide comparable F1 performance (precision and recall are shown in the Appendix). However, when it comes to decoding time, we can find VAULT decodes over 16 times faster than ROBERTA $_{DM}$ . We additionally see in the ablation experiments that our enhancements increase both F1 metrics by multiple points, at the expense of some decoding time. In particular we note that the F1 performance of Longformer is not competitive with VAULT. We conclude that VAULT provides the best balance of F1 and decoding time as it is effectively tied on F1 (with ROBERTA $_{DM}$ ) and is only around 20 minutes slower to decode than the quickest model. + +Domain Adaptation: Results on TechQA: Since VAULT has shown to be effective on NQ, we evaluate it on a new domain, TechQA. We compare it against a RoBERTa base model trained with the same hyper-parameters as (Castelli et al., 2020) except we use 11 epochs instead of 20. We chose base instead of large (as is used for the TechQA + +
ModelSA F1LA F1tdecode
ROBERTADM52.270.111h
VAULT51.670.440m
- GPO49.167.641m
- PAPR (Longformer)49.565.622m
+ +baseline) to give a fair comparison since we are using a base PLM for our experiments with VAULT. Similarly, we use RoBERTa rather than BERT as it is closer to Longformer. Having already established the run-time effectiveness of VAULT on NQ, we focus on F1 metrics here, including "has answer" (HA) F1. We consider HA F1 our primary metric as we are exploring paragraph answer extraction in this work and (as previously mentioned) answers in TechQA are much longer than other datasets. We believe that the improvements in HA F1, at least partially, come from GPO. + +Table 1: Comparison of VAULT vs. ROBERTA $_{DM}$ on NQ. We achieve comparable performance while being 16 times faster. + +
ModelF1HA F1
RoBERTa48.67.6
VAULT49.316.1
+ +Table 2: Results on TechQA dev set. VAULT clearly outperforms RoBERTa on both F1 and Has Answer F1. + +Results on TechQA are reported in Table 2. We see that our VAULT model provides an improvement of 0.7 F1 and 8.5 HA F1 (denotes Has Answer); thus showing the effectiveness of our approach. In particular, we see that this approach of imputing a paragraph structure to classify provides a large boost to performance when a non-null answer exists (HA F1). + +# 5 Conclusions + +In this work we introduce and examine a powerful yet simple model for reading comprehension on long texts which we call VAULT, based on the hypothesis that with a large sequence length long answers can be classified effectively without computationally heavy graph-based models. We validate our approach by showing it yields F1 scores competitive with heavier methods at a fraction of the decoding cost on two very different domain benchmark datasets that require reading long texts. + +# References + +Joshua Ainslie, Santiago Ontanón, Chris Alberti, Philip Pham, Anirudh Ravula, and Sumit Sanghai. 2020. + +ETC: encoding long and structured data in transformers. CoRR, abs/2004.08483. +Chris Alberti, Daniel Andor, Emily Pitler, Jacob Devlin, and Michael Collins. 2019a. Synthetic QA corpora generation with roundtrip consistency. CoRR, abs/1906.05416. +Chris Alberti, Kenton Lee, and Michael Collins. 2019b. A BERT baseline for the natural questions. CoRR, abs/1901.08634. +Iz Beltagy, Matthew E. Peters, and Arman Cohan. 2020. Longformer: The long-document transformer. CoRR, abs/2004.05150. +Vittorio Castelli, Rishav Chakravarti, Saswati Dana, Anthony Ferritto, Radu Florian, Martin Franz, Dinesh Garg, Dinesh Khandelwal, J. Scott McCarley, Mike McCawley, Mohamed Nasr, Lin Pan, Cezar Pendus, John F. Pitrelli, Saurabh Pajar, Salim Roukos, Andrzej Sakrajda, Avirup Sil, Rosario Uceda-Sosa, Todd Ward, and Rong Zhang. 2020. The techqa dataset. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5-10, 2020, pages 1269-1278. Association for Computational Linguistics. +Rishav Chakravarti, Anthony Ferritto, Bhavani Iyer, Lin Pan, Radu Florian, Salim Roukos, and Avi Sil. 2020. Towards building a robust industry-scale question answering system. In Proceedings of the 28th International Conference on Computational Linguistics: Industry Track, pages 90-101, Online. International Committee on Computational Linguistics. +Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes. 2017. Reading Wikipedia to answer open-domain questions. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1870-1879, Vancouver, Canada. Association for Computational Linguistics. +Eunsol Choi, Daniel Hewlett, Jakob Uszkoreit, Illia Polosukhin, Alexandre Lacoste, and Jonathan Berant. 2017. Coarse-to-fine question answering for long documents. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 209-220, Vancouver, Canada. Association for Computational Linguistics. +Yiming Cui, Zhipeng Chen, Si Wei, Shijin Wang, Ting Liu, and Guoping Hu. 2017. Attention-over-attention neural networks for reading comprehension. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 593-602, Vancouver, Canada. Association for Computational Linguistics. +Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference + +of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171-4186, Minneapolis, Minnesota. Association for Computational Linguistics. +Michael Glass, Alfio Gliozzo, Rishav Chakravarti, Anthony Ferritto, Lin Pan, G P Shrivatsa Bhargav, Dinesh Garg, and Avi Sil. 2020. Span selection pretraining for question answering. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 2773-2782, Online. Association for Computational Linguistics. +Hongyu Gong, Yelong Shen, Dian Yu, Jianshu Chen, and Dong Yu. 2020. Recurrent chunking mechanisms for long-text machine reading comprehension. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6751-6761, Online. Association for Computational Linguistics. +Mandar Joshi, Eunsol Choi, Daniel Weld, and Luke Zettlemoyer. 2017. TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1601-1611, Vancouver, Canada. Association for Computational Linguistics. +Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020. Dense passage retrieval for open-domain question answering. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6769-6781, Online. Association for Computational Linguistics. +Solomon Kullback and Richard A Leibler. 1951. On information and sufficiency. The annals of mathematical statistics, 22(1):79-86. +Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, Kristina Toutanova, Llion Jones, Matthew Kelcey, Ming-Wei Chang, Andrew M. Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov. 2019. Natural questions: A benchmark for question answering research. Transactions of the Association for Computational Linguistics, 7:452-466. +Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2019. Albert: A lite bert for self-supervised learning of language representations. +Kenton Lee, Ming-Wei Chang, and Kristina Toutanova. 2019. Latent retrieval for weakly supervised open domain question answering. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 6086–6096, Florence, Italy. Association for Computational Linguistics. + +Zuchao Li, Rui Wang, Kehai Chen, Masao Utiyama, Eiichiro Sumita, Zhuosheng Zhang, and Hai Zhao. 2020. Data-dependent gaussian prior objective for language generation. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. OpenReview.net. +Dayiheng Liu, Yeyun Gong, Jie Fu, Yu Yan, Jiusheng Chen, Daxin Jiang, Jiancheng Lv, and Nan Duan. 2020. RikiNet: Reading Wikipedia pages for natural question answering. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6762-6771, Online. Association for Computational Linguistics. +Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized BERT pretraining approach. CoRR, abs/1907.11692. +J. William Murdock, Lin Pan, Chung-Wei Hang, Mary Swift, Zhiguo Wang, Chris Nolan, Prathyusha Peddi, Nisarga Markandaiah, Eunyoung Ha, Kazi Hasan, and et al. 2018. Engineered ai still matters for question answering. Advances in Cognitive Systems, 6:140-158. +Pranav Rajpurkar, Robin Jia, and Percy Liang. 2018. Know what you don't know: Unanswerable questions for SQuAD. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 784-789, Melbourne, Australia. Association for Computational Linguistics. +Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016. SQuAD: 100,000+ questions for machine comprehension of text. EMNLP. +Ryota Tanaka, Kyosuke Nishida, and Sen Yoshida. 2021. Visualmrc: Machine reading comprehension on document images. CoRR, abs/2101.11272. +Petar Velicković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2018. Graph attention networks. In International Conference on Learning Representations. +Johannes Welbl, Pontus Stenetorp, and Sebastian Riedel. 2018. Constructing datasets for multi-hop reading comprehension across documents. Transactions of the Association for Computational Linguistics, 6:287-302. +SS Wilks et al. 1938. The large-sample distribution of the likelihood ratio for testing composite hypotheses. The Annals of Mathematical Statistics, 9(1):60-62. +Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumont, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, + +Quentin Lhoest, and Alexander Rush. 2020. Transformers: State-of-the-art natural language processing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 38-45, Online. Association for Computational Linguistics. +Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D. Manning. 2018. HotpotQA: A dataset for diverse, explainable multi-hop question answering. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2369-2380, Brussels, Belgium. Association for Computational Linguistics. +Bo Zheng, Haoyang Wen, Yaobo Liang, Nan Duan, Wanxiang Che, Daxin Jiang, Ming Zhou, and Ting Liu. 2020. Document modeling with graph attention networks for multi-grained machine reading comprehension. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6708-6718, Online. Association for Computational Linguistics. +Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, and Maosong Sun. 2018. Graph neural networks: A review of methods and applications. CoRR, abs/1812.08434. + +# A Additional Experimental Results + +For interested readers we further show precision and recall numbers for the NQ experiments in Table 3. + +# B Implementation Details + +# B.1 NQ + +All models for this work are implemented in (Wolf et al., 2020). We use the following hyperparameters for VAULT when finetuning on NQ: sequence length 4096, doc stride 2048 (Ainslie et al., 2020), negative instance subsampling rates (has answer/no answer) 0.02/0.08, learning rate 5e-5, and 4 epochs of training. + +# B.2 TechQA + +While TechQA does provide full HTML for its Technotes, the answers are annotated with respect to the cleaned plaintext. Therefore to determine paragraph breaks for VAULT we split on the "\n\n" token "C" in the vocabulary. By imputing paragraph answers in this way, we are then able to predict the paragraph answer and then a contained span answer. + +# C Example Analysis + +We examine additional examples in Figure 3 to provide insight on the improvements of VAULT. We + +
ModelSA F1SA PSA RLA F1LA PLA R
ROBERTADM52.257.248.070.169.470.9
VAULT51.661.544.470.469.571.4
- GPO49.157.642.767.667.068.1
- PAPR (Longformer)49.556.444.265.662.469.3
+ +Table 3: Comparison of VAULT vs. ROBERTA ${}_{DM}$ on NQ with precision (P) and recall (R) statistics. + +compare the correct answers produced by VAULT with the incorrect answers produced by the ablated model from the last row of Table 3 (NQ) and Roberta baseline from the first row of Table 2 (TechQA). + +In the first example the gold SA is null, however there is a gold LA. This indicates that there is no short span which answers the question: the correct answer here is an entire paragraph. This does not confuse VAULT which is able to identify the correct answer directly. However the ablated model which attempts to predict SA first struggles here – predicting the incorrect LA – as there is no gold SA. + +In the second example we see that in this Technote both the correct and incorrect answers are single sentence paragraphs surrounded by paragraph breaks. Our VAULT is able to identify the correct paragraph using our imputed structure and select the correct answer – whereas the Roberta baseline selects a nearby but incorrect answer. + +# Example A1 (NQ) + +Question: why did government sponsored surveys and land acts encourage migration to the west + +Wikipedia Page: Homestead Acts + +# Text: ... + +An extension of the Homestead Principle in law, the Homestead Acts were an expression of the "Free Soil" policy of Northerners who wanted individual farmers to own and operate their own farms, as opposed to Southern slave-owners who wanted to buy up large tracts of land and use slave labor, thereby shutting out free white men. + +The first of the acts, the Homestead Act of 1862, opened up millions of acres. Any adult who had never taken up arms against the U.S. government could apply. Women and immigrants who had applied for citizenship were eligible. The 1866 Act explicitly included black Americans and encouraged them to participate, but rampant discrimination slowed black gains. Historian Michael Lanza argues that while the 1866 law pack was not as beneficial as it might have been, it was part of the reason that by 1900 one fourth of all Southern black farmers owned their own farms. [1] + +··· + +# Example A2 (TechQA) + +Question: Are there any probes that can connect to the Nokia NSP EPC v17.9 and Nokia NSP RAN v17.3 using JMS/HTTP? + +Text: release notice; downloads; nco-p-nokia-nfmp; Probe for Nokia Network Functions Manager for Packet NEWS + +# ABSTRACT + +This new probe will be ready for downloading on July 20, 2017. + +# CONTENT + +This probe is written to support Nokia Network Functions Manager for Packet release 17.3. + +You can download the package you require from the IBM Passport Advantage website: + +www-01.ibm.com... + +Figure 3: Additional Examples of questions in the NQ and TechQA datasets. VAULT's correct answer is shown in green, incorrect baseline in red. (A1) The correct answer is a paragraph LA; only VAULT identifies the correct LA directly even though the gold SA is null. (A2) VAULT identifies the correct "paragraph" answer. \ No newline at end of file diff --git a/vaultvariableunifiedlongtextrepresentationformachinereadingcomprehension/images.zip b/vaultvariableunifiedlongtextrepresentationformachinereadingcomprehension/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..c00fe3ea823d25e4c40ff69e6d25b573b5497a83 --- /dev/null +++ b/vaultvariableunifiedlongtextrepresentationformachinereadingcomprehension/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:403dc61e8342c24134e0455c69c20a37374457db3e296243ff5e58a4ff77c34e +size 203327 diff --git a/vaultvariableunifiedlongtextrepresentationformachinereadingcomprehension/layout.json b/vaultvariableunifiedlongtextrepresentationformachinereadingcomprehension/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..8f3289d31c8112d9c37a3d73caa3a1b54c788e94 --- /dev/null +++ b/vaultvariableunifiedlongtextrepresentationformachinereadingcomprehension/layout.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b2db9bdf656fb6d3d23f6f4b9af7184f09477d3d9d3671b72274803a7b74317b +size 237611 diff --git a/videoparagraphcaptioningasatextsummarizationtask/9535ed81-ff3a-48fb-a11c-0930a91c8737_content_list.json b/videoparagraphcaptioningasatextsummarizationtask/9535ed81-ff3a-48fb-a11c-0930a91c8737_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..b8a922ab9fbdacb084c0272605e11e602cf86160 --- /dev/null +++ b/videoparagraphcaptioningasatextsummarizationtask/9535ed81-ff3a-48fb-a11c-0930a91c8737_content_list.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a72951f94131904574f4b3165b22e4bddf8ce4850dfb7a10b0da94b649bfec3a +size 39836 diff --git a/videoparagraphcaptioningasatextsummarizationtask/9535ed81-ff3a-48fb-a11c-0930a91c8737_model.json b/videoparagraphcaptioningasatextsummarizationtask/9535ed81-ff3a-48fb-a11c-0930a91c8737_model.json new file mode 100644 index 0000000000000000000000000000000000000000..0c38c86d2adc1f626864c30e7d2386e2f66ffb43 --- /dev/null +++ b/videoparagraphcaptioningasatextsummarizationtask/9535ed81-ff3a-48fb-a11c-0930a91c8737_model.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4111a4de8b1803965e0f43a9c762426cf2f76f1a19b1abd42e9d197d8ba40b01 +size 48956 diff --git a/videoparagraphcaptioningasatextsummarizationtask/9535ed81-ff3a-48fb-a11c-0930a91c8737_origin.pdf b/videoparagraphcaptioningasatextsummarizationtask/9535ed81-ff3a-48fb-a11c-0930a91c8737_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..0bc077742398d8e71a83f9a02250b68ad235a3f8 --- /dev/null +++ b/videoparagraphcaptioningasatextsummarizationtask/9535ed81-ff3a-48fb-a11c-0930a91c8737_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4ce72a9148379be50dd4cd46ecab48f76fd7bcacbe45d44a4b7dfb70d4d4fc25 +size 1642938 diff --git a/videoparagraphcaptioningasatextsummarizationtask/full.md b/videoparagraphcaptioningasatextsummarizationtask/full.md new file mode 100644 index 0000000000000000000000000000000000000000..4e1dea348eeaa544184806ead870e89d7ed8d18c --- /dev/null +++ b/videoparagraphcaptioningasatextsummarizationtask/full.md @@ -0,0 +1,185 @@ +# Video Paragraph Captioning as a Text Summarization Task + +Hui Liu, Xiaojun Wan + +Wangxuan Institute of Computer Technology, Peking University + +The MOE Key Laboratory of Computational Linguistics, Peking University + +{xinkeliuhui, wanxiao jun}@pku.edu.cn + +# Abstract + +Video paragraph captioning aims to generate a set of coherent sentences to describe a video that contains several events. Most previous methods simplify this task by using ground-truth event segments. In this work, we propose a novel framework by taking this task as a text summarization task. We first generate lots of sentence-level captions focusing on different video clips and then summarize these captions to obtain the final paragraph caption. Our method does not depend on ground-truth event segments. Experiments on two popular datasets ActivityNet Captions and YouCookII demonstrate the advantages of our new framework. On the ActivityNet dataset, our method even outperforms some previous methods using ground-truth event segment labels. + +# 1 Introduction + +Video captioning, the task of describing the content of a video in natural language, is a popular task both in computer vision and natural language processing. In the beginning, researchers try to generate sentence-level captions for short video clips (Venugopalan et al., 2015). Krishna et al. (2017) propose the task of dense video captioning. The system needs to detect event segments first and then generate captions. Park et al. (2019) propose the task of video paragraph captioning: they use ground-truth event segments and focus on generating coherent paragraphs. Lei et al. (2020) follow the task setting and propose a recurrent transformer model that can generate more coherent and less repetitive paragraphs. Considering the ground-truth event segments are often unavailable in practice, our goal is to generate paragraph captions without ground-truth segments. + +The conventional framework of video paragraph captioning is shown in Figure 1a. Given an untrimmed video, an Event Detection module out + +![](images/03cc4efc8c75d49bf426065387bcc40d85ccd2243734c55fde8337985d4de3e1.jpg) +(a) Conventional Framework + +![](images/2166824c23adcdcc77f9e0b7f9cac680c99bc543735cf42590700e9d5de0f9a1.jpg) +(b) Our Framework +Figure 1: Comparison between conventional framework and ours. + +puts a set of non-redundant event segments. The Event Captioning module generates captions for these segments. The works of (Park et al., 2019; Zhou et al., 2019; Lei et al., 2020) use ground-truth event segments and focus on the Event Captioning module. Zhou et al. (2019) use extra human-annotated bounding boxes as supervision. (Sah et al., 2017; Zhou et al., 2018; Mun et al., 2019) use predicted event segments and generate captions based on them. Sah et al. (2017) also summarizes these captions to generate a paragraph. The above methods heavily depend on accurate event segments. According to previous works (Zhou et al., 2018; Mun et al., 2019), the performance of the Event Detection module is not so good, making it a performance bottleneck. To tackle this problem, we propose a novel framework VPCSum as shown in Figure 1b. For a given video, we first extract dense event segment candidates (we call proposals), and a Proposal Captioning module is used to generate proposal captions. Then we treat video paragraph captioning as a text summarization task to obtain the final summary (paragraph caption). + +In this work, we only consider extractive summarization, where the paragraph caption is composed by selecting from proposal captions. We conduct experiments on two popular datasets ActivityNet + +Captions and YouCookII. The results demonstrate the advantages of our framework. On the ActivityNet Captions dataset, our method even outperforms some previous methods using ground-truth event segment labels. + +# 2 Our VPCSum Method + +As illustrated in Figure 1b, our framework has three modules. Proposal Extraction: it extracts dense proposals for a video; Proposal Captioning: it generates captions for extracted proposals; Caption Summarization: it summarizes the generated proposal captions to obtain the video paragraph caption. We will introduce each module next. + +# 2.1 Proposal Extraction + +For proposal extraction, we use the BMN model (Lin et al., 2019), a popular model for temporal action proposal generation. It can extract complete and accurate proposals. We extract the top 100 proposals for each video. + +# 2.2 Proposal Captioning + +For proposal captioning, we choose the TSRMRNN model (Wang et al., 2020) for ActivityNet Captions and VTransformer model (Lei et al., 2020) for YouCookII according to proposal captioning performance. We believe that if we choose a better sentence-level captioning model, the performance can be further improved. + +# 2.3 Caption Summarization + +![](images/66b0d4b7b843052343af93b1bbcb13006a4ff2d532f77d2d46d80f92af8f13db.jpg) +Figure 2: Architecture of the caption summarization model. + +The caption summarization module summarizes proposal captions to generate the final video paragraph caption. In this work, we focus on extractive + +summulation. The architecture of our summarization model is illustrated in Figure 2. We first sort the proposal captions according to the proposal start time and add special [CLS] and [SEP] tokens to the beginning and end of each caption. We use the summation of token embeddings, segment embeddings, and position embeddings to represent each word. The input representations are fed into a pre-trained BERT model (Devlin et al., 2018), after which we obtain the contextual token representations. We use the contextual vectors of [CLS]s to represent each caption and feed them into stacked transformer layers (Vaswani et al., 2017). We use a sigmoid layer to compute the score of each caption: + +$$ +x _ {i} = \sigma \left(W h _ {i} ^ {L} + b\right) \tag {1} +$$ + +where $W$ and $b$ are trainable parameters, $h_i^L$ is the vector for caption $i$ from the top transformer layer. + +For extractive summarization, we need to annotate each sentence according to the gold summary as our training target. Many researchers use a greedy algorithm (Nallapati et al., 2016), sentences are selected one by one to maximize the ROUGE score against the gold summary. The selected sentences are labeled 1 while others are labeled 0 (hard-label). In our task, we find a more effective soft-label annotation method. We label caption $c_{i}$ with the max ROUGE score against gold captions and use binary cross-entropy as our loss function: + +$$ +y _ {i} = \max _ {g _ {j} \in g o l d} R O U G E \left(c _ {i}, g _ {j}\right) \tag {2} +$$ + +$$ +\mathcal {L} = - \sum_ {i} \left(y _ {i} \log x _ {i} + \left(1 - y _ {i}\right) \log \left(1 - x _ {i}\right)\right) \tag {3} +$$ + +where $g_{j}$ is the $j$ -th gold caption. + +# 2.4 Leverage Visual Information + +The above caption summarization module assigns each proposal caption a predicted score, indicating how likely it appears in the final paragraph caption. The predicted score only depends on text information. To leverage visual information, we need a "visual summarization" module, which gives a visually weighting score to each proposal. The ESGN model (Mun et al., 2019) seems a good choice for us. It uses a pointer network to select events from proposals and assigns a visually weighting score for each proposal. We use this model to compute the visually weighting score. + +Now we can extract the final paragraph caption. The final score of each proposal caption is a + +weighted sum of the textually weighting score $s_{txt}$ and the visually weighting score $s_{vis}$ : + +$$ +\operatorname {s c o r e} (i) = s _ {t x t, i} + \lambda s _ {v i s, i} \tag {4} +$$ + +where $\lambda$ is a hyper-parameter tuned on validation set. We select captions according to score(i) and use Trigram Blocking to reduce redundancy, as in Liu and Lapata (2019). + +# 3 Experiments + +# 3.1 Datasets + +We conduct experiments on ActivityNet Captions (Krishna et al., 2017) and YouCookII (Zhou et al., 2017). ActivityNet Captions contains 10,009 videos in train set, 4,917 videos in val set. Each video has 3.65 event segments on average. Following (Lei et al., 2020), the original val set is split into ae-val with 2,460 videos for validation and ae-test with 2,457 videos for test. YouCookII contains 1,333 videos in train set, 457 videos in val set. Each video has 7.70 event segments on average. + +# 3.2 Evaluation Metrics + +Following (Lei et al., 2020; Park et al., 2019), we evaluate the captioning performance at paragraph level. We report standard caption metrics, including BLEU@4 (Papineni et al., 2002), METEOR (Denkowski and Lavie, 2014), CIDEr (Vedantam et al., 2015). We also evaluate repetition using R@4 (Xiong et al., 2018). We use the scripts provided by (Lei et al., 2020) for evaluation1. + +# 3.3 Implementation Details + +For video preprocessing, we use appearance and optical flow features provided by Zhou et al. (2018). For BMN model and captioning models, we use the same hyperparameters suggested by the authors. For ESGN model, we use a transformer encoder instead of an RNN encoder, with hidden size set to 512, number of heads set to 8, number of layers set to 3. For our caption summarization model, we use the base BERT model, 2 stacked transformer layers with hidden size set to 768, number of heads set to 8. We set max input length to 1,700, batch size to 10, $\lambda$ to 1 for ActivityNet Captions and max input length to 1,000, batch size to 1, $\lambda$ to 1 for YouCookII. Warmup steps are set to step num of 1 epoch. We use Adam optimizer with an initial learning rate of $6e - 4$ . + +# 3.4 Baselines and Results + +We compare our VPCSum model with the following baselines. Soft-NMS: it uses Soft-NMS (Bodla et al., 2017) to select event segments from BMN proposals, and uses the proposal captioning model to generate captions; ESGN: similar to Soft-NMS, but it uses ESGN model (Mun et al., 2019) to select event segments from BMN proposals; V-Trans: a Vanilla Transformer model, proposed by (Zhou et al., 2018); Trans-XL: a Transformer-XL model, proposed by (Lei et al., 2020); MART: a recurrent transformer model (Lei et al., 2020); COOT: it uses pretrained features to train MART model (Ging et al., 2020). Originally, the last four models deal with ground-truth event segments. For fair comparison, we also test them with predicted event segments generated by ESGN model $^{2}$ . + +
ModelsB@4MCR@4↓
Soft-NMS10.3314.9322.5810.17
ESGN10.3815.7421.856.51
V-Trans9.8915.1120.957.04
Trans-XL10.3614.8920.737.45
MART10.1314.9420.166.09
COOT9.8514.6721.837.15
VPCSum10.8915.8424.331.54
V-trans*9.3115.5421.337.45
Trans-XL*10.2514.9121.718.79
MART*9.7815.5722.165.44
COOT*10.8515.9928.196.64
+ +Table 1: Comparison with baselines on ActivityNet Captions ae-test split. * means the model uses ground-truth event segments. We report BLEU@4 (B@4), METEOR (M), CIDEr (C), Repetition (R@4). + +Tables 1 and 2 show the results on ActivityNet Captions and YouCookII. We can observe that on the ActivityNet Captions, our model VPCSum within the new framework can generate better paragraph captions with higher Bleu@4, METEOR, and CIDEr and lower repetition score R@4, even outperforming V-trans*, Trans-XL*, MART* models using ground-truth event segments on every metric. On the YouCookII dataset, our model outperforms the models in the same setting but is inferior to the models using ground-truth segments. This may be because YouCookII has more segments + +
ModelsB@4MCR@4↓
Soft-NMS5.5813.6718.184.94
ESGN5.3613.3717.012.82
V-Trans5.3513.3716.882.85
Trans-XL4.7812.6714.243.20
MART5.6113.4416.564.63
COOT5.9614.2119.675.99
VPCSum6.1415.1123.920.65
V-trans*7.6215.6532.267.83
Trans-XL*6.5614.7626.356.30
MART*8.0015.9035.744.39
COOT*9.4418.1746.066.30
+ +(7.70 vs 3.65) than ActivityNet Captions. + +# 3.5 Ablation Study + +Table 3 shows the ablation study on ActivityNet Captions. Compared to our full model (Full), the traditional extractive summarization annotation method (Hard-label) is not suitable for our task. If we set $\lambda$ in Eq.(4) to 0 (w/o vis), the model loses useful visual information and performs not well. If we remove Trigram Blocking (w/o tri-blk), the performance also degrades and repetition becomes a problem (R@4 increases to 7.91). To verify the role of pretrained BERT model, we retrain our VPCSum without BERT pretrained weights (w/o pretrain). We can see that BERT pretrained weights are not the major factor to the final performance. We also replace our summarization model with unsupervised methods LexRank (Erkan and Radev, 2004) and LSA(Steinberger and Jezek, 2004). The results show that simple unsupervised summarization methods cannot handle our data well and supervised training is necessary. + +Table 2: Comparison with baselines on YouCookII val split. + +
ModelsB@4MCR@4↓
Full10.8915.8424.331.54
Hard-label10.2914.9921.711.19
w/o vis10.6815.7823.341.36
w/o tri-blk10.4615.6121.407.91
w/o pretrain10.8415.8124.001.55
LexRank7.7813.6514.1926.51
LSA7.2414.4812.4328.14
+ +# 3.6 Qualitative Results + +![](images/8b90d9c2bac1a5da6caf0ecbbfbb2221514e03e25860d4f72ea01bdd1bf84d59.jpg) +Figure 3: An example from ActivityNet Captions. + +Ground Truth: A girl jumps onto a balance beam. She does a gymnastics routine on the balance beam. She does a flip off the balance beam and lands on a mat. + +MART: A gymnast is seen standing ready with her arms up and leads into her performing a gymnastics routine. She continues performing several flips and tricks and tricks and ends with her jumping down and walking away. She continues her routine and ends with her jumping down and jumping down and walking away. + +MART*: A gymnast is seen standing before a beam and begins performing a gymnastics routine. The girl then performs a routine on the beam and ends with her jumping down and jumping down and jumping. The girl jumps off the beam and lands on the mat and jumps off the beam. + +VPCSum: A gymnast is seen standing ready with her arms up and begins to do a routine. She does a gymnastics routine on the beam. She dismounts and lands on the mat. + +We show an example in Figure 3 with paragraph captions generated by MART, MART* and our VPCSum model. Compared to other models, our model can generate more clear and correct sentences with less redundancy. The generated paragraph of our model can better describe the process of the whole event. + +# 3.7 Human Evaluation + +Table 3: Model ablation study on ActivityNet Captions ae-test split. + +
OursMARTOursMART*
rel.56.0%†44.0%†52.7%47.3%
div.56.7%†43.3%†56.7%†43.3%†
+ +Table 4: Human evaluation results. Statistically significant differences $(p < 0.05)$ are marked with $\dagger$ + +We also conduct a human evaluation on randomly sampled 50 videos from the ActivityNet Captions val set. The annotators are asked to choose the better caption from two models in two aspects: relevance (how related is the caption to the video content) and diversity (how diverse is the generated text). We compare our VPCSum model with MART and MART* respectively. We have 17 college students as our annotators. Each video is judged by 3 annotators. We show the results of the pairwise experiments in Table 4. Our VPCSum model performs better in relevance and diversity, + +and more people choose the caption of our model as the better one. + +# 4 Conclusion + +In this work, we view the task of video paragraph captioning as a text summarization task and propose a novel framework VPCSum. It allows us to use text summarization techniques to handle this challenging task. Experimental results on two popular datasets show the advantages of our model. In the future, we will explore using abstractive summarization methods to generate better video paragraph captions. + +# Acknowledgments + +This work was supported by National Natural Science Foundation of China (61772036), MSRA Collaboration Research Project (FY20-Research-Sponsorship-266) and Key Laboratory of Science, Technology and Standard in Press Industry (Key Laboratory of Intelligent Press Media Technology). We appreciate the anonymous reviewers for their helpful comments. Xiaojun Wan is the corresponding author. + +# References + +Navaneeth Bodla, Bharat Singh, Rama Chellappa, and Larry S Davis. 2017. Soft-nms-improving object detection with one line of code. In Proceedings of the IEEE international conference on computer vision, pages 5561-5569. +Michael Denkowski and Alon Lavie. 2014. Meteor universal: Language specific translation evaluation for any target language. In Proceedings of the ninth workshop on statistical machine translation, pages 376-380. +Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805. +Günes Erkan and Dragomir R Radev. 2004. Lexrank: Graph-based lexical centrality as salience in text summarization. Journal of artificial intelligence research, 22:457-479. +Simon Ging, Mohammadreza Zolfaghari, Hamed Pirsiavash, and Thomas Brox. 2020. Coot: Cooperative hierarchical transformer for video-text representation learning. arXiv preprint arXiv:2011.00597. +Ranjay Krishna, Kenji Hata, Frederic Ren, Li Fei-Fei, and Juan Carlos Niebles. 2017. Dense-capitaling + +events in videos. In Proceedings of the IEEE international conference on computer vision, pages 706-715. +Jie Lei, Liwei Wang, Yelong Shen, Dong Yu, Tamara L Berg, and Mohit Bansal. 2020. Mart: Memory-augmented recurrent transformer for coherent video paragraph captioning. arXiv preprint arXiv:2005.05402. +Tianwei Lin, Xiao Liu, Xin Li, Errui Ding, and Shilei Wen. 2019. Bmn: Boundary-matching network for temporal action proposal generation. In Proceedings of the IEEE International Conference on Computer Vision, pages 3889-3898. +Yang Liu and Mirella Lapata. 2019. Text summarization with pretrained encoders. arXiv preprint arXiv:1908.08345. +Jonghwan Mun, Linjie Yang, Zhou Ren, Ning Xu, and Bohyung Han. 2019. Streamlined dense video captioning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 6588-6597. +Ramesh Nallapati, Feifei Zhai, and Bowen Zhou. 2016. Summarunner: A recurrent neural network based sequence model for extractive summarization of documents. arXiv preprint arXiv:1611.04230. +Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pages 311-318. +Jae Sung Park, Marcus Rohrbach, Trevor Darrell, and Anna Rohrbach. 2019. Adversarial inference for multi-sentence video description. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 6598-6608. +Shagan Sah, Sourabh Kulhare, Allison Gray, Subhashini Venugopalan, Emily Prud'Hommeaux, and Raymond Ptucha. 2017. Semantic text summarization of long videos. In 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), pages 989-997. IEEE. +Josef Steinberger and Karel Jezek. 2004. Using latent semantic analysis in text summarization and summary evaluation. Proc. ISIM, 4:93-100. +Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in neural information processing systems, pages 5998-6008. +Ramakrishna Vedantam, C Lawrence Zitnick, and Devi Parikh. 2015. Cider: Consensus-based image description evaluation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4566-4575. + +Subhashini Venugopalan, Marcus Rohrbach, Jeffrey Donahue, Raymond Mooney, Trevor Darrell, and Kate Saenko. 2015. Sequence to sequence-video to text. In Proceedings of the IEEE international conference on computer vision, pages 4534-4542. +Teng Wang, Huicheng Zheng, and Mingjing Yu. 2020. Dense-captioning events in videos: Sysu submission to activitynet challenge 2020. arXiv preprint arXiv:2006.11693. +Yilei Xiong, Bo Dai, and Dahua Lin. 2018. Move forward and tell: A progressive generator of video descriptions. In Proceedings of the European Conference on Computer Vision (ECCV), pages 468-483. +Luowei Zhou, Yannis Kalantidis, Xinlei Chen, Jason J Corso, and Marcus Rohrbach. 2019. Grounded video description. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 6578-6587. +Luowei Zhou, Chenliang Xu, and Jason J Corso. 2017. Towards automatic learning of procedures from web instructional videos. arXiv preprint arXiv:1703.09788. +Luowei Zhou, Yingbo Zhou, Jason J Corso, Richard Socher, and Caiming Xiong. 2018. End-to-end dense video captioning with masked transformer. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 8739-8748. \ No newline at end of file diff --git a/videoparagraphcaptioningasatextsummarizationtask/images.zip b/videoparagraphcaptioningasatextsummarizationtask/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..e2db385e4fa4c69eaabc039feb9ece0c6c4bf739 --- /dev/null +++ b/videoparagraphcaptioningasatextsummarizationtask/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a3fb088578891f7dfdb6c1bb14fca39b3eb3dfdb6ce380e4c8f129b5ff1eabb6 +size 245073 diff --git a/videoparagraphcaptioningasatextsummarizationtask/layout.json b/videoparagraphcaptioningasatextsummarizationtask/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..3b7a59cbdd1ac9c5f53f61c96216353c14f752dd --- /dev/null +++ b/videoparagraphcaptioningasatextsummarizationtask/layout.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:541433c945709e807b934bb4e80bd5a691bde606c4eea1e145b5c031715162e1 +size 179523 diff --git a/weaklysupervisedmethodsforsuicideriskassessmentroleofrelateddomains/0e170f66-197e-433b-80d3-aa8d63d400d1_content_list.json b/weaklysupervisedmethodsforsuicideriskassessmentroleofrelateddomains/0e170f66-197e-433b-80d3-aa8d63d400d1_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..0947ee8f0197dbc20961a46964ac84ad6f9633b3 --- /dev/null +++ b/weaklysupervisedmethodsforsuicideriskassessmentroleofrelateddomains/0e170f66-197e-433b-80d3-aa8d63d400d1_content_list.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:119dbf802c71601821574d116b20ae232a02b475be5baf4dfcf7bb96fccc9279 +size 50531 diff --git a/weaklysupervisedmethodsforsuicideriskassessmentroleofrelateddomains/0e170f66-197e-433b-80d3-aa8d63d400d1_model.json b/weaklysupervisedmethodsforsuicideriskassessmentroleofrelateddomains/0e170f66-197e-433b-80d3-aa8d63d400d1_model.json new file mode 100644 index 0000000000000000000000000000000000000000..742e7cf460a27ceaa837e42611018a0fe477fe1e --- /dev/null +++ b/weaklysupervisedmethodsforsuicideriskassessmentroleofrelateddomains/0e170f66-197e-433b-80d3-aa8d63d400d1_model.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1a1afee2cf8b0e070bf0dcf3d5f5a0def1afc594281e7b844016587e1513a3c8 +size 59352 diff --git a/weaklysupervisedmethodsforsuicideriskassessmentroleofrelateddomains/0e170f66-197e-433b-80d3-aa8d63d400d1_origin.pdf b/weaklysupervisedmethodsforsuicideriskassessmentroleofrelateddomains/0e170f66-197e-433b-80d3-aa8d63d400d1_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..e6621a0e4399f278648a8d416e849d4e3d2961ce --- /dev/null +++ b/weaklysupervisedmethodsforsuicideriskassessmentroleofrelateddomains/0e170f66-197e-433b-80d3-aa8d63d400d1_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:80c7041729c089526b801de75035a403e3d371707189b79ef831fa70809df7ee +size 387853 diff --git a/weaklysupervisedmethodsforsuicideriskassessmentroleofrelateddomains/full.md b/weaklysupervisedmethodsforsuicideriskassessmentroleofrelateddomains/full.md new file mode 100644 index 0000000000000000000000000000000000000000..fdde756c34b62222593c9f37b870b5ad3f6ec528 --- /dev/null +++ b/weaklysupervisedmethodsforsuicideriskassessmentroleofrelateddomains/full.md @@ -0,0 +1,187 @@ +# Weakly-Supervised Methods for Suicide Risk Assessment: Role of Related Domains + +Chenghao Yang $^{1}$ , Yudong Zhang $^{1}$ , Smaranda Muresan $^{1,2}$ +Department of Computer Science, Columbia University $^{1}$ +Data Science Institute, Columbia University $^{2}$ +yangalan1996@gmail.com +{zhang.yudong, smara}@columbia.edu + +# Abstract + +Social media has become a valuable resource for the study of suicidal ideation and the assessment of suicide risk. Among social media platforms, Reddit has emerged as the most promising one due to its anonymity and its focus on topic-based communities (subreddits) that can be indicative of someone's state of mind or interest regarding mental health disorders such as r/SuicideWatch, r/Anxiety, r/depression. A challenge for previous work on suicide risk assessment has been the small amount of labeled data. We propose an empirical investigation into several classes of weakly-supervised approaches, and show that using pseudo-labeling based on related issues around mental health (e.g., anxiety, depression) helps improve model performance for suicide risk assessment. + +# 1 Introduction + +Suicide has been identified as one of the leading causes of deaths and approximately $1.5\%$ of people die by suicide every year (WHO et al., 2016; Fazel and Runeson, 2020). Despite years o clinical research on suicide, researomers have concluded that suicide cannot be predicted using the standard clinical practice of asking patients about their suicidal thoughts (McHugh et al., 2019). Recently, Coppersmith et al. (2018) and Nock et al. (2019) discuss the opportunities of using social media combined with natural language processing (NLP) techniques to complement traditional clinical records and help in suicide risk analysis and early suicide intervention. + +To facilitate further research on automatic suicide risk assessment, Zirikly et al. (2019) proposed a shared task, where they collected user data from r/SuicideWatch subreddit and annotated it with user-level suicide risk: no-risk, low-risk, medium-risk and high-risk. By comparing the results of the + +participating teams in this shared task, Zirikly et al. (2019) conclude that one of the major challenges comes from the insufficient data for intermediate suicide risk levels (i.e., low risk and medium risk) rather than extreme risk levels (i.e., no risk and high risk). Matero et al. (2019) find that using a dual BERT-LSTM-Attention model to separately extract information from both SuicideWatch and Non-SuicideWatch posts together with feature engineering that includes emotion features, personality scores, user's anxiety and depression scores are important for model performance. + +In this paper, instead of feature engineering or complex model architectures, we explore whether weakly supervised methods and data augmentation techniques based on clinical psychology research can help improve model performance. We explore several weakly-supervised methods, and show that a simple approach based on insights from clinical psychology research (O'Connor and Nock, 2014) obtains the best performance. This model uses pseudo-labeling (PL) on data from the subreddits r/Anxiety and r/depression, which are considered important risk factors for suicide. We also present a potential application of our model for studying the suicide risk among people who use drugs, opening the door for using NLP methods to deepen our understanding between opioid use disorder (OUD) and mental health. The code for this paper can be found at https://github.com/yangalan123/WM-SRA. + +# 2 Methods + +We focus on Task A from the CLPsych 2019 shared task "Predicting the Degree of Suicide Risk in Reddit Posts" (Zirikly et al., 2019). The goal of the task is to predict the user-level suicide risk category based on their posts in the r/SuicideWatch subReddit. Specifically, a user $u_{i}$ is associated with a col + +lection of $n(i)$ posts $C_i = \{x_{i,1}, x_{i,2}, \ldots, x_{i,n(i)}\}$ , where each post $x_{i,k} (1 \leq k \leq n)$ has $m(i, k)$ sentences $x_{i,k} = [s_{ik,1}, s_{ik,2}, \ldots, s_{ik,m(i,k)}]$ . We need to predict $y_i \in \{a, b, c, d\}$ using $C_i$ , where $a, b, c, d$ represent no-risk, low-risk, medium-risk and high-risk, respectively. In the original dataset, there are 496 users in the training set and 125 users in the test sets. We further split 100 users from the training set to create the validation set. The sizes for the train-valid/test sets are 746, 173, and 186 respectively. + +Data Pre-processing Following the advice in (Zirikly et al., 2019), we replace all human names and URLs in the Reddit posts with special tokens "PERSON" and "URL", respectively. We also remove punctuation and stop words besides lowercasing. Due to the limitation of GPU memory, we split those large posts to be passages with no more than 128 words1 and make sure that the split point is not in the middle of the sentence2. Such passages are treated as separate posts. + +Model Architecture Our architecture is a BERT (Devlin et al., 2019) model. We also experimented with other state-of-the-art pre-trained language models (PLMs), including RoBERTa (Liu et al., 2019) and XLNET (Yang et al., 2019), but found BERT to work the best and thus consider it as our baseline architecture (more details can be found in Appendix A). Each post $x_{i,k}$ is fed into BERT (Devlin et al., 2019) and we get post embedding $\vec{e}_{i,k} = \mathrm{BERT}(x_{i,k})$ . Then we do simple mean-pooling to obtain the user embedding $\vec{u}_i = \frac{\sum_{k=1}^{n(i)} \vec{e}_{i,k}}{n(i)}$ . Finally, we feed $\vec{u}_i$ to a fully-connected layer and use the Softmax layer to predict the risk level probability $\tilde{P}(y_i | C_i)$ . The label with the largest probability is picked as the final prediction $\hat{y}_i$ . For training, the cross entropy loss $\mathcal{L}_{\mathrm{clf}}$ is applied to optimize our model. + +# 2.1 Weakly-supervised Methods + +Task-Adaptive Pre-training Recent works (Lee et al., 2020; Gururangan et al., 2020) point out + +1The 128 maximum passage length is tuned based on the validation set for both GPU memory and better computational efficiency for large posts. We do not observe a significant performance drop without a larger passage length. +2We use a limited-size stack and greedily add each sentence into the stack. If adding a new sentence will make the sum of lengths of all sentences in the stack exceed 128, we pop out all sentences, concatenate them to a new passage and then add this new sentence to the stack. For sentences having more than 128 words, we treat them as individual posts. + +that task-adaptive pre-training (TAP) can help pretrained language models better adapt to the target domains and can bring improvement, especially in data-poor scenarios. Specifically, we continue pre-training (e.g., masked language modeling for BERT) on a task-relevant unlabeled corpus and then do normal fine-tuning on the task. Our unlabeled corpus consists of all r/SuicideWatch posts (aggregated per user) from the training sets of all the tasks (A, B, C) in the shared task (Zirikly et al., 2019). There are 621 users and 138,057 posts in this unlabeled corpus. We do continued pretraining for 2 to 3 epochs and do early stopping. + +Multi-view Learning Multi-view learning (Xu et al., 2013) (MVL) is one of the widely recognized semi-supervised methods. Clark et al. (2018) provides a successful example of utilizing MVL in sequential labeling tasks. The idea is to create perturbations by masking words in certain positions and requiring the model to learn the similar distribution over the complete labeled examples and the corresponding masked examples besides normal classification training. However, since ours is a user-level classification task, we cannot directly borrow the same strategy from (Clark et al., 2018) as it mainly works on sequence labeling. We propose to create perturbations $\tilde{C}_i$ based on four strategies. First, for each sentence, we will randomly mask $10\%$ of tokens (Word-Mask). Second, considering that users may have posts of many words, we also propose a sentence-level masking strategy (Sent-Mask). For each post of a single user in the training set, we would randomly mask $10\%$ of tokens. Third, we only keep the beginning and ending sentences in each passage (BegEd). Usually these sentences convey the main purpose of the posts and should preserve important semantics. Forth, we use Bert-extractive-summarizer (Miller, 2019) to extract the summary for each passage (K-Sum). It works mainly by first encoding each sentence $s_{ik,j}$ using a PLM to a continuous-valued representation $\vec{s}_{ik,j}$ and then training a K-means clustering over $\vec{s}_{ik,j}$ . Finally it will pick $K$ sentences for each passage that are closest to the center. Empirically, we set $K = 5$ . + +In training, we use KL-divergence to enforce the constraint that the predicted probability on perturbed examples $\tilde{P}(y_i|\tilde{C}_i)$ should be close to the one on complete examples (i.e., $\tilde{P}(y_i|C_i)$ ). The + +loss incurred by KL-divergence is simply added to the classification loss and these two losses are optimized together for each training instance. + +Clinical Psychology Inspired Pseudo-labeling According to the analysis of the shared task report (Zirikly et al., 2019), the main challenge for the 4-way classification comes from insufficient data for the intermediate classes (i.e., low-risk and medium-risk). A straightforward solution is to collect data for these two classes. Recent clinical psychological research (O'Connor and Nock, 2014) points out that mental health issues such as depression and anxiety can be important risk factors for suicide. Inspired by this study, we collect data from r/Anxiety and r/depression from Reddit. The time range of all collected data is from December 1, 2008 to September 30, 2020. We sample a small proportion of the collected data from both subreddits and after manual verification, we decided to assign low-risk labels to all r/Anxiety users in the sample and medium-risk labels to all r/depression users in the sample. Since we do not have experts to label these posts, adding too much pseudo-labeling data might introduce too much noise. Based on preliminary experiments on the validation set, the number of added pseudo-labeling data is $8\%$ of the suicide risk assessment training data. The only difference between these experiments and the main experiments is that we only train the model for 10 epochs rather than full 20 epochs. Table 1 show results for different sizes of added pseudo-labeled data from r/depression on the validation set. All pseudo-labeling data follows roughly the same pattern with the best proportion being $8\%$ . + +
#(r/depression) +#(Training)Macro-F1 on Validation set
2%0.408
8%0.471
16%0.442
32%0.408
+ +# 3 Experiments and Results + +We implement our BERT model based on huggingface Transformer (Wolf et al., 2020). Due to the limitation of GPU memory, we only use the base version. We split $20\%$ of original training data to be the validation set and fix the split for all models. The model selection is made by early stopping and we train all models for 20 epochs with the batch + +Table 1: Results of different proportions of added pseudo-labeling data from r/depression. + +
No.ApproachSetupMacro (P/R/F1)
1BaselineBERT0.436 / 0.424 / 0.427
2TAPBERT0.439 / 0.445 / 0.432
3MVLWord-Mask0.464 / 0.466 / 0.463
4MVLSent-Mask0.380 / 0.409 / 0.383
5MVLBegEd0.384 / 0.422 / 0.401
6MVLK-Sum0.384 / 0.422 / 0.401
7PLDepression +(medium-risk)0.535 / 0.480 / 0.498
8PLAnxiety +(low-risk)0.495 / 0.469 / 0.478
9PLDepression ++ Anxiety0.473 / 0.456 / 0.463
10PLTask C +(low-risk)0.475 / 0.462 / 0.460
11-Task C +(crowd-labeled)0.418 / 0.406 / 0.408
+ +Table 2: Results Task A test set. For each of tasks 7-11, the size of added data is $8\%$ of training data. Metrics are all reported on macro-average. + +size 32. For users with too many posts and words, we only sample 100 passages for them. Table 2 shows our results on Macro-F1. + +Task-Adaptive Pre-training After applying task-adaptive pre-training on BERT, we see small performance gains over BERT (i.e., from 0.427 to 0.432). That might be because even we use the whole corpus provided by the shared task, it is still not large enough. + +Multi-view Learning Word-Mask strategy improves over the BERT baseline. Compared with the adaptive pre-training results on BERT, which also do word-level masking but only trained on language modeling, we can see that MVL provides a more efficient way to utilize a small training corpus and bring $3.1\%$ gain on Macro-F1. However, all the other MVL approaches hurt the performance when compared to the BERT baseline. This might be because the proposed sentence-level perturbation strategy can seriously break the semantics of each post and thus influence the overall performance, and random sampling over sentences hurts most. + +Clinical Psychology Inspired Pseudo-labeling Exp 7, 8 and 9 in Table 2 achieve the Top-3 Macro-F1 scores. This indicates that although our psychology-inspired pseudo-labeling technique is simpler than other weakly-supervised methods, adding meaningful pseudo-label data from relevant domains helps mitigate the problem of insufficient data in the intermediate classes (b and c). To verify this point, we show the class-wise classification results for PL-based models in Table 3 where we can + +
Setupabcd
Baseline0.7300.0770.3330.566
Depression (medium-risk)0.7640.2730.3270.627
Anxiety (low-risk)0.7240.1600.4150.614
Depression + Anxiety0.7670.1430.3700.574
Task C (low-risk)0.7620.0800.3180.678
Task C (crowd-labeled)0.66700.3570.609
+ +Table 3: Class-wise performance (F1) for PL-based methods (a=no-risk; b=low-risk; c=medium-risk; d=high-risk). + +see improvements on b and c classes. Due to space constraints, we present the class-wise performance for all models in Appendix C. + +The investigation over the confusion matrix of the best model (shown in Section 4) further supports our hypothesis. However, when we try to combine different pseudo-labeling data together (see Exp 9, where we add users from r/depression and r/Anxiety following the proportion of $1:2^{4}$ and still keep the added user number the same), we observe a slight performance drop. The reason might be that users in these two PL datasets might be at the boundary of the low-risk and medium-risk and simply mixing them together will make the model confuse between these two classes (see Supplemental material D for all confusion matrices). + +Furthermore, we wanted to test the role of the clinical psychology aspect of our pseudo-labeling approach. Does the gain come from the meaningful domains (anxiety and depression) or just by adding additional data? To answer this, we use additional data provided by Task C of the shared task that contains posts from random subreddits (e.g., sports). We do two experiments: 1) assign low-risk to all such users and 2) assign the gold labels provided by the task via crowdsourcing. We add the same size as for the other pseudo-label experiment (8% of training data). The results (Exp 10 & 11 in Table 2) show that the clinical psychology inspired PL outperforms these models by meaningfully addressing the intermediate classes insufficient data problem. + +# 4 Error Analysis + +In this section, we take a closer look at the prediction results of our best model (clinical psychol + +ogy inspired pseudo labeling using r/depression as medium risk) by looking at the confusion matrix and sampled error cases. We plot the confusion matrices for the baseline model (Exp 1 in Table 2) and the best model (Exp 7 in Table 2) in Figure 1. We can see that, the best model achieves the improvement mainly by fixing error cases wrongly predicted as no-risk (where the true labels are "b", "c" and "d", with greater error reduction for "d") and low-risk (where the true labels are "c" and "d"). As O'Connor and Nock (2014) point out, depression is a serious mental issue and has become one of the most important risk factors of suicide. Adding posts from r/depression can help the model understand better what is "medium-risk" and "high-risk" and thus raise the alert for the signals of similar or related mental issues. + +We can also see that the main problem of our best model, is still the confusion between "b" (low-risk) and "c" (medium-risk). In addition, the problem of wrongly predicting the examples belonging to intermediate classes to high-risk ones still exists. By manual investigation, we find that both problems require expertise in mental health to make the subtle distinctions. For example, the following text comes from a low-risk example5 that is wrongly predicted as high-risk by our best model: + +sadness has taken me...i am sad, lonely, and i have no interest in living anymore...i didnt want to die...my mind is diseased, unable to take happiness...i have no interest in forming any more...i dont think ill do it... + +It can be seen that there are many negative or even desperate expressions (marked as red) in this examples, mixed with some short signals (marked as blue) possibly indicating a person considered at low-risk. The model can be fooled by the massive negative expressions and make the wrong predictions if the model is not aware of the true intent of the person. Therefore, reliable intent identification that could consider user posts across time and other information would be a powerful tool to help the model prevent mistakes like this. + +# 5 Application: Predicting Suicide Risk of People Who Use Drugs + +In order to further verify the effectiveness of our model in real-world applications, we create a sim + +![](images/51859e7f6c1dcb1864ea62dc9f6d2cbca06730139d3149c9fbec51d753a434fb.jpg) +Figure 1: Visualization of the confusion matrices for the baseline model (Exp 1) and the best model (Exp 7). + +![](images/11c00700eca97802a39a30569b4a4dba9c56bfc67ced996236450382cc28d02c.jpg) + +ulation scenario: we apply our best model (Exp 7) over the data that is collected for 612 users who post on both r/opiates and r/SuicideWatch. r/opiates is a subreddit where people discuss topics around opioid usage (e.g., drug doses, withdrawal anguish, daily experiences, harm reduction). This community members could often be at a high suicide risk (Aladag et al., 2018; Yao et al., 2020). We apply our model over their 1,176 posts on r/SuicideWatch and find that our model predicts that $15.52\%$ of them are no-risk, while $84.48\%$ of them are of low-risk, medium-risk and high-risk. The results on sampled 2,863 r/opiate posts are $30.56\%$ for no-risk and $69.44\%$ for at least some risk. The predicted outputs are highly aligned with reported results using crowdsourcing annotation of suicidal or not-suicidal by Yao et al. (2020) and show the effectiveness of our model in this simulated scenario. $^{6}$ We hope this will open the door of using NLP methods to investigate the link between suicidal ideation and fatal overdoses among people who use drugs. + +# 6 Conclusions + +We investigated a series of weakly-supervised methods and find that pseudo-labeling on data related to risk factors for suicide (depression, anxiety) can help improve model performance. This provides an alternative way to use theoretically-grounded models (e.g., compared to feature engineering). We also show a potential use case of this work for understanding suicidal ideation among users who use drugs (e.g., opiates). + +# Ethical Considerations + +The dataset for suicide risk assessment was obtained from the organizers of the 2019 Clinical Psychology Shared Task on Suicide Risk Assessment, by filling in a participant application where we affirmed that we would follow the shared task's rules. We have obtained IRB approval (exempt) from Columbia University to use the data as it consists of publicly available and anonymous posts extracted from Reddit. For the application part, we also obtained Columbia IRB approval (exempt) for the data publicly available and anonymous data from r/ opiates. All data is kept secure and online userIDs are not associated with the posts. + +Our intention of developing and improving suicide risk assessment models is to help health professionals and/or social workers identify people that might be at risk of committing suicide. We emphasize our intention that suicide risk assessment models such as the ones developed here to be used responsibly, with a human in the loop — for example a medical professional, a mental health specialist, who can look at the predicted labels and offer explanations and decide whether or not they seem sensible. We would urge any user of suicide risk assessment technology to carefully control who may use the system. Currently, the presented models may fail in two ways: they may either mislabel an at-risk user as no-risk (our current models are particularly designed to minimize this risk), or classify a no-risk user with some level of risk. Obviously, there is some potential harm to a person who is truly in need if a system based on this work fails to detect their suicidal ideation, and it is possible that a person who is not truly in need may be irritated or offended if someone reaches out to them because + +of a mistake. That is why, this system needs only to be used as additional help for health professionals. + +We note that because most of our data were collected from Reddit, a website with a known overall demographic skew (towards young, white, American men7), our conclusions about what expressions of different suicide risk levels look like and how to detect them cannot necessarily be applied to broader groups of people. This might be particularly acute for vulnerable populations such as people with opioid use disorder (OUD). We hope that this research stimulates more work by the research community to consider and model ways in which different groups express suicidal ideation. + +# References + +Ahmet Emre Aladag, Serra Muderrisoglu, Naz Berfu Akbas, Oguzhan Zahmacioglu, and Haluk O Bingol. 2018. Detecting suicidal ideation on forums: proof-of-concept study. Journal of medical Internet research, 20(6):e215. +Kevin Clark, Minh-Thang Luong, Christopher D Manning, and Quoc Le. 2018. Semi-supervised sequence modeling with cross-view training. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1914-1925. +Glen Coppersmith, Ryan Leary, Patrick Crutchley, and Alex Fine. 2018. Natural language processing of social media as screening for suicide risk. *Biomedical informatics insights*, 10:1178222618792860. +Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. Bert: Pre-training of deep bidirectional transformers for language understanding. In *NAACL*. +Seena Fazel and Bo Runeson. 2020. Suicide. reply. New England journal of medicine, 382(21):e66-e66. +Suchin Gururangan, Ana Marasovic, Swabha Swayamdipta, Kyle Lo, Iz Beltagy, Doug Downey, and Noah A Smith. 2020. Don't stop pretraining: Adapt language models to domains and tasks. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 8342-8360. +Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, and Jaewoo Kang. 2020. Biobert: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics, 36(4):1234-1240. + +Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692. +Matthew Matero, Akash Idnani, Youngseo Son, Salvatore Giorgi, Huy Vu, Mohammad Zamani, Parth Limbachiya, Sharath Chandra Guntuku, and H Andrew Schwartz. 2019. Suicide risk assessment with multi-level dual-context language and bert. In Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology, pages 39-44. +Catherine M McHugh, Amy Corderoy, Christopher James Ryan, Ian B Hickie, and Matthew Michael Large. 2019. Association between suicidal ideation and suicide: meta-analyses of odds ratios, sensitivity, specificity and positive predictive value. BJPsych open, 5(2). +Derek Miller. 2019. Leveraging bert for extractive text summarization on lectures. arXiv preprint arXiv:1906.04165. +Matthew K Nock, Franchesca Ramirez, and Osiris Rankin. 2019. Advancing our understanding of the who, when, and why of suicide risk. JAMA psychiatry, 76(1):11-12. +Rory C O'Connor and Matthew K Nock. 2014. The psychology of suicidal behaviour. *The Lancet Psychiatry*, 1(1):73-85. +WHO et al. 2016. Suicide across the world. +Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumont, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush. 2020. Transformers: State-of-the-art natural language processing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 38-45, Online. Association for Computational Linguistics. +Chang Xu, Dacheng Tao, and Chao Xu. 2013. A survey on multi-view learning. arXiv preprint arXiv:1304.5634. +Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le. 2019. Xlnet: Generalized autoregressive pretraining for language understanding. In NeurIPS, pages 5754-5764. +Hannah Yao, Sina Rashidian, Xinyu Dong, Hongyi Duanmu, Richard N Rosenthal, and Fusheng Wang. 2020. Detection of suicidality among opioid users on reddit: Machine learning-based approach. Journal of medical internet research, 22(11):e15293. + +Ayah Zirikly, Philip Resnik, Ozlem Uzuner, and Kristy Hollingshead. 2019. Clpsych 2019 shared task: Predicting the degree of suicide risk in reddit posts. In Proceedings of the sixth workshop on computational linguistics and clinical psychology, pages 24-33. + +# A Comparison of Different Pre-trained Language Models + +Given that there has been significant progress on the architecture designs after BERT, we have experimented with different PLMs, such as RoBERTa (Liu et al., 2019) and XLNet (Yang et al., 2019). From Table 4, we can see that on the Test set, the Macro-F1 scores for BERT and RoBERTa are almost the same and XLNet performs worse than BERT. Therefore, we hypothesis that the architecture of PLMs will not influence substantially the results on this task so we chose BERT model. + +
PLMTAP?PL?MVL?Macro-F1
BERTNoNoNo0.427
XLNETNoNoNo0.422
RoBERTaNoNoNo0.408
+ +# B Results for Different Mixing Proportions + +Table 5 shows the results for different mixing proportions of pseudo-labeling data from r/Anxiety and r/depression. Due to the limitation of space, in the main paper, we only show the results achieved by the best mixing proportions. + +Table 4: Experiment results for different PLMs. Here we only show the macro-F1 for the baseline model built on different PLMs. + +
Mixing ProportionMacro-F1
1: 50.398
1: 20.463
1: 10.434
2: 10.441
5: 10.442
+ +Table 5: Experiment results for different mixing proportions. Here the proportion represents the user ratio of $\# (\mathrm{r}/\text{depression}): \# (\mathrm{r}/\text{Anxiety})$ . + +# C Class-wise Decomposition of Experimental Results + +Here we show the class-wise performance for all the models in Table 6. + +# D Additional Error Analysis + +Additional confusion matrices for high-performance models (8, 9, 10 in Table 2) are in Figure 3. + +![](images/25c26212a1b0ff85503323755e9eb336d18ca9ab6c1ee1e9cc88f1db4b5e815a.jpg) +Anxiety (low-risk) Model Confusion Matrix + +![](images/6b91a1581fdb6587017fd19cb6677067f82ee7a0737b87b5a85cff1c0f3cedf0.jpg) +Depress+Anx Model Confusion Matrix + +![](images/f870bd416baf241b0fbdf8a81ac9710bf39fadca03a00d532282408ae626e16f.jpg) +TaskC (low-risk) Model Confusion Matrix + +![](images/c78eb1d44d0753bc4f77119cf7d98c34f720fa82d2b18f958c8065410857d68c.jpg) +Word-Mask Model Confusion Matrix +Figure 2: Word-Mask Confusion Matrix. +Figure 3: Additional Confusion Matrices for Task 8, 9, 10, 3 in Table 2 + +
No.ApproachSetupabcd
1BaselineBERT0.742/0.719/0.7300.077/0.077/0.0770.400/0.286/0.3330.525/0.615/0.566
2TAPBERT0.774/0.750/0.7620.143/0.154/0.1480.250/0.107/0.1500.588/0.769/0.667
3MVLWord-Mask0.788/0.812/0.8000.111/0.077/0.0910.391/0.321/0.3530.567/0.654/0.607
4MVLSent-Mask0.551/0.844/0.6670.091/0.077/0.0830.294/0.179/0.2220.583/0.538/0.560
5MVLBegEd0.686/0.750/0.7160/0/00.320/0.286/0.3020.531/0.654/0.586
6MVLK-Sum0.686/0.750/0.7160/0/00.320/0.286/0.3020.531/0.654/0.586
7PLDepression (c)0.913/0.656/0.7640.333/0.231/0.2730.333/0.321/0.3270.561/0.712/0.627
8PLAnxiety (b)0.808/0.656/0.7240.167/0.154/0.1600.440/0.393/0.4150.565/0.673/0.614
9PLDepression + Anxiety0.821/0.719/0.7670.133/0.154/0.1430.385/0.357/0.3700.554/0.596/0.574
10PLTask C (b)0.774/0.750/0.7620.083/0.077/0.0800.438/0.250/0.3180.606/0.769/0.678
11-Task C (crowd-labeled)0.760/0.594/0.6670/0/00.357/0.357/0.3570.556/0.673/0.609
+ +Table 6: Class-wise decomposition results for models considered in this paper. The results under each class are presented following the "Precision/Recall/F1" format. \ No newline at end of file diff --git a/weaklysupervisedmethodsforsuicideriskassessmentroleofrelateddomains/images.zip b/weaklysupervisedmethodsforsuicideriskassessmentroleofrelateddomains/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..d94583e9ee1f403d34dbedeb187c2da5e551b7be --- /dev/null +++ b/weaklysupervisedmethodsforsuicideriskassessmentroleofrelateddomains/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9452ceca42d972016d0da3ca263c53c4941e62d48b82a4da006c33b9966cb54b +size 338318 diff --git a/weaklysupervisedmethodsforsuicideriskassessmentroleofrelateddomains/layout.json b/weaklysupervisedmethodsforsuicideriskassessmentroleofrelateddomains/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..1357b79e835fca18fc78268f430c0ad507672991 --- /dev/null +++ b/weaklysupervisedmethodsforsuicideriskassessmentroleofrelateddomains/layout.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:eeb1a689800a7061650b8b53d38423add34971a3c19e440cb177bf7138d07f62 +size 220690 diff --git a/whatmotivatesyoubenchmarkingautomaticdetectionofbasicneedsfromshortposts/57ee7e11-e027-4ef3-9849-f996c17c1678_content_list.json b/whatmotivatesyoubenchmarkingautomaticdetectionofbasicneedsfromshortposts/57ee7e11-e027-4ef3-9849-f996c17c1678_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..407ddfeaa8a296e5fcb97bedf2b8b425fec5be48 --- /dev/null +++ b/whatmotivatesyoubenchmarkingautomaticdetectionofbasicneedsfromshortposts/57ee7e11-e027-4ef3-9849-f996c17c1678_content_list.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:febbf53aaf520dc53b67fcbd4df7fedce32a262fa7bfb783665f328ed2e963d7 +size 55924 diff --git a/whatmotivatesyoubenchmarkingautomaticdetectionofbasicneedsfromshortposts/57ee7e11-e027-4ef3-9849-f996c17c1678_model.json b/whatmotivatesyoubenchmarkingautomaticdetectionofbasicneedsfromshortposts/57ee7e11-e027-4ef3-9849-f996c17c1678_model.json new file mode 100644 index 0000000000000000000000000000000000000000..57f39f14956208fb6ddb9ce87908bf9ce303c4f1 --- /dev/null +++ b/whatmotivatesyoubenchmarkingautomaticdetectionofbasicneedsfromshortposts/57ee7e11-e027-4ef3-9849-f996c17c1678_model.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:40f4e83c57d6d8d9b27dec02c97ef3fa3b12d554eecae00d7f10e2f2df2d6cf2 +size 70724 diff --git a/whatmotivatesyoubenchmarkingautomaticdetectionofbasicneedsfromshortposts/57ee7e11-e027-4ef3-9849-f996c17c1678_origin.pdf b/whatmotivatesyoubenchmarkingautomaticdetectionofbasicneedsfromshortposts/57ee7e11-e027-4ef3-9849-f996c17c1678_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..ac9349ddace7d1b87c4316fb303eac94074ae516 --- /dev/null +++ b/whatmotivatesyoubenchmarkingautomaticdetectionofbasicneedsfromshortposts/57ee7e11-e027-4ef3-9849-f996c17c1678_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:594b00bbdc33b55e30c0268f0f2e752de8f674ca78ab112787abc80ebb52c961 +size 211805 diff --git a/whatmotivatesyoubenchmarkingautomaticdetectionofbasicneedsfromshortposts/full.md b/whatmotivatesyoubenchmarkingautomaticdetectionofbasicneedsfromshortposts/full.md new file mode 100644 index 0000000000000000000000000000000000000000..ecc4a48dfaf799cdf4d7fcd35bccef0899996e95 --- /dev/null +++ b/whatmotivatesyoubenchmarkingautomaticdetectionofbasicneedsfromshortposts/full.md @@ -0,0 +1,219 @@ +# What Motivates You? Benchmarking Automatic Detection of Basic Needs from Short Posts + +Sanja Štajner1, Seren Yenikent1, Bilal Ghanem2, Marc Franco-Salvador2 + +1Symanto Research, Nuremberg, Germany + +$^{2}$ Symanto Research, Valencia, Spain + +{sanja.stajner, seren.yenikent, marc.franco}@symanto.com bilalhgm@gmail.com + +# Abstract + +According to the self-determination theory, the levels of satisfaction of three basic needs (competence, autonomy and relatedness) have implications on people's everyday life and career. We benchmark the novel task of automatically detecting those needs on short posts in English, by modelling it as a ternary classification task, and as three binary classification tasks. A detailed manual analysis shows that the latter has advantages in the real-world scenario, and that our best models achieve similar performances as a trained human annotator. + +# 1 Introduction + +Motivation is one of the most crucial aspects of human behaviour with implications ranging from daily life to career and educational contexts. Self-determination theory (SDT) provides a metaframework for understanding the broad, as well as specific, nutrients of the function and application of the concept of motivation (Deci and Ryan, 2000; Ryan and Deci, 2017a). + +SDT differs from the other motivational theories from the psychology literature in two substantial aspects (Ryan and Deci, 2000; Rigby and Ryan, 2018): (1) Unlike the drive theories that explain motivation as a function of its deficit (e.g. people are motivated by success to compensate its deficit), SDT focuses on growth and constructivism (e.g. people are naturally and universally motivated by success), thus giving the theory a more realistic understanding of the human behaviour, and making it applicable to wider contexts; and (2) Due to the applicability advantage, SDT is based on strong behavioural evidence and is thus not only a well-validated model but also sustainable and actionable. + +The SDT framework is supported by a body of cross-cultural studies strengthening the universality of the theory. Studies conducted in diverse countries showed that the basic needs are essentially + +represented across cultures (Chen et al., 2015; Jang et al., 2009). Although universal, the SDT framework is also able to point out the impact of sociocultural environment on the variations of basic needs in different cultures. For example, a study conducted in 11 countries showed that the need for competence was more linked to school performance in Eastern cultures than in the West (Nalipay et al., 2019). + +One of the central pillars of SDT are three basic psychological needs that drive the initiation of a behaviour and the maintenance of motivation: + +- Autonomy: the basic need to be the owner and controller of one's decisions and behaviours. +- Competence: the basic need to feel competent, effective and master-like. +- Relatedness: the basic need to belong, bond and connect with others. + +According to SDT, those three needs are universal and their importance does not change across individuals and situations. However, different contexts and time periods would require different support and resources for the maintenance of the motivations. For instance, cultivating autonomy need in students creates more engagement and willingness, thus leading to higher academic performance, lower dropouts, and more self-esteem in the long run (Ryan and Deci, 2020). Similarly, the SDT framework is used to increase levels of employee satisfaction and engagement, supportive leadership and parenting skills, healthier relationships, satisfactory consumer experience and better designed digital media and well-being tools (Slemp et al., 2018; Rigby and Ryan, 2018; Ryan and Deci, 2017b; Knee et al., 2002; Gilal et al., 2019; Peters et al., 2020; Peng et al., 2012). + +
NeedPost
AutonomyJust treated myself to a Roland TB-3. Should arrive this evening. #excited
AutonomyOne thing's for sure, I will not let you ruin my dreams, HIV. #determined
CompetenceWhat an achievement. Finally getting some credit. #Fury #SPOTY
CompetenceI fell asleep with socks on... I disgust myself.
RelatednessI'm so lucky to have my best friend and boyfriend rolled into one! #soppy #proud
RelatednessYou know what I feels like to be #ALONE in this cold world?
+ +Table 1: Annotated examples from the dataset (either satisfied or unsatisfied need). + +Traditionally, basic motivations are assessed via questionnaires which provide intensity-based scores for each dimension. The scores represent the degree to which that particular dimension is satisfied (Deci and Ryan, 2000). Although these questionnaires were developed and validated via laboratory and field studies which provide a strong empirical basis, they could suffer from biases commonly observed in questionnaire respondents such as social desirability bias (Kruppal, 2011) and the reference-group effect (Heine et al., 2002). The basic motivations can also be revealed in a more implicit way, by collecting subjects' narratives while showing them pictures and images (Murray, 1943; McClelland, 1979). Although being more expensive and time-consuming, as it requires the inclusion of trained assessors, this method shows that implicit motivations can be assessed from texts. A few studies attempted at automatic detection of basic motivations on the basis of their linguistic aspects from such narratives (Pennebaker and King, 1999; Johannssen and Biemann, 2019). + +To the best of our knowledge, our study is the first that attempts to automatically detect the three basic needs from short posts. In this study, we: + +- Benchmark the task of automatic detection of basic needs from English Twitter data using several architectures on an already existing manually annotated dataset. +- Provide a manual analysis which shed light on the complexity of the task and its usability. +- Discuss the limitations of the existing dataset, and suggest better annotation strategies. + +# 2 Dataset + +For our experiments, we used the first two layers of the Basic Psychological Needs Corpus (Alharthi et al., 2017), which is publicly available. The + +corpus contains Twitter posts annotated with five layers of annotation as the intention was to provide freely available multilayered annotated corpus for a wide range of applications (Alharthi et al., 2017). The manual annotation was performed by three annotators in three stages, encompassing thorough training sessions and detailed annotation guidelines, one round of collectively labelling tweets, one round of independently labelling the same posts for calculating inter-annotator agreement (IAA), and the final round of independently labelling the rest of the posts. The average pairwise agreement and the Fleiss Kappa $(\kappa)$ were $90\%$ and 0.815 for whether or not the post contains enough content for assigning one of the three basic needs (autonomy, relatedness, or competence), and $89\%$ and 0.819, respectively, for the assigned label (Alharthi et al., 2017). + +The final dataset with manual annotations of basic needs was already pre-filtered for non-emotional posts and those that do not contain enough signal (Alharthi et al., 2017). It contains 6334 posts with the following distribution of the labels: 1229 posts labelled with competence, 1771 with autonomy, and 3334 with relatedness label. In our experiments, we used this dataset and only the labels of the second layer of annotation (basic needs). Several examples are given in Table 1. Here is important to note that the original dataset also contains, in the third layer, the annotation for the satisfaction level (satisfied, dissatisfied, neutral) of the assigned basic need. We acknowledge that the combination of the basic needs and their level of satisfaction are often used together, e.g. as indicators of person's well-being (Deci and Ryan, 2011), violence and conflict possibility (Christie, 1997), stress and coping (Ntoumanis et al., 2008; Weinstein and Ryan, 2011). However, we opted for discarding these additional labels for three reasons: (1) because the inter-annotator agreement was significantly lower for this annotation layer + +(the average pairwise agreement was $75\%$ and the $\kappa$ was 0.640); (2) so that we do not increase the total number of classes (to nine instead of three) and therefore significantly lower the number of instances in each class; (3) because this task appears similar to the task of assigning the sentiment polarity of the post (Alharthi et al., 2017), and therefore might be modelled with various other datasets. + +# 3 Experimental Setup + +# 3.1 Preprocessing + +The instances were already cleaned in the original dataset by removing all usernames (@username) and URLs, while preserving emoticons, punctuation marks, social acronyms and abbreviations, which might contain psycholinguistic signals (Alharthi et al., 2017). Furthermore, the dataset does not contain any duplicated instances, tweets with less than three words, or tweets with more than three hashtags (Alharthi et al., 2017). We noticed that for this particular task, the hashtags may help the models, e.g. #proud usually signalizes competence, #relationship signalizes relatedness. To better assess how well the models would perform on a different type of texts, we experimented with two versions of the dataset: WITHOUT HASHTAGS and WITH HASHTAGS. + +# 3.2 Data Splits + +We randomly choose $15\%$ of the instances for testing, and then $15\%$ from the rest of the data for development, while maintaining the class ratio (Table 2). During our experiments, we found that applying upsampling on the minority classes (competence and autonomy) slightly improved the performances of some models, and had no change on others. Thus, we only report the results obtained by using upsampling. + +# 3.3 Task Definition + +We approached the problem of detecting basic needs with two different scenarios: (1) as a ternary classification problem (assigning one of the three possible basic needs to each post), and (2) as three binary classification tasks (for each basic need, assigning either yes or no label). The ternary classification is a more natural choice for this particular dataset, as all instances were annotated with only one of the three basic needs. However, according to the SDT, each person have at all times the all three needs just with different intensities and sat + +
NeedORIGINALREPORTED
TRAINDEVTESTTRAINDEVTEST
Autonomy12482282902416404290
Competence8681682042416404204
Relatedness24164045082416404508
+ +Table 2: Dataset splits. + +isfaction levels (Section 1). It is thus reasonable to assume that some posts will also contain signals of multiple basic needs. Therefore, we also performed three binary tasks which would allow us to model each basic need separately. By using three binary classifiers instead of one ternary, posts could be automatically labelled with none of, or any combination of, basic needs. + +# 3.4 Evaluation Metrics + +For both types of classification tasks (binary and ternary), we used the per-class precision, recall, and $\mathrm{F}_1$ -score, and the macro-averaged $\mathrm{F}_1$ -score for evaluating the performances of the models. + +# 3.5 Architectures + +In order to assess the importance of both lexical and semantic aspects of texts, we tested various approaches that use different text representations: + +- BOW: word unigrams and bigrams model with the TF-IDF weighting scheme (Salton and Buckley, 1988) using a Support Vector Machines (Chang and Lin, 2011) classifier with a linear kernel. $^{2,3}$ + +- Char-CNN: a Convolutional Neural Networks (CNN) architecture similar to the one proposed in (Zhang et al., 2015) but using a trainable character embedding layer as input. + +- BiLSTM: a bidirectional Long Short-Term Memory (BiLSTM) (Hochreiter and Schmidhuber, 1997) neural network that uses FastText word embeddings (Bojanowski et al., 2017) to represent texts. The BiLSTM hidden states are fed to an attention layer (Yang et al., 2016), and then the attention output is processed with a fully connected layer. As an output, a softmax layer is used to obtain the final classification. + +
ModelWITHOUT HASHTAGSWITH HASHTAGS
AutonomyCompetenceRelatedness\(F_1\)(macro)AutonomyCompetenceRelatedness\(F_1\)(macro)
PR\(F_1\)PR\(F_1\)PR\(F_1\)PR\(F_1\)PR\(F_1\)PR\(F_1\)
BiLSTM.54.54.54.48.45.47.74.76.75.59.65.47.55.47.63.54.80.82.81.63
Char-CNN.50.64.56.58.24.34.73.79.76.55.61.61.61.54.54.54.82.81.82.66
BOW.61.49.54.49.54.52.76.82.79.62.67.57.62.57.54.55.79.87.83.67
BERT.62.54.58.55.61.58.84.87.86.67.70.69.69.73.52.61.83.93.88.72
BERT+BiLSTM.62.60.61.56.66.60.87.82.85.69.71.61.66.69.62.65.83.93.88.73
Trained human.78.70.74.69.88.77.88.72.79.77.78.70.74.69.88.77.88.72.79.77
BERT+BiLSTM.78.70.74.79.65.71.73.93.81.75.73.75.74.81.62.70.77.93.84.76
+ +Table 3: Results of the ternary classification task. The last two rows present the results on a subset of the test set that was annotated by a trained human annotator and contains 40 instances of each class. + +- BERT: the neural language model, well-known for providing text representations that show leading performances on several natural language processing benchmarks (Devlin et al., 2019). We fine-tune BERT and use its hidden representation of the special [CLS] token to represent the full input text and feed it to a softmax output layer. +- BERT+BiLSTM: this model combines the previous two approaches. Instead of FastText word representation, the fine-tuned BERT embeddings are post-processed by the BiLSTM architecture defined above. We observed that such architectures help BERT to adapt to the target task and obtain better classification results in scenarios with small training datasets. + +# 4 Results and Discussion + +# 4.1 Ternary Classification + +All models performed noticeably better on the original than on the cleaned dataset, thus supporting our hypothesis that the presence of the hashtags leads to better model performances (Table 3). As expected, the models that are based on transfer learning (BERT and BERT+BiLSTM) performed best. Interestingly, the non-neural model (BOW) outperformed the BiLSTM and Char-CNN models on the competence class using the cleaned dataset $(\mathrm{F_1}$ -score of 0.52 against 0.47 and 0.34, respectively). + +In all models, most misclassifications were observed between the competence and autonomy classes. A possible reason for this might lie in the SDT theory, as autonomy and competence are self-originated needs, whereas relatedness includes both self and others (Vansteenkiste et al., 2020). + +This might lead to theme/topic overlaps between autonomy and competence due to the self-focus, while relatedness might be easier to distinguish due to including self and the others. + +# 4.2 Human Performance and Error Analysis + +To assess the expected performance ceiling, we hired a psychologist, well-versioned in SDT, provided the annotation guidelines with several examples, and asked to annotate randomly selected 150 instances from the cleaned test set (50 from each class). The annotator was allowed to assign as many classes as needed to each post. + +Our guidelines were based on a thorough review of psychology research by Ryan and Deci (2020, 2017a,b, 2000) who studied observable behavioural outcomes. We selected the following cues for each basic need: + +- Autonomy: focus of initiative, ownership of self-actions, feelings of restriction by any type of external control. +- Competence: focus on behaviours associated with mastery, achievements, success, and growth (both positive and negative), search for personal or contextual challenges, well-structured environments, and positive feedback. +- Relatedness: focus on spending and appreciating time with significant others, search for community and connection, sense of nurturing and caring for others. + +The annotator assigned two classes in 14 cases $(9.3\%)$ . Some of those were the cases in which our best system (BERT+BiLSTM) made 'wrong' prediction, which turned out to be the same as one of the classes assigned by the human annotator + +
GoldPredictedPost
RelatednessAutonomyWishing I was home this Christmas, maybe next year #homesick #holidays
RelatednessCompetenceI work with an amazing team. They work so hard and are so dedicated. Truly a top comms team #proud
+ +Table 4: Examples of penalized predictions which actually caught the secondary signal. For those examples, the human annotator assigned both classes (the gold and the predicted one). + +
TaskYesNoF1(macro)
PRF1PRF1
Autonomy.74.48.58.81.93.87.73
Competence.69.63.66.91.93.92.79
Relatedness.85.92.88.91.83.87.87
+ +Table 5: Results of the binary classification tasks on the datasets WITH HASHTAGS. + +
TaskYesNoF1(macro)
PRF1PRF1
Autonomy.61.49.54.81.87.84.69
Competence.55.64.59.90.87.88.74
Relatedness.83.86.85.85.82.84.84
+ +Table 6: Results of the binary classification tasks on the datasets WITHOUT HASHTAGS. + +(Table 4). Therefore, we took 120 instances for which the human annotator assigned only one class, and additionally ran our best model on that portion of the test set, to fairly compare its performance with the human performance (the last two rows in Table 3). + +# 4.3 Binary Classifications + +The results of the best performing architecture (BERT+BiLSTM) on the binary tasks using the datasets WITH HASHTAGS and WITHOUT HASHTAGS are presented in Tables 5 and 6. + +To assess the performance of those systems in the real-world scenario, we took 100 random new tweets and ran all three models on them. At the same time, we asked the psychologist to annotate each post (without showing the obtained automatic predictions) by assigning one of the three labels (no, low, high) for each basic need. For example, "@matchbox-sized Wait, you've seen it already? Thought it aired on Sunday nights?" was annotated as low for relatedness, high for autonomy, and no for competence. For the same example, the three best binary models assigned the following probabilities to each of the corresponding classes: + +$p(\text{autonomy}) = 0.88, p(\text{relatedness}) = 0.70,$ and $p(\text{competence}) = 0.30$ . + +We further investigated whether or not the class probabilities obtained by the binary models were related to the labels assigned by the annotator. On those 100 examples, we found that the manually assigned label no corresponds to the $p(yes) \in [0,0.5)$ (obtained by the models) in $90\%$ of the cases, the manually assigned label low to the $p(yes) \in [0.5,0.75)$ (obtained by the models) in $100\%$ of the cases, and manually assigned label high to the $p(yes) \in [0.75,1]$ (obtained by the models) in $98\%$ of the cases. These findings indicate that it might be possible to use the binary models in a more general setup, i.e. on the posts which are not pre-filtered for containing emotions or needs signals, and on posts that reflect more than one need. Furthermore, it seems that those models could capture the intensity of the signals. + +# 5 Conclusions + +In this study, we benchmarked the automatic detection of basic motivations on short (Twitter) posts in English, framing the problem as a ternary classification task, as well as three binary classification tasks. On the ternary classification task, our BERT+BiLSTM model performed almost equally well as a trained human annotator. + +We showed that modelling this problem as three binary classification tasks, instead of modelling it as one ternary classification task, allows for better applicability of the models. The proposed setup with three binary models assigns none of the basic motivations to those posts without any signal (all three models assign a no class), and multiple basic motivations to those posts with signals from multiple motivations (more than one model assigns a yes class), achieving a high agreement with the human annotator. We also found a high association between the class probabilities of the binary models and the human-perceived motivation intensities. + +# 6 Ethics/Impact Statement + +# 6.1 Intended Use + +The goal of our experiments was to investigate if there is a possibility to automatically detect basic needs from short posts, and to benchmark this novel NLP task. As we do not have any demographic information in the dataset used, and we did not thoroughly investigate performances of our models on different text types, demographic groups, and in different contexts, we do not encourage the use of these particular models in real-world applications. Instead, the contribution of our study lies in setting the ground for future models of automatic detection of basic needs from short texts, by benchmarking the task with various machine learning architectures on a specific dataset, experimenting with both ternary and binary setups, providing performance ceiling estimation via human annotations, and discussing the usability of presented approaches. Our study thus provides the foundations for future models which, if trained on carefully sampled data (representative data with strict bias control), have the potential to speed up and provide additional quality checks for traditional questionnaire-based basic needs estimation procedures, which are already widely used for: (1) providing supportive information about the user in organizational contexts such as leadership style and team building processes (Rigby and Ryan, 2018); and (2) prompting learner perspectives in educational contexts such as designing motivation-supportive settings and activities (Schneider et al., 2018). + +# 6.2 Failure Modes + +To try to estimate how the model would perform if trained on different type of data, i.e. non-Twitter data, we evaluated models trained on posts with hashtags and models trained on the same posts but after removing all hashtags. However, it is not certain how would the reported models perform on different types of data, neither whether training models with different data sources would lead to similar results or not. On the used Twitter datasets, we found most misclassifications between autonomy and competence classes. + +# 6.3 Biases + +Given that we do not have any demographic information about the authors of the posts in the used dataset, and that the dataset was prefiltered for emotional and needs signals (Alharthi et al., 2017), + +the presented models might suffer from various algorithmic biases. Furthermore, it is known that certain age groups or socio-economic groups are more present in Twitter than others (Tufekci, 2014; Morstatter et al., 2014), and that certain personality types are more active on particular media platforms (Goby, 2006). + +# 6.4 Misuse Potential + +Using automatic detection of basic needs in decision-making processes during hiring and placement could lead to a potential misuse and unfair decisions due to: (1) algorithmic biases and imperfections of the models; (2) giving too much weight to the estimation of basic needs instead of taking it only as one of many aspects of the employee (e.g. personality, educational background) and team work. + +Basic needs could be used in combination with other psychological variables (e.g. personality) for marketing and consumer targeting purposes. Tailoring marketing materials for different personalities can be beneficial for consumers by leading them to spend their money on personality-matching items (Matz et al., 2016). However, it can also be misused by leading people to act against their best interests, e.g. by persuading them to gamble (Matz et al., 2016). + +# 6.5 Potential Harm to Vulnerable Populations + +As any other psychological modelling, when combined with demographic characteristics (e.g. age, gender, socio-economic background), machine learning models could potentially harm vulnerable groups such as immigrants or people with mental health issues. The models could potentially detect people who suffer from psychological and emotional instability, as it is highly likely that those people may be unsatisfied about their basic needs. To avoid such unintended harms, special attention should be given to carefully collecting a representative sample for any intended use (Williams et al., 2018). + +# References + +Rajwa Alharthi, Benjamin Guthier, Camille Guertin, and Abdulmotaleb El Saddik. 2017. A dataset for psychological human needs detection from social networks. IEEE Access, 5:9109-9117. + +Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017. Enriching Word Vectors with Subword Information. Transactions of the Association for Computational Linguistics, 5:135-146. +Chih-Chung Chang and Chih-Jen Lin. 2011. Libsvm: A library for support vector machines. ACM transactions on intelligent systems and technology (TIST), 2(3):1-27. +Beiwen Chen, M. Vansteenkiste, W. Beyers, L. Boone, E. Deci, J. Kaap-Deeder, B. Duriez, W. Lens, Lennie Matos, Athanasios Mouratidis, R. Ryan, K. Sheldon, B. Soenens, S. Petegem, and Joke Verstuyf. 2015. Basic psychological need satisfaction, need frustration, and need strength across four cultures. Motivation and Emotion, 39:216-236. +Daniel J. Christie. 1997. Reducing direct and structural violence: The human needs theory. Peace and Conflict: Journal of Peace Psychology, 3(4):315-332. +Edward L. Deci and Richard M. Ryan. 2000. The "What" and "Why" of Goal Pursuits: Human Needs and the Self-Determination of Behavior. Psychological inquiry, 4(11):227-268. +Edward L. Deci and Richard M. Ryan. 2011. Levels of analysis, regnant causes of behavior and well-being: The role of psychological needs. Psychological Inquiry, 22(1):17-22. +Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171-4186. +Faheem Gul Gilal, Jian Zhang, Justin Paul, and Naeem Gul Gilal. 2019. The role of self-determination theory in marketing science: An integrative review and agenda for research. European Management Journal, 1(37):29-44. +Valerie Priscilla Goby. 2006. Personality and online/offline choices: MBTI profiles and favored communication modes in a Singapore study. CyberPsychology & Behavior, 9:5-13. +Steven J. Heine, Darrin. R. Lehman, Kaiping Peng, and Joe Greenholtz. 2002. What's wrong with cross-cultural comparisons of subjective likert scales?: The reference-group effect. Journal of Personality and Social Psychology, 82(6):903-918. +Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation, 9(8):1735-1780. +Hyungshim Jang, Johnmarshall Reeve, Richard M. Ryan, and Ahyoung Kim. 2009. Can Self-Determination Theory Explain What Underlies the + +Productive, Satisfying Learning Experiences of Collectivistically Oriented Korean Students? Journal of Educational Psychology, 101:644-661. +Dirk Johannssen and Chris Biemann. 2019. Neural classification with attention assessment of the implicit-association test omit and prediction of subsequent academic success. In Proceedings of the 15th Conference on Natural Language Processing, KONVENS. German Society for Computational Linguistics & Language Technology. +C. Raymond Knee, Heather Patrick, Nathaniel A. Vieter, Aruni Nanayakkara, and Clayton Neighbors. 2002. Self-Determination as Growth Motivation in Romantic Relationships. *Personality and Social Psychology Bulletin*, 5(28):609-619. +Ivar Krumpal. 2011. Determinants of social desirability bias in sensitive surveys: a literature review. *Quality & Quantity*, 47(4). +Sandra C. Matz, Joe J. Gladstone, and David Stillwell. 2016. Money buys happiness when spending fits our personality. Psychological science, 27(5):715-725. +David C. McClelland. 1979. Inhibited power motivation and high blood pressure in men. Journal of Abnormal Psychology, 88:182-190. +Fred Morstatter, Jürgen Pfeffer, and Huan Liu. 2014. When is it biased? assessing the representativeness of twitter's streaming api. In Proceedings of the 23rd International Conference on World Wide Web, WWW '14 Companion, page 555-556, New York, NY, USA. Association for Computing Machinery. +Henry A. Murray. 1943. Thematic Apperception Test. Harvard University Press, Cambridge, MA. +Ma Jenina N. Nalipay, Ronnel B. King, and Yuyang Cai. 2019. Autonomy is equally important across East and West: Testing the cross-cultural universality of self-determination theory. Journal of adolescence, 78:67-72. +Nikos Ntoumanis, Jemma Edmunds, and Joan L. Duda. 2008. Understanding the coping process from a self-determination theory perspective. *British Journal of Health Psychology*, 14(2):249–260. +Wei Peng, Jih-Hsuan Lin, Karin A Pfeiffer, and Brian Winn. 2012. Need satisfaction supportive game features as motivational determinants: An experimental study of a self-determination theory guided exergame. Media Psychology, 2(15):175-196. +James W. Pennebaker and Laura A. King. 1999. Linguistic styles: Language use as an individual difference. Journal of personality and social psychology, 77(6):1296-1312. +Dorian Peters, Naseem Ahmadpour, and Rafael A Calvo. 2020. Tools for wellbeing-supportive design: Features, characteristics, and prototypes. Multimodal Technologies and Interactio, 3(4). + +C. Scott Rigby and Richard M. Ryan. 2018. Self-determination theory in human resource development: New directions and practical considerations. Advances in Developing Human Resources, 2(20):133-147. +Richard M. Ryan and Edward L. Deci. 2000. The darker and brighter sides of human existence: Basic psychological needs as a unifying concept. *Psychological inquiry*, 4(11):319-338. +Richard M. Ryan and Edward L. Deci. 2017a. Self-determination theory: Basic psychological needs in motivation, development, and wellness. Guilford Publications, New York, USA. +Richard M. Ryan and Edward L. Deci. 2017b. Self-determination theory: Basic psychological needs in motivation, development, and wellness. Guilford Publications. +Richard M. Ryan and Edward L. Deci. 2020. Intrinsic and extrinsic motivation from a self-determination theory perspective: Definitions, theory, practices, and future directions. Contemporary Educational Psychology, (101860). +Gerard Salton and Christopher Buckley. 1988. Termweighting approaches in automatic text retrieval. Information processing & management, 24(5):513-523. +Sascha Schneider, Steve Nebel, Maik Beege, and Günter Daniel Rey. 2018. The autonomy-enhancing effects of choice on cognitive load, motivation and learning with digital media. Learning and Instruction, 58:161-172. +Gavin R. Slemp, Margaret L. Kern, Kent J. Patrick, and Richard M. Ryan. 2018. Leader autonomy support in the workplace: A meta-analytic review. Motivation and emotion, 5(42):706-724. +Zeynep Tufekci. 2014. Big questions for social media big data: Representativeness, validity and other methodological pitfalls. Proceedings of the International AAAI Conference on Web and Social Media, 8(1). +Maarten Vansteenkiste, Richard M. Ryan, and Bart Soenens. 2020. Basic psychological need theory: Advancements, critical themes, and future directions. Motivation and Emotion, 44:1-31. +Netta Weinstein and Richard M. Ryan. 2011. A self-determination theory approach to understanding stress incursion and responses. Stress & Health, 27(1):4-17. +Betsy Anne Williams, Catherine F Brooks, and Yotam Shmargad. 2018. How algorithms discriminate based on data they lack: Challenges, solutions, and policy implications. Journal of Information Policy, 8:78-115. + +Zichao Yang, Diyi Yang, Chris Dyer, Xiaodong He, Alex Smola, and Eduard Hovy. 2016. Hierarchical Attention Networks for Document Classification. In Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: human language technologies, pages 1480-1489. +Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015. Character-level Convolutional Networks for Text Classification. Advances in neural information processing systems, 28:649-657. \ No newline at end of file diff --git a/whatmotivatesyoubenchmarkingautomaticdetectionofbasicneedsfromshortposts/images.zip b/whatmotivatesyoubenchmarkingautomaticdetectionofbasicneedsfromshortposts/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..0482efeaadaa5949117e32fbc66a8cbae14dd27a --- /dev/null +++ b/whatmotivatesyoubenchmarkingautomaticdetectionofbasicneedsfromshortposts/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3dc421fc0669067a1b470b3f50bdca5f3b4383f47a86da5521de31fe6ef44bfc +size 204034 diff --git a/whatmotivatesyoubenchmarkingautomaticdetectionofbasicneedsfromshortposts/layout.json b/whatmotivatesyoubenchmarkingautomaticdetectionofbasicneedsfromshortposts/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..465c0e1c41d2e6826f2b887f70aef421515aa2ed --- /dev/null +++ b/whatmotivatesyoubenchmarkingautomaticdetectionofbasicneedsfromshortposts/layout.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:889b491f16315e00f642fb693f3bcf30632d1f9f021a0a095cb9c69e9f5750d4 +size 238848 diff --git a/whatsintheboxananalysisofundesirablecontentinthecommoncrawlcorpus/c999b776-8e97-48ab-bec5-c27830d8d82b_content_list.json b/whatsintheboxananalysisofundesirablecontentinthecommoncrawlcorpus/c999b776-8e97-48ab-bec5-c27830d8d82b_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..fd7349f89278da406a2f1a785432a42287d013c9 --- /dev/null +++ b/whatsintheboxananalysisofundesirablecontentinthecommoncrawlcorpus/c999b776-8e97-48ab-bec5-c27830d8d82b_content_list.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b002cea3cc740489f99b6e8f075d3b2139bf545f6df9108de5fc69905f3dba4f +size 50500 diff --git a/whatsintheboxananalysisofundesirablecontentinthecommoncrawlcorpus/c999b776-8e97-48ab-bec5-c27830d8d82b_model.json b/whatsintheboxananalysisofundesirablecontentinthecommoncrawlcorpus/c999b776-8e97-48ab-bec5-c27830d8d82b_model.json new file mode 100644 index 0000000000000000000000000000000000000000..5985ee5f50d14b0c1b12ee43a5035b7294b4449a --- /dev/null +++ b/whatsintheboxananalysisofundesirablecontentinthecommoncrawlcorpus/c999b776-8e97-48ab-bec5-c27830d8d82b_model.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6d694f9776e6077d5218fd4e72412792a32b9b575a66eae37ab7fcba499b56e8 +size 68963 diff --git a/whatsintheboxananalysisofundesirablecontentinthecommoncrawlcorpus/c999b776-8e97-48ab-bec5-c27830d8d82b_origin.pdf b/whatsintheboxananalysisofundesirablecontentinthecommoncrawlcorpus/c999b776-8e97-48ab-bec5-c27830d8d82b_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..c7e1e15489ae0ca66beb1ace6c970691a8cc3912 --- /dev/null +++ b/whatsintheboxananalysisofundesirablecontentinthecommoncrawlcorpus/c999b776-8e97-48ab-bec5-c27830d8d82b_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b056d6c5befd41217ed026b144a21912cc86b77be3e0ae698d074d3c9a2bbe3d +size 250899 diff --git a/whatsintheboxananalysisofundesirablecontentinthecommoncrawlcorpus/full.md b/whatsintheboxananalysisofundesirablecontentinthecommoncrawlcorpus/full.md new file mode 100644 index 0000000000000000000000000000000000000000..96191077a5c5f34a85af2a730933014d3a8ac59f --- /dev/null +++ b/whatsintheboxananalysisofundesirablecontentinthecommoncrawlcorpus/full.md @@ -0,0 +1,188 @@ +# What's in the Box? A Preliminary Analysis of Undesirable Content in the Common crawl Corpus + +Alexandra (Sasha) Luccioni + +Université de Montréal & + +Mila Quebec AI Institute + +sasha.luccioni@mila.quebec + +Joseph D. Viviano + +Mila Quebec AI Institute + +joseph@viviano.ca + +# Abstract + +Whereas much of the success of the current generation of neural language models has been driven by increasingly large training corpora, relatively little research has been dedicated to analyzing these massive sources of textual data. In this exploratory analysis, we delve deeper into the Common Crawl, a colossal web corpus that is extensively used for training language models. We find that it contains a significant amount of undesirable content, including hate speech and sexually explicit content, even after filtering procedures. We discuss the potential impacts of this content on language models and conclude with future research directions and a more mindful approach to corpus collection and analysis. + +# 1 Introduction + +In recent years, much of the progress in Natural Language Processing (NLP) research has been largely driven by Transformer-based language models, which have pushed forward the state-of-the-art in tasks such as question answering (Rajpurkar et al., 2018) and natural language inference (Bowman et al., 2015). However, these increasingly complex models also require increasingly large amounts of data to train them, which is often a combination of curated, high-quality datasets such as encyclopedic articles and books and non-curated content from the Web (Radford et al., 2018, 2019). This second category of large, non-curated dataset is becoming increasingly popular as they are required to train large language models. + +The current largest dataset used for training neural language models, the Common Crawl, is a non-curated corpus consisting of multilingual snapshots of the web. New versions of the Common Crawl are released monthly, with each version containing 200 to 300 TB of textual content scraped via automatic web crawling. This dwarfs other commonly used corpora such as English-language + +Wikipedia, which adds up to roughly 5.6 TB of data, and the BookCorpus, which only represents around 6 GB (Zhu et al., 2015). The Common Crawl has been used to train many of the recent neural language models in recent years, including the GPT model series (Radford et al., 2018; Brown et al., 2020), BERT (Devlin et al., 2018) and FastText (Grave et al., 2018) and, given its size, often represents the majority of data used to train these architectures. + +In the current article, we present an initial analysis of the Commoncrawl, highlighting the presence of several types of explicit and abusive content even after filtering. We discuss our findings and, given the potential downstream impact of this content on language models, we discuss the importance of ensuring that the corpora we use for training language models are extracted more mindfully and with more emphasis on their quality and propose avenues of research to achieve this goal. + +# 2 Related Work + +In recent years, a growing body of research in NLP has unearthed biases in common language models (Bolukbasi et al., 2016; Sheng et al., 2019; Zhao et al., 2019; Bordia and Bowman, 2019; Hutchinson et al., 2020). This work has raised important questions regarding the impact of these embedded biases on downstream decision-making, given the increasing usage of these models in various applications. Consequently, much work has also been dedicated to creating standardized diagnostic tests to detect these biases (Caliskan et al., 2017; May et al., 2019; Nadeem et al., 2020; Sweeney and Najafian, 2019) and to remove them (Bolukbasi et al., 2016; Zhao et al., 2018; Manzini et al., 2019), although the extent to which this is possible is still under debate (Gonen and Goldberg, 2019). In fact, research has found that "The biases found in Internet-scale language models like GPT-2 are representative of the data on which the model was trained" (So + +laiman et al., 2019), which can be directly linked to the presence of hate speech on the Internet (Abid et al., 2021). + +However, given the importance of this research, comparatively little attention has been dedicated to analyzing the corpora used to train language models. This is understandable because frequently used datasets such as the Commoncrawl contain truly massive amounts of data, making it challenging to mine it for meaningful insights. In fact, a recent survey on automatic web page classification has deemed the task difficult not only due to the complexity and heterogeneity of web content, but also due its the high computational cost, suggesting that machine learning (ML) approaches have much to contribute to it (Hashemi, 2020). While certain notable endeavors have indeed analyzed specific aspects of corpora such as the Commoncrawl (Kolias et al., 2014; Caswell et al., 2021) and Wikipedia (Hube, 2017), they have only scratched the surface of what these bodies of text contain. For instance, recent work has found that the Commoncrawl contained over 300,000 documents from unreliable news sites and banned subReddit pages containing hate speech and racism (Gehman et al., 2020), while complementary research has shown that individual training examples can be extracted by querying language models (Carlini et al., 2020), together illustrating that the presence of questionable content is a significant issue for statistical language models. In the current work, we endeavor to understand the content and quality of the Commoncrawl as a first step towards establishing more consistent approaches to filtering and refining it. + +# 3 Analyzing the Common Crawl + +Given its size, both downloading and analyzing the Commoncrawl are time-consuming and costly endeavors. The most recent version of the Commoncrawl, dating from November/December 2020, has 2.6 billion web pages in raw text format, saved in 'shards' each containing of tens of thousands of pages. Given our hardware constraints, we chose to focus on a subset of the corpus, randomly sampling $1\%$ of the files it contains, which after filtering by language amounts to roughly 115 GB of textual content or 5,835,339 web pages in total, which we analyzed in terms of hate speech, adult content, and efficacy of perplexity-based filtering1. In this work, + +we focus on detecting sexually-explicit and hate speech, since they represent common examples of "undesirable" content that can be generally seen as inappropriate for a language model to generate in most situations. We acknowledge that desirable model behaviour is application specific, and believe our findings can extend to any other "undesirable" topic that might be present in available language corpora. We present our results in the sections below. + +# 3.1 Detecting Hate Speech + +The existence of hate speech on the internet has been described as "an important societal problem of our time", with "profound and lasting" psychological effects on its victims (Mishra et al., 2019). As such, a substantial amount of NLP research dedicated to automating hate speech detection, with several datasets and approaches being proposed in recent years (Schmidt and Wiegand, 2017; Mishra et al., 2019; Vidgen and Derczynski, 2020; Kiritchenko and Mohammad, 2018). Most of this research is carried out on data extracted from social media sources such as Twitter (Founta et al., 2018; Basile et al., 2019; Waseem and Hovy, 2016) and Reddit (Tadesse et al., 2019; Farrell et al., 2019), with both ML-based (Badjatiya et al., 2017) and count-based approaches (Davidson et al., 2017) achieving comparable results (Fortuna and Nunes, 2018). In order to estimate the quantity of hate speech in the Common Crawl, we endeavored to compare 3 approaches: DELIMIT, a recent BERT-based model trained on social media data (Aluru et al., 2020), Hate Sonar, a Logistic Regression approach trained on data from Web fora and Twitter (Davidson et al., 2017) and a n-gram-based approach using a list of n-grams extracted from Hate Base. We present samples of text flagged by all of these approaches in Table 1, below. + +We found that the three approaches compared suggest similar proportions of websites containing hate speech: $5.24\%$ of websites from our sample were flagged by DELIMIT, $4.02\%$ by HateSonar, and $6.38\%$ by the n-gram approach 2. Qualitative analysis of a sample of sites flagged by each approach showed that while n-grams picked up on racial slurs, HateSonar also detected debates about racial supremacy and racially-charged conspiracy theories. Many of the sites that DELIMIT + +
ApproachText
HateSonarTheir US/Euro plan put in your face: demonic jews hate white goyim! Such sick and twisted people, white people are.
Delimitthey are only stupid arab from wp-ar haha Yeah, dumb ass n*gger †
N-gramnude attention whore asian bastards In America all male look like this homo
+ +Table 1: Examples of hate speech found by the approaches tested. Examples with $\dagger$ have been censored by the authors. + +flagged were adult content with mentions of violent acts towards specific ethnic groups, illustrating the fine line between sexual violence and hate speech, which we elaborate further in the following subsection. Generally speaking, the presence of even a small fraction of websites that incite hate in training corpora is worrisome since it can result in models that replicate this kind of discourse when prompted (Wolf et al., 2017; Carlini et al., 2020). + +# 3.2 Sexually Explicit Content + +Compared to hate speech, the detection of sexually explicit content has received less attention from the NLP community, with existing ML approaches focusing mainly on the detection of explicit images (Wehrmann et al., 2018; Rowley et al., 2006) and URLs (Matic et al., 2020), whereas n-gram-based approaches remain predominantly used in practice by web providers (Hammami et al., 2003; Polpinij et al., 2006; Ho and Watters, 2004). In our analysis, we used a list of n-grams extracted from adult websites in order to establish the percentage of websites from our sample that contained sexually explicit content; however, we found no available statistical or ML-based approach that we could use to compare our count-based approach with. The n-gram approach detected that $2.36\%$ of the web pages that we analyzed contained at least one of the words from our list, with $1.36\%$ containing 3 or more and $0.73\%$ containing 10 or more (see Table 3 for results). We show a sample of the URLs flagged by our approach in Table 2, below. + +While a few percent of sexually explicit content may not seem like much, the type of language and content contained on adult websites can have harmful repercussions. For instance, the prevalence of sexual violence towards women, especially towards women of color, on adult websites (Foubert et al., + +Page URL (http://removed) + +
adultmovietop100.com/
erohon.me/
celebrityfan.net/
queantube.com/
adelaide-femaleescorts.webcam
+ +Table 2: Sample of URLs of adult content websites identified by the n-gram approach. Protocol removed to prevent URL generation. + +2019; Shim et al., 2015; Fritz et al., 2020) may contribute to further dissemination and amplification of these biases in downstream models. As modern language models have no way to evaluate generation appropriateness, models trained with even a small proportion of these undesirable inputs cannot be guaranteed to avoid generating outputs with similar biases if presented with a specific context or prompt. This is a risk that is important to mitigate in applications, where the general-purpose language models can end up being used in applications used by sensitive groups in professional contexts or minors, such as chatbots and toys. + +# 3.3 Filtering by Perplexity Score + +While the analyses described above were carried out on unfiltered web pages from the Common Crawl, the training pipeline of many large-scale NLP models involves some type of filtering and cleaning, from excluding low-quality content (Grave et al., 2018) to fuzzy dedduplication (Brown et al., 2020). One such popular filtering approach is based on training a language model on a target, high-quality domain such as Wikipedia, and using it to calculate the perplexity score of web pages using this model (Wenzek et al., 2020). To test the efficacy of this scoring procedure, we calculated the perplexity score of each web page from our sample of the Common Crawl and used it to separate pages into 3 equal buckets (high, middle and low-quality) based on their perplexity. We compare the percentages of hate speech and sexually explicit content for the entire sample, as well as the high- and low-quality documents, in Table 3. + +While filtering by perplexity does seem to filter out many websites containing sexual content, it does not detect much of the hate speech that is flagged by the count-based or statistical methods. In fact, perplexity scores had low correlations with all detection methods tested (Figure 1). This supports the methodology of Wenzek et al. (2020), + +
Entire SampleHigh QualityLow Quality
1+ sexual n-grams2.36%1.81%3.97%
3+ sexual n-grams1.36%0.42%3.11%
10+ sexual n-grams0.73%0.08%1.98%
1+ hate n-grams17.78%18.95%17.19%
3+ hate n-grams6.38%6.19%8.26%
10+ hate n-grams1.16%1.17%1.70%
Hate speech (Sonar)4.02%3.47%5.09%
Hate speech (Delimit)5.24%5.77%5.66%
+ +Table 3: Comparison of hate speech and sexual content detected in the entire corpus, as well as high- and low-quality sites. + +who noted that while "perplexity was a relative good proxy for quality", also argued that some of the lower-quality texts could still be useful for specific applications, and therefore did not use it to exclude documents from the training set of their language model. While we are exploring ways of modifying the original approach in order to be more discerning, we believe that there more nuanced metrics that can be used for estimating and filtering documents based on text, potentially coupling embedding-based approaches with statistical ones. + +# 3.4 Behaviour of Different Detection Methods + +The approaches that we compared in the current study are different in the features that they use and techniques employed for detecting particular types of content. HateSonar employs classical NLP techniques for hate speech detection, constructing features from Penn Part-of-Speech N-grams with TF-IDF weighting based on a hand-crafted hate speech dataset, training simple classifier ensembles using Support Vector Machines, random forests, naive Bayes, and linear models. Delimit, on the other hand, is A BERT-based model trained on Twitter and Reddit posts, not relying on any handcrafted features. Our simple n-gram approach unsurprises + +![](images/4abf4b15dabb3e91d08f808c753794f4faf6bba94431ed8d352179caa0d14040.jpg) +Figure 1: Correlation coefficients (Pearson's $r$ ) calculated between all content metrics investigated and perplexity, a commonly-used text quality metric. + +ingly was more in agreement with HateSonar than Delimit, given that both rely on count-based features. The fact that all methods identified different instances of clear hate speech implies that we are far from a general purpose dataset-filtering approach. These results also imply that deep learning models learn very different features to classify hate speech than other methods, and given their sensitivity to the specific composition of the dataset used to train them (as exposed by the propensity of large models to memorize training examples (Carlini et al., 2020)), the presence of undesirable content in the corpora used to train them should be taken seriously. + +# 4 Discussion + +# 4.1 Summary of Results + +We recognize that the exploratory work presented above is only the tip of the iceberg in terms of the analyses that can be done on the massive web corpora that are feeding our language models. However, analyzing the Common Crawl would require computational resources far in excess of what is available to most research institutions. We therefore hope that this initial analysis will inspire our fellow researchers to continue to dig deeper into this topic, and to propose more scalable, thorough, and nuanced approaches for analyzing the massive corpora used to train language models. We also recognize this analysis would have been more comprehensive on a small curated dataset, but given the + +amount of data needed to train modern language models, we believe the community needs to move beyond analysis techniques only compatible with small-data, toward something that will scale to the datasets used to train these large models. + +Also, while we have currently adopted a purely descriptive approach, we feel that it is worth discussing and debating the consequences of our analysis, and those of our peers, within the NLP community. While it can be argued that the Common Crawl corpus is an accurate portrayal of the discourse of modern society – which includes sexual content, hate speech, and racial and gender biases – we believe that it is up for debate whether this discourse is the one that we, as a community, want to use to train the models that translate our texts, influence our search results and answer our questions. Notably, the Common Crawl over-represents those populations that are avid users of the internet: younger, English-speaking individuals from developed countries, who are those who have the most access to the internet globally (World Bank, 2018). Furthermore, internet communities supported by anonymity and and particular norms can amplify toxic discourse that would not be found in mainstream corpora (Massanari, 2017) often exacerbated by the well-documented 'online disinhibition' phenomenon where users find themselves more likely to engage in anti-social behaviours due to the lack of immediate social feedback (Wachs et al., 2019; Mathew et al., 2019; de Lima et al., 2021). This can further perpetuate the lack of diverse, representative language models that can adequately mirror society beyond the boundaries of internet communities. + +# 4.2 Future Work + +Given the general superior performance of large language models on common benchmarks, and that they require ever larger datasets to train them, we believe it is important that for the ML community to carry out a more extensive analysis of: 1) the impact of undesirable content in the datasets used to train these models on downstream performance; 2) the effect of properly filtering these examples out of the dataset before model training, and 3) approaches for regularizing model outputs to be acceptable regardless of the data used to train the model. All three directions require a better understanding of the contents of the datasets, which we believe requires new tools that are scalable to the + +Common Crawl (or similarly large and diverse corpora) to identify such examples. Models trained to detect undesirable examples, like the ones used in this paper, need to be improved such that they can reliably generalize to the Common Crawl, which constitutes a significant undertaking. Additionally, future work could explore the utility of controlling model generation using labelled "undesirable" examples (Zhang et al., 2020; Engel et al., 2017), or human-in-the-loop learning methods (Wang et al., 2021) for fine-tuning a language model trained using undesirable examples. It will also be important to evaluate whether curation is sufficient: it remains possible that a model could create an undesirable generation from multiple distinct innocuous examples (Bender et al., 2021; Gehman et al., 2020). It is also worth considering that for some applications, task-focused models with curated training examples may perform better than large models trained on unfiltered corpora, so that their behaviour can be more reliably guaranteed: these are all interesting avenues for future work. + +Finally, while larger corpora generally result in better models (Kaplan et al., 2020; Sun et al., 2017), data quality and corpora content also plays a major role in the caliber and appropriateness of these models for the various downstream applications (Florez, 2019; Abid et al., 2021; Bhardwaj et al., 2021). To produce high quality and safe neural language models will likely require the community to adopt more mindful data collection practices (Gehman et al., 2020; Bender and Friedman, 2018; Gebru et al., 2018; Jo and Gebru, 2020; Paullada et al., 2020; Bender et al., 2021), establish standardized filtering pipelines for corpora (Roziewski and Stokowiec, 2016; Ortiz Suarez et al., 2019; Wenzek et al., 2020), and develop methods for evaluating the bias in trained models (Schick et al., 2021). We recognize that this is not a straightforward task with a one-size-fits all solution, but we propose that as much attention should be dedicated to the corpora used for training language models as to the models themselves, and that corpora transparency is a prerequisite for language model accountability. + +# References + +Abid, A., Farooqi, M., and Zou, J. (2021). Persistent Anti-Muslim Bias in Large Language Models. arXiv preprint arXiv:2101.05783. +Aluru, S. S., Mathew, B., Saha, P., and Mukherjee, A. (2020). Deep learning models for multilingual hate speech detection. arXiv preprint arXiv:2004.06465. +Badjatiya, P., Gupta, S., Gupta, M., and Varma, V. (2017). Deep learning for hate speech detection in tweets. In Proceedings of the 26th International Conference on World Wide Web Companion, pages 759-760. +Basile, V., Bosco, C., Fersini, E., Debora, N., Patti, V., Pardo, F. M. R., Rosso, P., Sanguinetti, M., et al. (2019). Semeval-2019 task 5: Multilingual detection of hate speech against immigrants and women in Twitter. In 13th International Workshop on Semantic Evaluation, pages 54-63. Association for Computational Linguistics. +Bender, E., Gebru, T., McMillan-Major, A., et al. (2021). On the dangers of stochastic parrots: Can language models be too big. Proceedings of FAccT. +Bender, E. M. and Friedman, B. (2018). Data statements for natural language processing: Toward mitigating system bias and enabling better science. Transactions of the Association for Computational Linguistics, 6:587-604. +Bhardwaj, R., Majumder, N., and Poria, S. (2021). Investigating gender bias in BERT. Cognitive Computation, pages 1-11. +Bolukbasi, T., Chang, K.-W., Zou, J. Y., Saligrama, V., and Kalai, A. T. (2016). Man is to computer programmer as woman is to homemaker? debiasing word embeddings. In Advances in Neural Information Processing Systems, pages 4349-4357. +Bordia, S. and Bowman, S. R. (2019). Identifying and reducing gender bias in word-level language models. arXiv preprint arXiv:1904.03035. +Bowman, S. R., Angeli, G., Potts, C., and Manning, C. D. (2015). A large annotated corpus for learning natural language inference. arXiv preprint arXiv:1508.05326. +Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020). Language models are few-shot learners. arXiv preprint arXiv:2005.14165. +Caliskan, A., Bryson, J. J., and Narayanan, A. (2017). Semantics derived automatically from language corpora contain human-like biases. Science, 356(6334):183-186. + +Carlini, N., Tramer, F., Wallace, E., Jagielski, M., Herbert-Voss, A., Lee, K., Roberts, A., Brown, T., Song, D., Erlingsson, U., et al. (2020). Extracting training data from large language models. arXiv preprint arXiv:2012.07805. +Caswell, I., Kreutzer, J., Wang, L., Wahab, A., van Esch, D., Ulzii-Orshikh, N., Tapo, A., Subramani, N., Sokolov, A., Sikasote, C., et al. (2021). Quality at a glance: An audit of web-crawled multilingual datasets. arXiv preprint arXiv:2103.12028. +Davidson, T., Warmsley, D., Macy, M., and Weber, I. (2017). Automated hate speech detection and the problem of offensive language. In Proceedings of the International AAAI Conference on Web and Social Media, volume 11. +de Lima, L. H. C., Reis, J., Melo, P., Murai, F., and Benevenuto, F. (2021). Characterizing (un) moderated textual data in social systems. arXiv preprint arXiv:2101.00963. +Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2018). BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805. +Engel, J., Hoffman, M., and Roberts, A. (2017). Latent constraints: Learning to generate conditionally from unconditional generative models. arXiv preprint arXiv:1711.05772. +Farrell, T., Fernandez, M., Novotny, J., and Alani, H. (2019). Exploring misogyny across the manosphere in reddit. In Proceedings of the 10th ACM Conference on Web Science, pages 87-96. +Florez, O. U. (2019). On the unintended social bias of training language generation models with data from local media. arXiv preprint arXiv:1911.00461. +Fortuna, P. and Nunes, S. (2018). A survey on automatic detection of hate speech in text. ACM Computing Surveys (CSUR), 51(4):1-30. +Foubert, J. D., Blanchard, W., Houston, M., and Williams, R. R. (2019). Pornography and sexual violence. In Handbook of Sexual Assault and Sexual Assault Prevention, pages 109-127. Springer. +Founta, A., Djouvas, C., Chatzakou, D., Leontiadis, I., Blackburn, J., Stringhini, G., Vakali, A., Sirivianos, M., and Kourtellis, N. (2018). Large scale crowdsourcing and characterization of Twitter abusive behavior. In Proceedings of the International AAAI Conference on Web and Social Media, volume 12. +Fritz, N., Malic, V., Paul, B., and Zhou, Y. (2020). Worse than objects: The depiction of black women and men and their sexual relationship in pornography. Gender Issues, pages 1-21. +Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé III, H., and Crawford, K. (2018). Datasheets for datasets. arXiv preprint arXiv:1803.09010. + +Gehman, S., Gururangan, S., Sap, M., Choi, Y., and Smith, N. A. (2020). Realtoxicityprompts: Evaluating neural toxic degeneration in language models. arXiv preprint arXiv:2009.11462. +Gonen, H. and Goldberg, Y. (2019). Lipstick on a pig: Debiasing methods cover up systematic gender biases in word embeddings but do not remove them. arXiv preprint arXiv:1903.03862. +Grave, E., Bojanowski, P., Gupta, P., Joulin, A., and Mikolov, T. (2018). Learning word vectors for 157 languages. arXiv preprint arXiv:1802.06893. +Hammami, M., Chahir, Y., and Chen, L. (2003). Webguard: Web based adult content detection and filtering system. In Proceedings IEEE/WIC International Conference on Web Intelligence (WI 2003), pages 574-578. IEEE. +Hashemi, M. (2020). Web page classification: a survey of perspectives, gaps, and future directions. *Multimedia Tools and Applications*, pages 1-25. +Ho, W. H. and Watters, P. A. (2004). Statistical and structural approaches to filtering internet pornography. In 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No. 04CH37583), volume 5, pages 4792-4798. IEEE. +Hube, C. (2017). Bias in Wikipedia. In Proceedings of the 26th International Conference on World Wide Web Companion, pages 717-721. +Hutchinson, B., Prabhakaran, V., Denton, E., Webster, K., Zhong, Y., and Denuyl, S. (2020). Social biases in NLP models as barriers for persons with disabilities. arXiv preprint arXiv:2005.00813. +Jo, E. S. and Gebru, T. (2020). Lessons from archives: strategies for collecting sociocultural data in machine learning. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pages 306-316. +Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., Gray, S., Radford, A., Wu, J., and Amodei, D. (2020). Scaling laws for neural language models. arXiv preprint arXiv:2001.08361. +Kiritchenko, S. and Mohammad, S. M. (2018). Examining gender and race bias in two hundred sentiment analysis systems. arXiv preprint arXiv:1805.04508. +Kolias, V., Anagnostopoulos, I., and Kayafas, E. (2014). Exploratory analysis of a terabyte scale web corpus. arXiv preprint arXiv:1409.5443. +Manzini, T., Lim, Y. C., Tsvetkov, Y., and Black, A. W. (2019). Black is to criminal as caucasian is to police: Detecting and removing multiclass bias in word embeddings. arXiv preprint arXiv:1904.04047. +Massanari, A. (2017). # gamerge and the fappening: How reddit's algorithm, governance, and culture support toxic technocultures. New media & society, 19(3):329-346. + +Mathew, B., Illendula, A., Saha, P., Sarkar, S., Goyal, P., and Mukherjee, A. (2019). Temporal effects of unmoderated hate speech in gab. arXiv preprint arXiv:1909.10966. +Matic, S., Iordanou, C., Smaragdakis, G., and Laoutaris, N. (2020). Identifying sensitive URLs at web-scale. In Proceedings of the ACM Internet Measurement Conference, pages 619-633. +May, C., Wang, A., Bordia, S., Bowman, S. R., and Rudinger, R. (2019). On measuring social biases in sentence encoders. arXiv preprint arXiv:1903.10561. +Mishra, P., Yannakoudakis, H., and Shutova, E. (2019). Tackling online abuse: A survey of automated abuse detection methods. arXiv preprint arXiv:1908.06024. +Nadeem, M., Bethke, A., and Reddy, S. (2020). Stereoset: Measuring stereotypical bias in pretrained language models. arXiv preprint arXiv:2004.09456. +Ortiz Suarez, P. J., Sagot, B., and Romary, L. (2019). Asynchronous pipelines for processing huge corpora on medium to low resource infrastructures. Proceedings of the Workshop on Challenges in the Management of Large Corpora (CMLC-7) 2019. Cardiff, 22nd July 2019, pages 9 - 16, Mannheim. Leibniz-Institut fur Deutsche Sprache. +Paullada, A., Raji, I. D., Bender, E. M., Denton, E., and Hanna, A. (2020). Data and its (dis) contents: A survey of dataset development and use in machine learning research. arXiv preprint arXiv:2012.05345. +Polpinij, J., Chotthanom, A., Sibunruang, C., Chamchong, R., and Puangpronpitag, S. (2006). Content-based text classifiers for pornographic web filtering. In 2006 IEEE International Conference on Systems, Man and Cybernetics, volume 2, pages 1481-1485. IEEE. +Radford, A., Narasimhan, K., Salimans, T., and Sutskever, I. (2018). Improving language understanding by generative pre-training. +Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., and Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI blog, 1(8):9. +Rajpurkar, P., Jia, R., and Liang, P. (2018). Know what you don't know: Unanswerable questions for SQuAD. arXiv preprint arXiv:1806.03822. +Rowley, H. A., Jing, Y., and Baluja, S. (2006). Large scale image-based adult-content filtering. Google Research Paper. +Roziewski, S. and Stokowiec, W. (2016). Language-crawl: A generic tool for building language models upon common-crawl. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 2789-2793. + +Schick, T., Udupa, S., and Schütze, H. (2021). Self-diagnosis and self-debiasing: A proposal for reducing corpus-based bias in nlp. arXiv preprint arXiv:2103.00453. +Schmidt, A. and Wiegand, M. (2017). A survey on hate speech detection using natural language processing. In Proceedings of the Fifth International workshop on natural language processing for social media, pages 1-10. +Sheng, E., Chang, K.-W., Natarajan, P., and Peng, N. (2019). The woman worked as a babysitter: On biases in language generation. arXiv preprint arXiv:1909.01326. +Shim, J. W., Kwon, M., and Cheng, H.-I. (2015). Analysis of representation of sexuality on women's and men's pornographic websites. Social Behavior and Personality: an international journal, 43(1):53-62. +Solaiman, I., Brundage, M., Clark, J., Askell, A., Herbert-Voss, A., Wu, J., Radford, A., Krueger, G., Kim, J. W., Kreps, S., et al. (2019). Release strategies and the social impacts of language models. arXiv preprint arXiv:1908.09203. +Sun, C., Shrivastava, A., Singh, S., and Gupta, A. (2017). Revisiting unreasonable effectiveness of data in deep learning era. In Proceedings of the IEEE international conference on computer vision, pages 843-852. +Sweeney, C. and Najafian, M. (2019). A transparent framework for evaluating unintended demographic bias in word embeddings. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1662-1667. +Tadesse, M. M., Lin, H., Xu, B., and Yang, L. (2019). Detection of depression-related posts in reddit social media forum. IEEE Access, 7:44883-44893. +Vidgen, B. and Derczynski, L. (2020). Directions in abusive language training data: Garbage in, garbage out. arXiv preprint arXiv:2004.01670. +Wachs, S., Wright, M. F., and Vazsonyi, A. T. (2019). Understanding the overlap between cyberbullying and cyberhate perpetration: Moderating effects of toxic online disinhibition. Criminal Behaviour and Mental Health, 29(3):179-188. +Wang, Z. J., Choi, D., Xu, S., and Yang, D. (2021). Putting humans in the natural language processing loop: A survey. arXiv preprint arXiv:2103.04044. +Waseem, Z. and Hovy, D. (2016). Hateful symbols or hateful people? predictive features for hate speech detection on Twitter. In Proceedings of the NAACL student research workshop, pages 88-93. +Wehrmann, J., Simões, G. S., Barros, R. C., and Cavalcante, V. F. (2018). Adult content detection in videos with convolutional and recurrent neural networks. Neurocomputing, 272:432-438. + +Wenzek, G., Lachaux, M.-A., Conneau, A., Chaudhary, V., Guzmán, F., Joulin, A., and Grave, É. (2020). CCNet: Extracting high quality monolingual datasets from web crawl data. In Proceedings of The 12th Language Resources and Evaluation Conference, pages 4003-4012. +Wolf, M. J., Miller, K. W., and Grodzinsky, F. S. (2017). Why we should have seen that coming: comments on microsoft's tay "experiment," and wider implications. The ORBIT Journal, 1(2):1-12. +World Bank (2018). Individuals using the Internet. https://data.worldbank.org/indicator/IT.NETUSER.ZS?end=2017&locations $\equiv$ US& start $= 2015$ .Accessed:2021-01-10. +Zhang, Y., Wang, G., Li, C., Gan, Z., Brockett, C., and Dolan, B. (2020). Pointer: Constrained text generation via insertion-based generative pre-training. arXiv preprint arXiv:2005.00558. +Zhao, J., Wang, T., Yatskar, M., Cotterell, R., Ordonez, V., and Chang, K.-W. (2019). Gender bias in contextualized word embeddings. arXiv preprint arXiv:1904.03310. +Zhao, J., Zhou, Y., Li, Z., Wang, W., and Chang, K.-W. (2018). Learning gender-neutral word embeddings. arXiv preprint arXiv:1809.01496. +Zhu, Y., Kiros, R., Zemel, R., Salakhutdinov, R., Urtasun, R., Torralba, A., and Fidler, S. (2015). Aligning books and movies: Towards story-like visual explanations by watching movies and reading books. In Proceedings of the IEEE international conference on computer vision, pages 19-27. \ No newline at end of file diff --git a/whatsintheboxananalysisofundesirablecontentinthecommoncrawlcorpus/images.zip b/whatsintheboxananalysisofundesirablecontentinthecommoncrawlcorpus/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..2c239ed7f2bd236b8650d52e8daba1e8b993e957 --- /dev/null +++ b/whatsintheboxananalysisofundesirablecontentinthecommoncrawlcorpus/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d618798ee2ec9a2b937af141be10571cbcb222d0995cf15d94d50542a648f472 +size 125066 diff --git a/whatsintheboxananalysisofundesirablecontentinthecommoncrawlcorpus/layout.json b/whatsintheboxananalysisofundesirablecontentinthecommoncrawlcorpus/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..f5cd995eb510b3553a15d4cea80d6f8f4224fd1b --- /dev/null +++ b/whatsintheboxananalysisofundesirablecontentinthecommoncrawlcorpus/layout.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fa54f031b05f6ae966189b54a2b411f6c48a6c979642b7b9a2be75fbdcfaa72a +size 229462 diff --git a/whenischarbetterthansubwordasystematicstudyofsegmentationalgorithmsforneuralmachinetranslation/6e625f2d-4db6-4858-926b-6a0207a93c79_content_list.json b/whenischarbetterthansubwordasystematicstudyofsegmentationalgorithmsforneuralmachinetranslation/6e625f2d-4db6-4858-926b-6a0207a93c79_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..99e452aaef2da9966839e99ae8ce8abdacd37072 --- /dev/null +++ b/whenischarbetterthansubwordasystematicstudyofsegmentationalgorithmsforneuralmachinetranslation/6e625f2d-4db6-4858-926b-6a0207a93c79_content_list.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c65536af9108701cd06e2f5b8fd971e8782aa7176cbe73eb95eea2c0aaae1366 +size 47858 diff --git a/whenischarbetterthansubwordasystematicstudyofsegmentationalgorithmsforneuralmachinetranslation/6e625f2d-4db6-4858-926b-6a0207a93c79_model.json b/whenischarbetterthansubwordasystematicstudyofsegmentationalgorithmsforneuralmachinetranslation/6e625f2d-4db6-4858-926b-6a0207a93c79_model.json new file mode 100644 index 0000000000000000000000000000000000000000..a2980bcdcfc8f514f244351076ecbb374bcebd8b --- /dev/null +++ b/whenischarbetterthansubwordasystematicstudyofsegmentationalgorithmsforneuralmachinetranslation/6e625f2d-4db6-4858-926b-6a0207a93c79_model.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:10110506b8133598b46f9af2d59978ca76254fed905bfa89eab9af87b08f4633 +size 57751 diff --git a/whenischarbetterthansubwordasystematicstudyofsegmentationalgorithmsforneuralmachinetranslation/6e625f2d-4db6-4858-926b-6a0207a93c79_origin.pdf b/whenischarbetterthansubwordasystematicstudyofsegmentationalgorithmsforneuralmachinetranslation/6e625f2d-4db6-4858-926b-6a0207a93c79_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..fbb24b1013fa2b9413e773267b0295188468ca96 --- /dev/null +++ b/whenischarbetterthansubwordasystematicstudyofsegmentationalgorithmsforneuralmachinetranslation/6e625f2d-4db6-4858-926b-6a0207a93c79_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:939cf997340f4543577d050ba043416530ff6fe1096c83b8b0f11ae0dcf1ad0e +size 375524 diff --git a/whenischarbetterthansubwordasystematicstudyofsegmentationalgorithmsforneuralmachinetranslation/full.md b/whenischarbetterthansubwordasystematicstudyofsegmentationalgorithmsforneuralmachinetranslation/full.md new file mode 100644 index 0000000000000000000000000000000000000000..581cf20443642ec95157c47272a9f51980f5ab47 --- /dev/null +++ b/whenischarbetterthansubwordasystematicstudyofsegmentationalgorithmsforneuralmachinetranslation/full.md @@ -0,0 +1,194 @@ +# When is Char Better Than Subword: A Systematic Study of Segmentation Algorithms for Neural Machine Translation + +Jiahuan Li * Yutong Shen * Shujian Huang † Xinyu Dai Jiajun Chen + +National Key Laboratory for Novel Software Technology, Nanjing University, China + +{lijh, shenyt}@smail.nju.edu.cn, {huangsj, daixinyu, chenjj}@nju.edu.cn + +# Abstract + +Subword segmentation algorithms have been a de facto choice when building neural machine translation systems. However, most of them need to learn a segmentation model based on some heuristics, which may produce suboptimal segmentation. This can be problematic in some scenarios when the target language has rich morphological changes or there is not enough data for learning compact composition rules. Translating at fully character level has the potential to alleviate the issue, but empirical performances of character-based models has not been fully explored. In this paper, we present an in-depth comparison between character-based and subword-based NMT systems under three settings: translating to typologically diverse languages, training with low resource, and adapting to unseen domains. Experimental results show strong competitiveness of character-based models. Further analyses show that compared to subword-based models, character-based models are better at handling morphological phenomena, generating rare and unknown words, and more suitable for transferring to unseen domains. + +# 1 Introduction + +Neural machine translation (NMT) has achieved great success in recent years. Modern NMT systems typically operate on subword level, using segmentation algorithms such as byte pair encoding (BPE) (Sennrich et al., 2016) or Morfessor (Creutz and Lagus, 2002). Compared to word-level models, subword segmentation helps overcome the out-of-vocabulary (OOV) problem and make better use of morphological information in the surface form. + +Despite their empirical effectiveness, subword algorithms may produce improper segmentation due to their data-dependent nature. NMT models + +are typically robust to such errors when trained on large corpora or the target language is regular in morphological changes, like French or German. However, the problem will arise when such conditions are not met, i.e. there is not enough data for learning compact composition rules or the target language is morphologically rich and complex. + +An alternative segmentation choice is to use fully character-level (CHAR) models (Lee et al., 2017; Cherry et al., 2018; Gupta et al., 2019; Gao et al., 2020; Banar et al., 2020), which has the potential to alleviate above issues. CHAR does not need to learn any segmentation rules and keeps all available information in the surface form, avoiding the risk of information loss due to improper segmentation. What is more, the main pain point of CHAR that it takes too long to train is less obvious in above settings since there is not as much data as in the rich resource setting. However, there has not been a comprehensive study in these settings. + +In this paper, we conduct a systematic comparison between CHAR and other subword algorithms, e.g. BPE and Morfessor. Experiments show strong competitiveness of CHAR under three settings: translating to typologically diverse languages (Section 2), training with low resource (Section 3), and adapting to distant domains (Section 4). Further analyses show that compared to subword algorithms, the benefits of CHAR mainly come from better capture of the morphological phenomena, better generation of rare and unknown words, and better translation of domain-specific words. + +# 2 Translation Across Typologically Diverse Languages + +Human languages are known to exhibit diverse morphological phenomena, which could serve as a principle to classify languages into different morphological categories, such as fusional, agglutinative, introflexive and isolating. While previous + +
WordCharBPEMorf.
F.Fr39.1/.58040.1/.58941.2/.59739.6/.592
Ro31.1/.48733.9/.52632.9/.51730.6/.517
A.Fi21.9/.41223.5/.48722.3/.47221.7/.466
Tr19.8/.39622.8/.45621.1/.44016.9/.437
In.Hi14.0/.26215.6/.29014.8/.28514.8/.276
Ar22.5/.45124.7/.49123.9/.48123.5/.481
Is.Vi21.6/.37422.5/.38522.2/.38121.1/.373
Ml22.9/.32425.0/.34924.3/.34724.1/.356
+ +works only focus on performances of character-level models when translating to fusional and agglutinative languages (Gupta et al., 2019; Libovicky and Fraser, 2020), we conduct a comprehensive study covering all four morphological categories. + +# 2.1 Experiment Setup + +Dataset We consider the translation from English to eight target languages representing four morphological categories, i.e. French (Fr) and Romanian (Ro) for fusional, Finnish (Fi) and Turkish (Tr) for agglutinative, Hebrew (He) and Arabic (Ar) for introflexive, and Vietnamese (Vi) and Malaysian (MI) for isolating. We use OPUS-100 corpus1 (Tiedemann, 2012), which consists of 1M parallel sentences for each language pair. + +Model and Hyperparameters We use the Transformer architecture (Vaswani et al., 2017) throughout all experiments. To ensure results' reliability, we run an exhaustive search of hyperparameters including batch size and learning rate. Detailed hyperparameters can be found in Appendix A. + +# 2.2 Results + +The results are listed in Table 1. We can see that CHAR outperforms other algorithms in 7 out of 8 languages in terms of BLEU (Papineni et al., 2002) and chrF3 (Popovic, 2015), showing strong competitiveness of CHAR's ability across languages. The only exception is the En-Fr language pair, which are known to be quite similar and is beneficial for BPE to learn a joint segmentation model. + +It is intuitive that BPE and Morfessor cannot outperform CHAR on introflexive languages (Hi, Ar). Introflexive languages follow non-concatenative morphology (McCarthy, 1981), i.e. grammatical + +Table 1: BLEU/chrF3 scores of systems translating from English to languages of different morphological categories, using different segmentation algorithms. Best score in each line is shown in bold. + +
WordCharBPEMorf.
Comp. adj.55.670.863.060.0
Det. poss.49.683.078.078.4
Pron. hum60.667.066.266.6
Local case36.661.850.647.6
Pron. gender73.676.679.079.0
Verb neg96.697.298.498.0
Preposition33.869.260.264.2
Future tense51.443.853.850.8
Past tense83.291.887.490.8
Pron. plural74.679.277.475.2
Noun plural48.876.062.860.8
Det. definite38.438.840.844.8
Named Ent.9.270.466.430.2
Number65.496.691.277.8
+ +Table 2: Performance of different segmentation algorithms on the MorphEval En-Fi benchmark. Each row represents a kind of morphological phenomenon. + +information is conveyed by directly modifying the root words. This makes it hard for linear segmenting methods such as BPE and Morfessor to work well. This finding is also consistent with previous research on other tasks (Zhu et al., 2019). + +For isolating languages (Vi, MI), there are rare morphological phenomena indicating grammatical relations, so segmentation algorithms do not greatly affect the performance. We can see that the two open-vocabulary segmentation algorithms (CHAR, BPE) show comparable performances. + +Surprisingly, even for highly agglutinative languages such as Finnish and Turkish, which has very regular morphological changes by adding affixes or suffixes, CHAR still achieves better performance. + +# 2.3 Analysis on MorphEval + +To understand where the advantages of CHAR model come from, we take Finnish as an example and evaluate the morphological competence of different models using MorphEval test suites (Burlot et al., 2018). MorphEval generates pairs of source sentences that differ by one kind of morphological phenomena, and assesses a MT system's ability by computing the percentage of its generated target sentences that convey as the source sentences. Higher accuracy means the model is more sensitive to the current morphological phenomenon. + +As shown in Table 2, CHAR performs the best in 10 out of 14 tests. Among these 10 tests, in comparative adjectives, possessive determiner, local postposition case, preposition case, plural nouns, CHAR surpasses other algorithms notably by at least $5\%$ accuracy. This indicates CHAR's strong ability to capture the fine-grained morphological + +![](images/6c5a6045bd44151254a7e8d2d47aecd7af4e406f94582d58aa2ccd55a72cc5a7.jpg) +Figure 1: BLEU and chrF3 score curves using different amount of parallel data for training En-De (above) and En-Fi (below) translation systems. + +phenomena, which is crucial for MT models when translating into morphologically rich languages. + +Interestingly, three of four morphological phenomena on which CHAR falls behind are so-called stability features (Burlot et al., 2018), which are expressed differently in the source language but should be expressed identically in the target language. The disadvantage of CHAR in this kind of phenomena shows CHAR-based model may be less robust to lexical changes to source-side changes, and the reason needs to be further researched. + +# 3 Translation with Low Resource + +Subword algorithms help alleviate the OOV problem. However, most of them are based on heuristics and may produce wrong segmentation. While this problem is not so evident when there is enough data to learn robust composition rules, in low-resource setting it could be a different story and their effectiveness should be examined. While for CHAR, pure character sequences can directly provide all the information to the model for learning the composition rules. Therefore a prudent choice of segmentation should be studied in this setting. + +# 3.1 Experiment Setup + +We perform evaluation on WMT14 En-De $^3$ and WMT17 En-Fi $^4$ dataset. Datasets of size 50k, 100k, 200k, 500k and 1000k are subsampled from the original training dataset and serve as training data + +![](images/40ddef2605a2b85ac6e3f08cda28152111ddf33049dc5e8d780a2cdd9477b3f3.jpg) +Figure 2: Recall rates of unknown and rare words generated by systems based on different tokenizers models. Words appearing no more than 5 times in the training set are considered as rare words. + +of different resource conditions. For validation and test, we use the original development and test split. + +Previous works (Sennrich and Zhang, 2019; Nguyen and Chiang, 2017) show that in low resource settings the evaluation results can be sensitive to model size (e.g. hidden dimension, layer number) and the number of BPE merges $k$ , so we run an additional search of hidden dimension, layer number and $k$ , and report the best results in this section. See Appendix A for details. + +# 3.2 Results + +We evaluate models with BLEU andchrF3. The results are showed in Figure 1. In general, the performances of CHAR and BPE are on par, and are better than Word and Morfessor. In different data conditions, the results varies. + +medium-resource When there are plenty resources, e.g. 500k and 1000k, the performance of CHAR and BPE are comparable but different for different language pairs. For En-Fi, CHAR is better than BPE. It is because morphological changes in Finnish are quite complex. More fine-grained segmentation like CHAR is needed to learn corresponding rules. Conversely, German's morphological changes are so regular that BPE can learn most of merging rules, making them performing better. + +low-resource When the corpus size is 50k to 200k, CHAR performs the best among four segmentation methods. BPE and Morfessor usually regard frequently occurring words as single tokens, many of which contain rich morphological information. This, together with the improper segmentation problem, prevents NMT models from learning correct composition rules, damaging the model's generalization ability on rare and unknown words. In low resource setting this problem would be more se + +![](images/455254e9278d7e2ba306abab8d48a86e6ea4d5dcbdfa0a204a6bd48cdf6d89c2.jpg) +(a) Average OOD BLEU (No Adapt). + +![](images/65aa1c107484775cda535fd331c15748d60b3b41b1ecc5ae552a23300fa66085.jpg) +Figure 3: Domain robustness of translation systems based on different segmentation algorithms. + +![](images/78f7c6f0355b9c475d700ea3fd0db53734d5f23439a0f690f17d4492aa1e2cdf.jpg) +(b) Average OOD BLEU (Finetune). +(c) Recall of different word types. + +vere, since there are much more rare and unknown words but not enough data for learning compact composition rules. + +Compared with subwords, character-based models learn combinations directly from character sequences. Not limited to fixed char sequence patterns in subwords, more words with different morphological changes can be generated through CHAR. Therefore, CHAR can learn more correct composition rules than subword-based model, leading to better translation of rare and unknown words. + +# 3.3 Analysis on Rare and Unknown Words + +To further support the above analysis, we evaluate the translation quality of rare and unknown words by calculating their recall rates. The results are showed in Figure 2. We can see that CHAR has achieved the highest recall rates of rare and unknown words. Although, as the resource increases, the gap between CHAR and BPE is shrinking gradually, the results can still prove that CHAR can capture more morpheme information, performing better at generating rare and unknown words. + +# 4 Translation Across Distant Domains + +Domain robustness (Müller et al., 2020), which refers to models' generalization ability on unseen domains, is important for NMT applications. However, subword algorithms need to learn segmentation rules from a given corpus, which may be domain-specific. When applied to a new domain, they may improperly segment target-domain specific words, hurting the domain robustness. In contrast, CHAR does not suffer from the issue. In this section, we investigate how different segmentation algorithms affect NMT models' domain robustness. + +# 4.1 Experiment Setup + +We use the same corpora as (Koehn and Knowles, 2017), which is a De-En dataset covering subsets of four domains: Law, Medical, IT and Koran. + +Following Koehn and Knowles (2017), each time we train a source domain model on one of four subsets and report results on test sets of the other three domain. We experiment in two settings: No Adapt and Finetune. The first one involves no target domain data, while the latter uses randomly sampled 100k sentence pairs from target domain data to finetune the source domain model. + +# 4.2 Results + +We report the average out-of-domain (OOD) BLEU scores of NMT systems based on different segmentation algorithms in Figure 3a and Figure 3b. As can be seen from the figure, CHAR surpasses other algorithms in almost all settings, except when finetuning from Medical to others. This illustrates the suitability of CHAR for domain robustness, especially when there is no enough data for adaptation. + +# 4.3 Analysis on Different Types of Words + +To understand the advantages of CHAR, we take the setting of finetuning from $IT$ to Medical as an example and analyze performances on different types of words. Specifically, we divide words in the test set into three types: (1) Domain-specific words occur only in the target domain training data; (2) Common words occur in both the source and target domain training data; (3) OOV words do not occur in both training data. + +The result can be seen in Figure 3c. CHAR achieves better performance on OOV words, which is consistent with findings in Section 3. While performances of CHAR and subword-based algorithms are on par on common words, CHAR outperforms the others by a large margin on domain-specific words. This suggests that the advantage of CHAR mainly comes from the correct translation of domain-specific and OOV words, which may be segmented improperly by subword algorithms. + +
WordCharBPEMorf.BPE-D
No adapting11.0312.469.029.7411.11
Finetune30.2640.5339.5338.4940.26
+ +Table 3: Average OOD BLEU of models based on different subword algorithms when adapting from Law to other domains. BPE-D: BPE-dropout (Provilkov et al., 2020) + +# 4.4 Comparison with Advanced Segmentation Algorithms + +Although we focus on deterministic segmentation algorithms in this paper, there are more advanced ones such as BPE-dropout (Provilkov et al., 2020) and subword regularization (Kudo, 2018), which produce multiple segmentation candidates when training and show improved performance. Therefore, we also conduct experiments comparing CHAR with BPE-dropout in terms of domain adaptation performance. We take the setting of adapting from Law to other domains and report results in Table 3. As can be seen, although BPE-dropout surpasses BPE by a large margin, CHAR still achieves the best performance, which again shows the superiority of CHAR. + +# 5 Related Work + +Character-level neural machine translation has received growing attention in recent years. Lee et al. (2017) first propose a fully character-level NMT model based on recurrent encoder-decoder architecture and convolutional layers, which shows a promising results. Gao et al. (2020) propose to incorporate convolution layers in the more advanced Transformer architecture and show their model can learn more robust character-level alignments. + +However, translating at character level may incur significant computational overhead. Therefore, later works on character-level NMT (Cherry et al., 2018; Banar et al., 2020) mainly focus on reducing computation cost of them. Cherry et al. (2018) show that by employing source sequence compression techniques, the quality and efficiency of character-based models can be properly balanced. Banar et al. (2020) share the same idea as Cherry et al. (2018) but build their models using Transformer architecture. Our work differs from theirs in that we aim to analyze the performance of existing models instead of exploring novel architectures. + +There are also several researches on comparison between CHAR and other subword algorithms + +(Durrani et al., 2019; Gupta et al., 2019). Durrani et al. (2019) compare character-based models and subword-based models in terms of representation quality, and find that representation learned by the former are more suitable for modeling morphology, and more robust to noisy input. Gupta et al. (2019) investigate the performance of different segmentation algorithms when using Transformer architecture, and find that character-based models can achieve better performance when translating noisy text or text from a different domain. Our finds are consistent with them, yet we conduct a more large-scale and in-depth analysis by covering language pairs from more language families and explaining where the advantage of character-based models comes from. + +# 6 Conclusion + +We conduct a comprehensive study and show advantages of CHAR over subword algorithms in three settings: translating to typologically diverse languages, translating with low resource, and adapting to distant domains. Note that although we have tried our best to take as much language pairs as possible into consideration, there are certainly a lot of languages remaining uncovered in this paper. However, we believe our experimental results can serve as an evidence of character-based NMT models' strong competitiveness. We hope more attention will be drawn to them, including exploring their more benefits and reducing the possibly higher computation cost in practice. + +# Acknowledgments + +We would like to thank the anonymous reviewers for their insightful comments. Shujian Huang is the corresponding author. This work is supported by the National Key R&D Program of China (No. 2019QY1806), National Science Foundation of China (No. 61772261, U1836221). This work is also partially supported by the research funding from ZTE Corporation. + +# References + +Nikolay Banar, Walter Daelemans, and Mike Kestemont. 2020. Character-level transformer-based neural machine translation. arXiv preprint arXiv:2005.11239. +Franck Burlot, Yves Scherrer, Vinit Ravishankar, Ondrej Bojar, Stig-Arne Gronroos, Maarit Koponen, Tommi Nieminen, and François Yvon. 2018. The wmt'18 morpheval test suites for english-czech, english-german, english-finnish and turkish-english. + +In 3rd Conference on Machine Translation (WMT 18), volume 2, pages 550-564. +Colin Cherry, George Foster, Ankur Bapna, Orhan First, and Wolfgang Macherey. 2018. Revisiting character-based neural machine translation with capacity and compression. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4295-4305, Brussels, Belgium. Association for Computational Linguistics. +Mathias Creutz and KristaLAGus.2002.Unsupervised discovery of morphemes.In Proceedings of the ACL- 02 Workshop on Morphological and Phonological Learning, pages 21-30.Association for Computational Linguistics. +Nadir Durrani, Fahim Dalvi, Hassan Sajjad, Yonatan Belinkov, and Preslav Nakov. 2019. One size does not fit all: Comparing NMT representations of different granularities. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 1504-1516, Minneapolis, Minnesota. Association for Computational Linguistics. +Yingqiang Gao, Nikola I. Nikolov, Yuhuang Hu, and Richard H.R. Hahnloser. 2020. Character-level translation with self-attention. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1591–1604, Online. Association for Computational Linguistics. +Rohit Gupta, Laurent Besacier, Marc Dymetman, and Matthias Galle. 2019. Character-based NMT with transformer. CoRR, abs/1911.04997. +Philipp Koehn and Rebecca Knowles. 2017. Six challenges for neural machine translation. In Proceedings of the First Workshop on Neural Machine Translation, pages 28-39, Vancouver. Association for Computational Linguistics. +Taku Kudo. 2018. Subword regularization: Improving neural network translation models with multiple subword candidates. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 66-75, Melbourne, Australia. Association for Computational Linguistics. +Jason Lee, Kyunghyun Cho, and Thomas Hofmann. 2017. Fully character-level neural machine translation without explicit segmentation. Transactions of the Association for Computational Linguistics, 5:365-378. +Jindrich Libovicky and Alexander Fraser. 2020. Towards reasonably-sized character-level transformer NMT by finetuning subword systems. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 2572-2579, Online. Association for Computational Linguistics. + +John J McCarthy. 1981. A prosodic theory of nonconcatenative morphology. Linguistic inquiry, 12(3):373-418. +Mathias Müller, Annette Rios, and Rico Senrich. 2020. Domain robustness in neural machine translation. In Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track), pages 151-164, Virtual. Association for Machine Translation in the Americas. +Toan Q. Nguyen and David Chiang. 2017. Transfer learning across low-resource, related languages for neural machine translation. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 296-301, Taipei, Taiwan. Asian Federation of Natural Language Processing. +Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pages 311-318. +Maja Popovic. 2015. *chrF: character n-gram F-score for automatic MT evaluation*. In *Proceedings of the Tenth Workshop on Statistical Machine Translation*, pages 392–395, Lisbon, Portugal. Association for Computational Linguistics. +Ivan Provilkov, Dmitrii Emelianenko, and Elena Voita. 2020. BPE-dropout: Simple and effective subword regularization. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1882-1892, Online. Association for Computational Linguistics. +Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016. Neural machine translation of rare words with subword units. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1715-1725, Berlin, Germany. Association for Computational Linguistics. +Rico Sennrich and Biao Zhang. 2019. Revisiting low-resource neural machine translation: A case study. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 211-221, Florence, Italy. Association for Computational Linguistics. +Jörg Tiedemann. 2012. Parallel data, tools and interfaces in opus. In Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12), pages 2214-2218. +Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Proceedings of the 31st International Conference on Neural Information Processing Systems, pages 6000-6010. + +Yi Zhu, Ivan Vulic, and Anna Korhonen. 2019. A systematic study of leveraging subword information for learning word representations. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 912-932, Minneapolis, Minnesota. Association for Computational Linguistics. + +# A Hyperparameters + +We conduct a grid search of hyperparameters for the training of Transformer models, including batch size (tokens per batch) and learning rate. For batch size, the searching range is \{4096, 8192, 16384, 32768\}. For learning rate, the searching range is $\{5e - 5, 1e - 4, 5e - 4, 1e - 3\}$ . + +Besides, we also experiment with different model size and number of bpe merges $k$ in the low resource settings(50k, 100k, 200k). The searching range of $k$ is $\{2000, 10000\}$ . We consider four kinds of model size, i.e. tiny, mini, small and base, which differ in their hidden size and transformer layers. The details can be found in Table 4. + +
hidden sizelayer
tiny1282
mini2564
small5124
base5126
+ +Table 4: Detailed hyperparameters for different model sizes. \ No newline at end of file diff --git a/whenischarbetterthansubwordasystematicstudyofsegmentationalgorithmsforneuralmachinetranslation/images.zip b/whenischarbetterthansubwordasystematicstudyofsegmentationalgorithmsforneuralmachinetranslation/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..24090c63c7032df5c8cf52251ae811772e40ecf6 --- /dev/null +++ b/whenischarbetterthansubwordasystematicstudyofsegmentationalgorithmsforneuralmachinetranslation/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:567c4b91945cf0382df82c41bb21a2e4642ed337c18cb8b07092288ff891e4b8 +size 246318 diff --git a/whenischarbetterthansubwordasystematicstudyofsegmentationalgorithmsforneuralmachinetranslation/layout.json b/whenischarbetterthansubwordasystematicstudyofsegmentationalgorithmsforneuralmachinetranslation/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..1c6bfaf14b3e28034c806da743bba3c653a67361 --- /dev/null +++ b/whenischarbetterthansubwordasystematicstudyofsegmentationalgorithmsforneuralmachinetranslation/layout.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4b969fbbf165f28fc1871d6eee2ebdaae9bdd926ac0245618e066534dcf40ede +size 193656 diff --git a/wikisumcoherentsummarizationdatasetforefficienthumanevaluation/71eb3d25-471e-4bdd-b188-ae4cb111325d_content_list.json b/wikisumcoherentsummarizationdatasetforefficienthumanevaluation/71eb3d25-471e-4bdd-b188-ae4cb111325d_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..f9114dd986da2e08a6fd17833d0f141b0a0d016b --- /dev/null +++ b/wikisumcoherentsummarizationdatasetforefficienthumanevaluation/71eb3d25-471e-4bdd-b188-ae4cb111325d_content_list.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c3d479e9ba829da57bf077148ac7de17b8ded8d4b43a36a04dd6233daddd1119 +size 58843 diff --git a/wikisumcoherentsummarizationdatasetforefficienthumanevaluation/71eb3d25-471e-4bdd-b188-ae4cb111325d_model.json b/wikisumcoherentsummarizationdatasetforefficienthumanevaluation/71eb3d25-471e-4bdd-b188-ae4cb111325d_model.json new file mode 100644 index 0000000000000000000000000000000000000000..a022096ef3ab041ea262d341a837d2bfd1ded4d0 --- /dev/null +++ b/wikisumcoherentsummarizationdatasetforefficienthumanevaluation/71eb3d25-471e-4bdd-b188-ae4cb111325d_model.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3a0ba8e82349cedb3e8b1bc006a9689ae28439e2cfbba704adbe0d15e7ac0ddd +size 72071 diff --git a/wikisumcoherentsummarizationdatasetforefficienthumanevaluation/71eb3d25-471e-4bdd-b188-ae4cb111325d_origin.pdf b/wikisumcoherentsummarizationdatasetforefficienthumanevaluation/71eb3d25-471e-4bdd-b188-ae4cb111325d_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..53b2a5c05374294d286d4ded28d9bc219a0057d9 --- /dev/null +++ b/wikisumcoherentsummarizationdatasetforefficienthumanevaluation/71eb3d25-471e-4bdd-b188-ae4cb111325d_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8de3d6abcf5debe88ebb7c129b49649bec5c653c50aca00b67e22612e721962d +size 318633 diff --git a/wikisumcoherentsummarizationdatasetforefficienthumanevaluation/full.md b/wikisumcoherentsummarizationdatasetforefficienthumanevaluation/full.md new file mode 100644 index 0000000000000000000000000000000000000000..03ff747003b4c3f9d5a86fd1895dc164ad88e9c2 --- /dev/null +++ b/wikisumcoherentsummarizationdatasetforefficienthumanevaluation/full.md @@ -0,0 +1,243 @@ +# WikiSum: Coherent Summarization Dataset for Efficient Human-Evaluation + +Nachshon Cohen* + +Amazon + +nachshonc@gmail.com + +Oren Kalinsky* + +Amazon + +orenk@amazon.com + +Yftah Ziser* + +Facebook + +yftahz@fb.com + +# Alessandro Moschitti + +Amazon + +amosch@amazon.com + +# Abstract + +Recent works have made significant advances on summarization tasks, facilitated by summarization datasets. Several existing datasets have the form of coherent-paragraph summaries. However, these datasets were curated from academic documents written for experts, making the essential step of assessing the summarization output through human-evaluation very demanding. + +To overcome these limitations, we present a dataset1 based on article summaries appearing on the WikiHow website, composed of how-to articles and coherent-paragraph summaries written in plain language. We compare our dataset attributes to existing ones, including readability and world-knowledge, showing our dataset makes human evaluation significantly more manageable and effective. A human evaluation conducted on PubMed and the proposed dataset reinforces our findings. + +# 1 Introduction + +Summarization is the task of preserving the key information in a text while reducing its length. Recently, many summarization datasets were published and helped push the boundaries of new summarization systems. These datasets differ on several properties, including the domain (e.g., academic or news) and the summary form. PubMed, arXiv, and BigPatent (Cohan et al., 2018; Sharma et al., 2019) provide a summary in the form of coherent paragraphs (i.e., each sentence flows smoothly into the next). In contrast, other summarization datasets (Hermann et al., 2015; Grusky et al., 2018; Koupaee and Wang, 2018; Ladhak et al., 2020) offer a summary in the form of a key points list (i.e., highlights). In this paper, we focus on coherent paragraph summarization datasets. + +How to Bake Chicken Breast? To bake chicken breast, start by lining a baking dish with foil or parchment paper. Then, put the chicken in the baking dish and bake it for 30-40 minutes at 400 degrees Fahrenheit, or until it reaches an internal temperature of 160 degrees Fahrenheit. + +How to Break Up with Your Friend? The best way to break up with a friend is to confront them. Choose a time and place to meet up and explain to them why you are ending the friendship. Allow your friend to speak their mind as well, and work together to set boundaries for moving forward. + +Figure 1: Examples of how-to questions and their corresponding answer's summarization in WikiSum. + +Automatic evaluation of summarization systems, e.g., by using the ROUGE metric, is challenging (Lloret et al., 2018) and is often inconsistent with human evaluation (Liu and Liu, 2008; Cohan and Goharian, 2016; Tay et al., 2019; Huang et al., 2020). To understand – and later improve – the quality of summarization systems, it is necessary to conduct a human evaluation. A human evaluation's quality depends on the ease of reading and understanding of the measured text: a simple text does not require annotators with unique expertise, can be evaluated faster, and is easier to annotate correctly. However, existing coherent-paragraph summarization datasets consist of academic papers and cannot be considered easy to read. Evaluating such summarization samples requires unique expertise, takes time, and comes at a high cost. + +In this work, we present WikiSum, a new summarization dataset from the WikiHow knowledge base2. The WikiSum documents are written in simple English, and the summaries provide "non-obvious tips that mimic the advice a knowledgeable, empathetic friend might give."3 Unlike previous WikiHow summarization (Koupaee and Wang, 2018; Ladhak et al., 2020) datasets and summaries + +![](images/fe8d000eb4afaed80560ea36c6715640ba90a4f46491da46efa77b10fe1762bd.jpg) +Figure 2: Category distribution in WikiSum. + +from the news domain, the summaries of WikiSum are in the form of a coherent paragraph written by the document authors (examples in Figure 1). Moreover, in contrast to other coherent-paragraph summarization datasets from the academic domain, WikiSum is written using simple English. This critical property can help with the challenging task of evaluating summarization systems and provide insights that can go unnoticed using automatic evaluation methods. + +The key attributes of WikiSum are: (1) Summaries written as a single, coherent passage. (2) Articles and summaries that are easy to read. (3) Articles and summaries require less world knowledge to understand. We evaluate the dataset readability and estimate the required world-knowledge in Section 3. Moreover, we reinforce our results by conducting a human-evaluation of a summarization dataset in Section 4. Finally, to establish a baseline on the proposed dataset, we benchmark WikiSum using recent summarization systems and report their performance on Section 5. + +# 2 Related Work + +The summarization landscape can be roughly divided into three primary summary-forms: (1) Single sentence (Napoles et al., 2012; Grusky et al., 2018; Narayan et al., 2018; Kim et al., 2019) - summarize the document in a single sentence; (2) Highlights (Hermann et al., 2015; Koupaee and Wang, 2018; Ladhak et al., 2020) - a summary in the form of bullets listing the key points in the text; (3) Coherent summary (Sharma et al., 2019; Cohan et al., 2018) - short coherent paragraphs describing the salient information. The summarization datasets from the news domain, which are commonly used for human evaluation, include summaries in the form of highlights or single-sentence summaries. However, summarization datasets written in a co + +herent format come from the academic domain, making them extremely difficult to annotate manually. Our proposed WikiSum is the only dataset written in a coherent format, yet easy for human evaluation. We do not claim that coherent paragraph summaries are better, but rather different; each format has its use cases, and human evaluation should be done on each of the different formats separately. + +The existing WikiHow datasets (Koupae and Wang, 2018; Ladhak et al., 2020) can be considered the closest to WikiSum, as they originate from the same knowledge base. However, while the existing WikiHow datasets split the article to generate the document and summary, WikiSum uses the entire article as the document and a summary specifically written by the article's author (called the Article Quick Summary). The former uses the concatenation of the first line of each step, called the step header, as the list of highlights and the remainder of step text's concatenation called "wrap-text," as the document4. In addition to the different summary-form of the highlight-based WikiHow and WikiSum, the content of the summaries is significantly different, which can be illustrated by the low BLEU-4 $(0.06^{5})$ between the two. + +BigPatent (Sharma et al., 2019), Arxiv and PubMed (Cohan et al., 2018) are recent summarization datasets with coherent paragraph summaries. These datasets focus on the academic domain and are written for experts. Like these datasets, WikiSum is composed of long documents and coherent paragraph summaries. Nonetheless, it uses common everyday language and ranges over many domains (see Figure 2). Finally, Table 1 compares WikiSum to common existing datasets. Additional details on WikiSum are available in the appendix. + +# 3 Measuring Text Difficulty + +This section focuses on two crucial attributes: ease of readability and external knowledge required, shown (in Section 4) to be important for easy and effective human evaluation. For brevity, we focus on summarization datasets with coherent-paragraph summaries. + +
Domain#DocsComp. ratioSummaryDoc
# word# sent# word
WIKISUMinstructional39,77513.9101.25.01,334.2
ARXIVacademic215,91339.8292.89.66,913.8
PUBMEDacademic133,21516.2214.46.93,224.4
BIGPATENTacademic1,341,36236.4116.53.53,572.8
WIKIHOWinstructional215,36514.569.07.2500.8
CNN/DMnews312,08513.055.63.8789.9
NYTnews654,78812.044.92.0795.9
NEWSROOMnews1,212,72643.030.41.4750.9
XSUMnews226,71118.823.31.0431.1
+ +# 3.1 Readability + +Readability metrics attempt to indicate how difficult a passage in English is to read. We used classical readability measures, including FKGL (Farr et al., 1951), GFI (Robert, 1968), SMOG (Mc Laughlin, 1969), ARI (Senter and Smith, 1967), CLI (Coleman and Liau, 1975). All these metrics are based on lexical features of the text, e.g., number of words in a sentence or mean number of syllables per word. They produce a score that is interpreted as the number of years of formal education required (for a native English speaker) to understand a piece of text6. + +For each document, we measured readability scores for the document and the ground truth summary. The document is longer than the summary, so its readability is of higher importance. We report the average readability score for all the samples in the dataset. + +Readability scores for the documents are presented at the top of Table 2. The table shows that WikiSum is significantly easier to read than other documents from coherent-summary datasets (arXiv, PubMed, BigPatent). Similar results can be found for the readability scores for the summaries (bottom of Table 2). To conclude, WikiSum is measured as drastically simpler to read than other coherent-summary datasets. + +# 3.2 External Knowledge + +Existing datasets are composed of academic documents that are written for experts. Often, to fully understand academic texts requires domain knowledge, which makes the annotator pool smaller, and + +Table 1: Statistics comparison of summarization datasets. Datasets not in coherent-paragraph form are marked in gray. + +
DatasetARIFKGLGFISMOGCLI
DocumentWikiSum7.46.8210.159.718.83
arXiv14.0213.5118.4715.4414.31
PubMed16.7416.2720.6417.0315.01
BigPatent13.4613.3217.4714.6811.68
SummaryWikiSum9.718.4911.9110.248.78
arXiv16.4416.120.516.815.23
PubMed17.7317.3521.617.4416.6
BigPatent22.4720.9125.1218.7514.0
+ +Table 2: Readability scores for the documents (top) and summaries (bottom), measured in years of formal education required to read the text. Smaller is simpler. + +![](images/07eda2aa1c2a5d02d4b65f0724350dc61d15824678188178011a9cc46cfa9cbf.jpg) +Figure 3: Ratio of uncommon words in the document, which cannot be found in the Top-K OpenSubtitles words, for different $k$ values. + +thus, in most cases, more expensive. Word frequency is a strong indicator of how familiar a word is (Paetzold and Specia, 2016), where rare words tend to be less familiar. + +We used OpenSubtitles (Lison and Tiedemann, 2016), text corpora compiled from an extensive database of movie and TV subtitles to obtain word frequencies. We hypothesize that movie and TV subtitles can roughly represent common knowledge among many people. In Figure 3, we show the percentage of non-frequent words in a document (i.e., words that cannot be found in the top-k words in OpenSubtitles) as a function $K$ , averaged over a random sample of 10,000 documents from each dataset. This figure clearly shows that WikiSum is composed of significantly fewer words unpopular in TV shows and movies, requiring less specialized external knowledge. + +# 4 Human Evaluation + +We conducted a standard human evaluation on a summarization task, in addition to the automatic readability and the external knowledge metrics. We gathered a pool of 6 annotators, without any prior knowledge of the project, all with a graduate degree (M.sc. or Ph.D.) and proficient English reading-level. We asked them to evaluate summaries generated by Pegasus (Zhang et al., 2020). The an + +
datasettime (minutes)difficulty (rating)exhausting (rating)qualified (rating)unknown (%)
WikiSum6.8±1.21.9±0.32.2±0.54.2±0.30.2±0.1
PubMed10.0±1.23.7±0.33.9±0.42.2±0.43.7±1.4
+ +notation task followed Huang et al. (2020) and consisted of relevance, consistency, fluency, and coherency. + +Due to resource limitations (and the difficulty of annotating articles from the academic domain), we had to pick one coherent-paragraph dataset for comparison with WikiSum. To avoid annotators' domain bias, we selected articles from PubMed, which contains articles not in the area of expertise of any annotator, in addition to WikiSum. We sampled random articles with 950 - 1050 words to avoid length bias, ensuring that article length is similar in both datasets. All annotators allocated 1 hour, which amounted to 42 annotations, 21 for each dataset. + +During the annotation task, we measured the evaluation time and asked the annotators to mark unfamiliar words. In addition, we asked the annotators to rate the following aspects on a 1-5 scale: (a) How difficult was the task? (b) How tiring was it? (c) How qualified are you for this task? After each pair of PubMed and WikiHow samples were completed, the annotators selected which dataset they prefer to evaluate. + +In Table 3 we show the annotators' assessment of the tasks. Compared to PubMed, a WikiSum annotation takes significantly less time, is less difficult, and less tiring. Moreover, the annotators revealed that they were much more qualified to assess the WikiSum task summary. Finally, in $90\%$ of the cases (19 out of 21), the annotators revealed that they preferred a WikiSum annotation task. This reinforces our findings that WikiSum is significantly easier to annotate than PubMed. + +In the annotation task, we also asked the annotators to mark unfamiliar words in the article. We found a strong correlation between the count of unfamiliar words and the task difficulty, evaluation time, and perceived required qualification (Pearson correlation of 0.57, 0.36, $-0.48^8$ , respectively, + +Table 3: Evaluation time per sample, evaluation difficulty/exhaustion rating, perceived qualification, and the ratio of unknown words in the document. $\pm$ denotes $95\%$ confidence interval according to student's t distribution (df=20). Difficulty, qualification, and tiring were marked on a 1-5 scale. + +
ModelsLEAD-3TextRankPEGASUSLARGE
WIKISUM25.3/6.84/16.232.7/8.8/18.943.35/15.48/26.91
ARXIV25.53/5.98/15.2233.1/9.7/18.143.07/19.70/34.79
PUBMED26.38/8.73/16.635.3/13.1/20.444.70/17.27/25.80
BIGPATENT28.9/7.96/18.1733.0/9.8/19.645.49/19.90/27.69
+ +Table 4: ROUGE-1/2/L F1 scores on coherent-summary datasets. Pegasus baseline results are from (Zhang et al., 2020), except for WikiSum. + +$p < 0.05$ ). Strong correlation was also found between the ARI readability metric (Section 3.1) and the above-mentioned annotation metrics (Pearson correlation of 0.69, 0.49, -0.76, $p < 0.05$ ). This demonstrates the effect of readability on the difficulty of an annotation task. + +Finally, we found that unfamiliar words correspond to low-frequency OpenSubtitles words (Section 3.2). The unfamiliar words on WikiSum and PubMed appear in the top 91,550 and 230,596 words on average, respectively, while familiar words appear in the top 16,935 and 59,244 words on average, respectively. It also further validates Paetzold and Specia (2016) hypothesis about the strong correlation between word frequency and complexity. + +# 5 Model Results and Discussion + +To provide both abstractive and extractive baselines for WikiSum, we evaluate on PEGASUSLarge (Zhang et al., 2020), TextRank (Mihalcea and Tarau, 2004), and the common LEAD-3 that selects the first three sentences of the document as the summary. We compare the results on WikiSum to the Arxiv, PubMed, and BigPatent Datasets results. Table 4 reports the F1 scores of ROUGE-1, 2 and L for all the models. The results show that the models' performance on WikiSum is not drastically different from the other datasets, making it an interesting dataset for benchmarking summarization systems. The detailed evaluation setup can be found in the supplementary materials. + +To conclude, this paper presents the WikiSum dataset, which is drastically simpler for human evaluation than existing summarization datasets where the summary appears as a coherent paragraph. We showed WikiSum's simplicity via various readability metrics and demonstrated that the text requires less external knowledge to be understood. Finally, we validated our finding via a human evaluation task on WikiSum and PubMed. + +# References + +Carl Hugo Björnsson. 1968. Läsbarhet, Stockholm: Liber. +Arman Cohan, Franck Dernoncourt, Doo Soon Kim, Trung Bui, Seokhwan Kim, Walter Chang, and Nazli Goharian. 2018. A discourse-aware attention model for abstractive summarization of long documents. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT, New Orleans, Louisiana, USA, June 1-6, 2018, Volume 2 (Short Papers), pages 615-621. Association for Computational Linguistics. +Arman Cohan and Nazli Goharian. 2016. Revisiting summarization evaluation for scientific articles. CoRR, abs/1604.00400. +Meri Coleman and Ta Lin Liau. 1975. A computer readability formula designed for machine scoring. Journal of Applied Psychology, 60(2):283. +James N Farr, James J Jenkins, and Donald G Paterson. 1951. Simplification of flesch reading ease formula. Journal of applied psychology, 35(5):333. +Rudolph Flesch. 1948. A new readability yardstick. Journal of applied psychology, 32(3):221. +Max Grusky, Mor Naaman, and Yoav Artzi. 2018. Newsroom: A dataset of 1.3 million summaries with diverse extractive strategies. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2018, New Orleans, Louisiana, USA, June 1-6, 2018, Volume 1 (Long Papers), pages 708-719. Association for Computational Linguistics. +Karl Moritz Hermann, Tomás Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015. Teaching machines to read and comprehend. In Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, December 7-12, 2015, Montreal, Quebec, Canada, pages 1693-1701. +Dandan Huang, Leyang Cui, Sen Yang, Guangsheng Bao, Kun Wang, Jun Xie, and Yue Zhang. 2020. What have we achieved on text summarization? In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 446-469, Online. Association for Computational Linguistics. +Byeongchang Kim, Hyunwoo Kim, and Gunhee Kim. 2019. Abstractive summarization of reddit posts with multi-level memory networks. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, Volume 1 (Long and Short Papers), pages 2519-2531. Association for Computational Linguistics. + +Mahnaz Koupaee and William Yang Wang. 2018. Wikihow: A large scale text summarization dataset. CoRR, abs/1810.09305. +Faisal Ladhak, Esin Durmus, Claire Cardie, and Kathleen R. McKeown. 2020. Wikilingua: A new benchmark dataset for multilingual abstractive summarization. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings, EMNLP 2020, Online Event, 16-20 November 2020, pages 4034-4048. Association for Computational Linguistics. +Pierre Lison and Jörg Tiedemann. 2016. Opensubtitles2016: Extracting large parallel corpora from movie and tv subtitles. +Feifan Liu and Yang Liu. 2008. Correlation between ROUGE and human evaluation of extractive meeting summaries. In Proceedings of ACL-08: HLT, Short Papers, pages 201-204, Columbus, Ohio. Association for Computational Linguistics. +Elena Lloret, Laura Plaza, and Ahmet Aker. 2018. The challenging task of summary evaluation: an overview. Language Resources and Evaluation, 52(1):101-148. +G Harry Mc Laughlin. 1969. Smog grading-a new readability formula. Journal of reading, 12(8):639-646. +Rada Mihalcea and Paul Tarau. 2004. Textrank: Bringing order into text. In Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing, EMNLP 2004, A meeting of SIGDAT, a Special Interest Group of the ACL, held in conjunction with ACL 2004, 25-26 July 2004, Barcelona, Spain, pages 404-411. ACL. +Courtney Naples, Matthew R. Gormley, and Benjamin Van Durme. 2012. Annotated gigaword. In Proceedings of the Joint Workshop on Automatic Knowledge Base Construction and Web-scale Knowledge Extraction, AKBC-WEKEX@NAACL-HLT 2012, Montréal, Canada, June 7-8, 2012, pages 95-100. Association for Computational Linguistics. +Shashi Narayan, Shay B. Cohen, and Mirella Lapata. 2018. Don't give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31 - November 4, 2018, pages 1797-1807. Association for Computational Linguistics. +Gustavo Paetzold and Lucia Specia. 2016. Semeval 2016 task 11: Complex word identification. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), pages 560-569. +Gunning Robert. 1968. The technique of clear writing. Revised Edition). New York: McGraw Hill. +RJ Senter and Edgar A Smith. 1967. Automated readability index. Technical report, CINCINNATI UNIV OH. + +Eva Sharma, Chen Li, and Lu Wang. 2019. BIGPATENT: A large-scale dataset for abstractive and coherent summarization. In Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Florence, Italy, July 28-August 2, 2019, Volume 1: Long Papers, pages 2204-2213. Association for Computational Linguistics. + +Wenyi Tay, Aditya Joshi, Xiuzhen Jenny Zhang, SarvNaz Karimi, and Stephen Wan. 2019. Red-faced rouge: Examining the suitability of rouge for opinion summary evaluation. In Proceedings of the The 17th Annual Workshop of the Australasian Language Technology Association, pages 52-60. + +wikihow.com. 2020. WikiHow Article Guide + +lines. https://www.wikihow.com/ + +Write-a-New-Article-on-wikiHow. + +Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Peter J. Liu. 2020. PEGASUS: pre-training with extracted gap-sentences for abstractive summarization. In Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event, volume 119 of Proceedings of Machine Learning Research, pages 11328-11339. PMLR. + +# A Data Description + +# A.1 Gathering the data + +We use Scrapy scraper9 to download articles and summaries from the wikihow.com website. We removed HTML tags using BeautifulSoup10. Finally, we removed any sample in which the summary is a list of bullet points; around 7k samples were excluded in this manner. + +# A.2 Authors Instructions for Writing Quick Summaries + +The wikihow.com website provides the following guidelines for authors writing a quick summary.[11] + +The goal of the "Quick Summary" section on wikiHow is to provide a short summary of non-obvious tips that mimic the advice a knowledgeable, empathetic friend might give you if you asked them for help on the given topic. Among other uses, Quick Summaries help smart devices like Google Homes and Amazon Echos deliver wikiHow advice to listeners in need of how-to guidance. + +We remark that the quick summaries are indeed used by commercial voice assistants to answer how-to questions. As voice assistants gain popularity, so does the importance of such coherent-paragraph summaries. + +# A.3 Data Layout + +Raw data is available in the supplementary material, in a json format. Each line consists of a single sample, with the following fields + +1. Link to the original article +2. Article title +3. Article text +4. Quick summary +5. Split fold (train, dev, or test) + +Finally, it also includes stepheaders: the first line in each step. This is part of the article but might be considered more important, and therefore, it might find further uses by system designers. + +# A.4 Dataset Statistics + +Most dataset statistics appear in Table 1 in the article's main body and are repeated here for completeness. The total number of samples in the WikiSum dataset is 39,775. On average, each summary consists of 101.2 words, while each article consists of 1,334.2 words. The average compression ratio is 13.9. + +# A.5 Evaluation details + +We randomly split WikiSum into 35,775 (document, summary) training pairs, as well as 2,000 validation pairs and 2,000 test pairs. The rest of the datasets were downloaded from the HuggingFace dataset repository $^{12}$ . + +All the datasets were evaluated using TextRank $^{13}$ and Pegasus-large. The ROUGE scores throughout the paper were calculated using rouge-score $^{14}$ . We utilized TextRank to generate three summary sentences. The Pegasus results on Arxiv, Pubmed, and Arxiv were taken from the Pegasus paper. The results on WikiSum were computed by using the Github repository of the Pegasus paper $^{15}$ . Pegasus was trained on a single NVIDIA V100 Tensor Core + +GPU, using max input and output sequence lengths of 1024 and 256, respectively. + +# B Example Summaries + +In this appendix, we provide an example summary from WikiSum and arXiv, PubMed, and bigPatent. Note that the article can be quite long (for arXiv and PubMed, it is a full academic paper), so it is not presented in this appendix. Instead, we provide a link to the online version of the full article. + +# B.1 WikiSum + +The WikiSum example summary is provided below: + +"To ace a test, even if you're not prepared, start by glancing over the test before you get started to get an idea of how long it is so you can manage your time better. Then, read through each question twice and try to answer it. If you can't answer a question, skip it and come back to it later if you can, which will save you from wasting all of your time on one question. If your test is multiple choice and you don't know the answer, eliminate two answers, so you're left with just two options. Then, guess if necessary since you'll have a 50-percent chance of being right." + +The article is available at https://www.wikiphow.com/Ace-a-Test. + +# B.2 WikiHow + +For the sake of comparison between WikiHow and WikiSum datasets, we provide the WikiHow summary originating from the same raw material (i.e., the same wikihow.com how-to article) as the WikiSum example at Appendix B.1. We remark that the article to be summarized is not exactly the same, as the WikiHow example does not contain the step headers from the article's text. The WikiHow summary is provided below. + +"Study well before the test. Get a study friend. Take breaks. Relax. Pay attention in class. Do all available practice questions. Get some sleep the night before. Have proper meals before the test day. Have your test-taking materials assembled and ready. Listen to music you like. Go into the test in a positive manner. Take deep breaths to try to keep calm. Read the questions carefully. Do the easy questions first. Go with your first answer. Use logic if you're stuck on a multiple choice question. Review your answers thoroughly when you are done." + +It can easily be seen that the WikiSum summary is a coherent, fluent paragraph, while the WikiHow summary is a set of bullet points. The content of the two summaries are also quite different between the two datasets. + +# B.3 arXiv + +"the effect of a random phase diffuser on fluctuations of laser light ( scintillations ) is studied. not only spatial but also temporal phase variations introduced by the phase diffuser are analyzed. the explicit dependence of the scintillation index on finite - time phase variations is obtained for long propagation paths. it is shown that for large amplitudes of phase fluctuations , a finite - time effect decreases the ability of phase diffuser to suppress the scintillations." + +The article is available at https://arxiv.org/pdf/0903.5449.pdf. + +# B.4 PubMed + +"tardive dystonia ( td ) is a serious side effect of antipsychotic medications, more with typical antipsychotics, that is potentially irreversible in affected patients. studies show that newer atypical antipsychotics have a lower risk of td. as a result, many clinicians may have developed a false sense of security when prescribing these medications. we report a case of 20-year - old male with hyperthymic temperament and borderline intellectual functioning, who developed severe td after low dose short duration exposure to atypical antipsychotic risperidone and then olanzapine. the goal of this paper is to alert the reader to be judicious and cautious before using casual low dose second generation antipsychotics in patient with no core psychotic features, hyperthymic temperament, or borderline intellectual functioning suggestive of organic brain damage, who are more prone to develop adverse effects such as td and monitor the onset of td in patients taking atypical antipsychotics." + +The article is available at + +https://www.ncbi.nlm.nih.gov/PMC/articles/PMC5330001/. + +# B.5 BigPatent + +"this invention relates to novel calcium phosphate - coated implantable medical devices and processes of making same. the calcium - phosphate coatings are designed to minimize the immune response to the implant and can be used to store and release a medicinally active agent in a controlled manner. + +such coatings can be applied to any implantable medical devices and are useful for a number of medical procedures including balloon angioplasty in cardiovascular stenting, ureteral stenting and catheterisation. the calcium phosphate coatings can be applied to a substrate as one or more coatings by a sol - gel deposition process, an aerosol - gel deposition process, a biomimetic deposition process, a calcium phosphate cement deposition process, an electro - phoretic deposition process or an electrochemical deposition process. the coating can contain and elude a drug in an engineered manner." + +The article is available at https://patentscope.wipo.int/search/en/detail.jsf?docId=WO2007147234. \ No newline at end of file diff --git a/wikisumcoherentsummarizationdatasetforefficienthumanevaluation/images.zip b/wikisumcoherentsummarizationdatasetforefficienthumanevaluation/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..1895e5617fff49cd516963b5274318d949137e70 --- /dev/null +++ b/wikisumcoherentsummarizationdatasetforefficienthumanevaluation/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c53dcab379bcb5e286180901a739f01d5da4bec288cd6efc4ef4f7f35564a514 +size 184900 diff --git a/wikisumcoherentsummarizationdatasetforefficienthumanevaluation/layout.json b/wikisumcoherentsummarizationdatasetforefficienthumanevaluation/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..69c96fb4db9919c947ab3b05f57367fc82b2c14a --- /dev/null +++ b/wikisumcoherentsummarizationdatasetforefficienthumanevaluation/layout.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b00a6d1d2f262513c0c69a92adbca25cba4ef2c750e9a3591a29d0536d31dc26 +size 246521 diff --git a/xfactanewbenchmarkdatasetformultilingualfactchecking/594ea4db-e852-4852-bf86-19ed29c9b6b7_content_list.json b/xfactanewbenchmarkdatasetformultilingualfactchecking/594ea4db-e852-4852-bf86-19ed29c9b6b7_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..58bae70255f07f1ccc0a57c189a6fd8687226159 --- /dev/null +++ b/xfactanewbenchmarkdatasetformultilingualfactchecking/594ea4db-e852-4852-bf86-19ed29c9b6b7_content_list.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1aa90d53dc0f8e4d8fb6f49b9cdadd1bdb9195f96b38b3d11f1333a405a379ee +size 53541 diff --git a/xfactanewbenchmarkdatasetformultilingualfactchecking/594ea4db-e852-4852-bf86-19ed29c9b6b7_model.json b/xfactanewbenchmarkdatasetformultilingualfactchecking/594ea4db-e852-4852-bf86-19ed29c9b6b7_model.json new file mode 100644 index 0000000000000000000000000000000000000000..d493a97a977f799e5ae3d46b4d18513f36df9782 --- /dev/null +++ b/xfactanewbenchmarkdatasetformultilingualfactchecking/594ea4db-e852-4852-bf86-19ed29c9b6b7_model.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:542a179299a6f4dd20789da0cfe9a51f25c67e142ebe968e2a94c83518d34ce1 +size 64134 diff --git a/xfactanewbenchmarkdatasetformultilingualfactchecking/594ea4db-e852-4852-bf86-19ed29c9b6b7_origin.pdf b/xfactanewbenchmarkdatasetformultilingualfactchecking/594ea4db-e852-4852-bf86-19ed29c9b6b7_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..1cc8018b7e379d71635100c815b99233b3c67bda --- /dev/null +++ b/xfactanewbenchmarkdatasetformultilingualfactchecking/594ea4db-e852-4852-bf86-19ed29c9b6b7_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7b3938227bc909a451483045541567a6f5decf77ebe4e0d1bb54bba8884187fa +size 239114 diff --git a/xfactanewbenchmarkdatasetformultilingualfactchecking/full.md b/xfactanewbenchmarkdatasetformultilingualfactchecking/full.md new file mode 100644 index 0000000000000000000000000000000000000000..02c331e167315921cc00209394af4cc3f1754268 --- /dev/null +++ b/xfactanewbenchmarkdatasetformultilingualfactchecking/full.md @@ -0,0 +1,201 @@ +# X-FACT: A New Benchmark Dataset for Multilingual Fact Checking + +Ashim Gupta, Vivek Srikumar + +School of Computing, + +University of Utah + +{ashim,svivek}@cs.utah.edu + +# Abstract + +In this work, we introduce X-FACT: the largest publicly available multilingual dataset for factual verification of naturally existing real-world claims. The dataset contains short statements in 25 languages and is labeled for veracity by expert fact-checkers. The dataset includes a multilingual evaluation benchmark that measures both out-of-domain generalization, and zero-shot capabilities of the multilingual models. Using state-of-the-art multilingual transformer-based models, we develop several automated fact-checking models that, along with textual claims, make use of additional metadata and evidence from news stories retrieved using a search engine. Empirically, our best model attains an F-score of around $40\%$ , suggesting that our dataset is a challenging benchmark for evaluation of multilingual fact-checking models. + +# 1 Introduction + +Curbing the spread of fake news and misinformation on the web has become an important societal challenge. Several fact-checking initiatives, such as PolitiFact, $^{1}$ expend a significant amount of manual labor to investigate and determine the truthfulness of viral statements made by public figures, organizations, and social media users. Of course, since this process is time-consuming, often, a large number of falsified statements go unchecked. + +With the aim of assisting fact-checkers, researchers in NLP have sought to develop computational approaches to fact-checking (Vlachos and Riedel, 2014; Wang, 2017; Pérez-Rosas et al., 2018). Many such works use the FEVER dataset, which contains claims extracted from Wikipedia documents (Thorne et al., 2018). Using real-world claims, Wang (2017) introduced LIAR, a dataset + +with 12,836 claims from PolitiFact. Recently, Augenstein et al. (2019) introduced MultiFC, an even larger corpus of 34,918 claims collected from 26 fact-checking websites. + +Although misinformation transcends countries and languages (Bradshaw and Howard, 2019; Islam et al., 2020), much of the recent work focuses on claims and statements made in English. Developing Automated Fact Checking (AFC) systems in other languages is much more challenging, the primary reason being the absence of a manually annotated benchmark dataset for those languages. Moreover, there are fewer fact-checkers in these languages, and as a result, a non-English monolingual dataset will inevitably be small and less effective in developing fact-checking systems. As recent research points out, a possible solution in dealing with data scarcity is to train multilingual models (Aharoni et al., 2019; Wu and Dredze, 2019; Hu et al., 2020). Indeed, this finding motivates us to construct a large multilingual resource that the research community can use to further the development of fact-checking systems in languages other than English. + +Recent efforts in the construction of a multilingual dataset are limited, both in scope and in size (Shahi and Nandini, 2020; Patwa et al., 2020). For instance, FakeCovid, a dataset introduced by Shahi and Nandini (2020) contains 3066 non-English claims about COVID-19. In comparison, X-FACT contains 31,189 general domain non-English claims from 25 languages. Moreover, FakeCovid contains only two labels, namely, False, and Others. We argue that this is undesirable, as fact checking is a fine-grained classification task. Due to subtle differences in language, most claims are neither entirely true nor entirely false (Rashkin et al., 2017). In contrast, our dataset contains seven labels—we make distinctions between true, mostly true, half-true etc. Table 1 shows two such + +
ClaimMuslimische Gebete sind Pflichtpro- +gramm an katholischer Schule. +Muslim prayers are compulsory in +Catholic schools.
LabelMostly-False (Grisstenteils Falsch)
ClaimantFreie Welt
LanguageGerman
Sourcede.correctiv.org
Claim DateMarch 16, 2018
Review DateMarch 23, 2018
ClaimTemos, hoje, a despesa de Pre- +vidência Social representando 57% +do orçamento. +Today, we have Social Security ex- +penses representing 57% of the bud- +get.
LabelPartly-True (Exagerado)
ClaimantHenrique Meirelles
LanguagePortuguese (Brazilian)
Sourcept.piaui.folha.uol.com.br
Claim DateNone
Review DateMay 2, 2018
+ +Table 1: Examples from X-FACT. Original labels are shown in parenthesis along with the manually mapped labels. For reference, translations are also shown. + +examples from German and Brazilian Portuguese. In summary, our contributions are: + +1. We release a multilingual fact-checking benchmark X-FACT, which includes 31,189 short statements labeled for factual correctness and covers 25 typologically diverse languages across 11 language families. X-FACT is an order of magnitude larger than any other multilingual dataset available for fact checking. +2. Apart from the standard test set, we create two additional challenge sets to evaluate fact checking systems' generalization abilities across different domains and languages. +3. We report results for several modeling approaches and find that these models underperform on all three test sets in our benchmark, suggesting the need for more sophisticated and robust modeling methods. + +The X-FACT dataset, and the code for our experiments, can be obtained at https://github.com/utahnlp/x-fact. + +# 2 The X-FACT Dataset + +X-FACT is constructed from several fact-checking sources. We briefly outline this process here. + +Sources of Claims. We relied on a list of non-partisan fact-checkers compiled by International Fact-Checking Network (IFCN) $^2$ , and Duke Reporter's Lab $^3$ . We removed all the websites that conduct fact-checks in English and are covered by previous work(Wang, 2017; Augenstein et al., 2019). As a starting point, we first queried Google's Fact Check Explorer (GFCE) $^4$ for all the fact-checks done by a particular website. Then we crawled the linked article on the website and additional metadata such as claimant, URL, date of the claim. For websites not linked through GFCE, we directly crawled all the available fact-checking articles from the fact-checker's website. We left out some fact-checkers because either the claims on their websites were not well specified or the fact-checker did not use any rating scale. We performed semi-automated text processing to remove duplicate claims and examples where the label appeared in the claim itself. This resulted in data from a total of 85 fact checkers for further processing. Refer to the appendix for more details on the this process. + +Filtering the Dataset. There are two major challenges in using the crawled data directly: a) the labels are in different languages, and b) each fact checker uses a different rating scale for categorization. To deal with these issues, first, we manually translated all ratings to English, followed by semiautomatic merging of labels if they were found to be synonyms. Second, in consultation with Factly, $^{5}$ an IFCN signatory, we created a rating scale compatible with most fact-checkers. Our label set contains five labels with a decreasing level of truthfulness: True, Mostly-True, Partly-True, Mostly-False, and False. To encompass several other cases where assigning a label is difficult due to lack of evidence or subjective interpretations, we introduced Unverifiable as another label. A final label Other was used to denote cases that do not fall under the above-specified categories. Following the process described, we reviewed each fact-checker's rating system along with some examples and manually mapped these labels to our newly designed label scheme. See table 1 for examples. In our subsequent discussions, we refer to each fact-checking website as a source. + +$^{2}$ https://www.poynter.org/ifcn/ + $^{3}$ https://reporterslab.org/ +fact-checking/ + $^{4}$ https://toolbox.google.com/factcheck/explorer + $^{5}$ https://factly.in + +
Data split# claims# languages
Train1907913
Development253512
In-domain (α1)382612
Out-of-domain (α2)23684
Zero-Shot (α3)338112
+ +Table 2: Dataset details. X-FACT contains three challenge sets, namely, In-domain Test $(\alpha_{1})$ , Out-of-domain Test $(\alpha_{2})$ , Zero-Shot Test $(\alpha_{3})$ . + +We found that the data from several sources was dominated by a single label $(>80\%)$ . Since it is difficult to train machine learning models on highly imbalanced datasets, we removed 54 such websites. We additionally removed fact-checking websites that contained fewer than 60 examples. In total, our dataset contains 31,189 fact-checks. + +A Single Test Set is Not Sufficient. Recent advances in NLP have shown that multilingual models are effective for cross-lingual transfer (Kondratyuk and Straka, 2019; Wu and Dredze, 2019; Hu et al., 2020). A multilingual fact-checking system of similar transfer capabilities will certainly be an asset, especially in languages with no or few fact-checkers. From this perspective, we seek to provide a robust evaluation benchmark that can help us understand the generalization abilities of our fact-checking systems. + +With this objective, we construct three test sets, namely $\alpha_{1}$ , $\alpha_{2}$ , and $\alpha_{3}$ . The first test set $(\alpha_{1})$ is distributionally similar to the training set. The $\alpha_{1}$ set contains fact-checks from the same languages and sources as the training set. + +Second, the out-of-domain test set $(\alpha_{2})$ , contains claims from the same languages as the training set but are from a different source. A model that performs well on both $\alpha_{1}$ and $\alpha_{2}$ can be presumed to generalize across different source distributions. + +Third test set is the zero-shot set $(\alpha_{3})$ , which seeks to measure the cross-lingual transfer abilities of fact-checking systems. The $\alpha_{3}$ set contains claims from languages not contained in the training set. Models that overfit language-specific artifacts will underperform on $\alpha_{3}$ . + +Languages. For training and development, we choose the top twelve languages based on the num + +ber of labeled examples. The average number of examples per language is 1784, with Serbian being the smallest (835). We split the data into training $(75\%)$ , development $(10\%)$ , and $\alpha_{1}$ test set $(15\%)$ . This leaves us with 13 languages for our zero-shot test set $(\alpha_{3})$ . The remaining set of sources form our out-of-domain test set $(\alpha_{2})$ . See table 2 for the number of claims and langauges in each of these splits. + +In total, X-FACT covers the following 25 languages (shown with their ISO 639-1 code for brevity): ar, az, bn, de, es, fa, fr, gu, hi, id, it, ka, mr, no, nl, pa, pl, pt, ro, ru, si, sr, sq, ta, tr. Please refer to the appendix for more details. + +# 3 Experiments and Results + +# 3.1 Experimental Setting + +The goal of our experiments is to study how different modeling choices address the task of multilingual fact-checking. All our experiments use mBERT, the multilingual variant of BERT (Devlin et al., 2019) and use macro F1 score as the evaluation metric.7 We report average F1 scores and standard deviations on four runs with different random seeds. + +We implement the following multilingual models as baselines for future work: + +1. Claim Only Model (Claim-Only): We provide textual claim as the only input to the model, in effect treating the problem as a simple sentence classification problem. +2. Attention-based Evidence Aggregator (Attn-EA): Typically, to determine the veracity of a claim, fact-checkers first gather relevant evidence by performing a web search and then aggregate this evidence to reach their final decision. We emulate this procedure by developing an attention-based evidence aggregation model that operates on evidence documents retrieved after performing web search with the claim using Google. For each claim, we obtain the top five results and use them as evidence. Using full text from web pages is not feasible, as the mBERT model has a restricted input sequence length of 512. Following previous work (Augenstein et al., 2019), we use snippets from search results as our evidence. + +For a given claim and a collection of $n$ evidence documents, we first encode the claim and evidences separately using mBERT by extracting the output of the CLS token, denoted as: $\mathbf{c}$ , $[\mathbf{e}_1, \mathbf{e}_2, \dots, \mathbf{e}_n]$ . We first apply dot-product attention (Luong et al., 2015) to obtain the attention weights $[\alpha_1, \alpha_2, \dots, \alpha_n]$ , and then compute a linear combination using these attention coefficients: $\mathbf{e} = \sum_{i} \alpha_i \mathbf{e}_i$ . This representation is then concatenated with $\mathbf{c}$ and fed to the classification layer. In all our experiments, we fix the number of evidence documents to five. + +3. Augmenting metadata (+Meta): We concatenate additional key-value metadata with the claim text by representing it as a sequence of the form: Key : Value (Chen et al., 2019). This metadata includes the language, website-name, claimant, claim-date, and review-date. If a certain field is not available for a claim, we represent the value by none. + +All the models are trained in a multilingual setting, i.e., a single model is trained for all languages. We could not use monolingual models as the trained monolingual models were unstable due to the small size of data for each language. + +
Modelα1α2α3
Majority6.9(−)10.6(−)7.6(−)
Claim-Only38.2(0.9)16.2(0.9)14.7(0.6)
Claim-Only + Meta39.4(0.9)15.4(0.8)16.7(1.1)
Attn-EA (Random)37.5(0.8)16.3(0.5)14.9(1.2)
Attn-EA38.9(0.2)15.7(0.1)16.5(0.7)
Attn-EA + Meta41.9(1.2)15.4(1.5)16.0(0.3)
+ +Table 3: Average F1 scores (and standard deviations) of the models studied in this work. Models in top rows are claim-only models while those in bottom are evidence-based. Attn-EA (Random) denotes the results of the evidence-based model when it is trained with random search snippets. (+ Meta) models denote those augmented with additional metadata. + +# 3.2 Results + +The results are shown in table 3. We will discuss results by answering a series of research questions. As an indicator of label distribution, we include a majority baseline with the most frequent label of the distribution (i.e. false). + +Does the dataset exhibit claim-only bias? Before moving to more sophisticated systems, let us first examine if the model can predict a statement's veracity by only using the textual claim. Note that this setting is similar to that of hypothesis only models for the task of Natural Language Inference (NLI) (Poliak et al., 2018). From table 3, we see that a claim-only model outperforms a majority baseline by a large margin. We can draw two inferences: a) A significant number of examples in $\alpha_{1}$ can be labeled by just relying on the textual claim, and b) the claim-only model has learned spurious correlations from the dataset. + +Do search snippets improve fact-checking? First, results from table 3 show that augmenting models with metadata is helpful. Second, using search snippets as evidence with an attention-based model along with metadata improves performance by 2.5 percentage points on the in-domain test set $(\alpha_{1})$ . To further validate that snippets indeed help the evidence-based model, we perform another experiment in which we pair each claim with random search snippets of the same language. Since there is no relevant evidence, the performance is indeed similar to the claim-only model. This again confirms our finding that the dataset exhibits some claim-only bias. + +While the Attn-EA model provides some performance improvement on the in-domain test set, surprisingly, the claim-only model outperforms the evidence-based model by a small margin on $\alpha_{3}$ . This might be due to the evidence-based over-fitting the in-domain data. + +# How informative are the search snippets? + +Note that we used snippets to summarize the retrieved search results. To gauge the relevance of these snippets, we manually examine 100 examples from $\alpha_{1}$ test set for Hindi. Our preliminary analysis reveals that only $45\%$ of snippets provide sufficient information to classify the claim, indicating why the performance increase with the evidence-based model is small. Our same analysis suggests that for $83\%$ of the examples, using full text of the web pages provides sufficient evidence to determine veracity of the claim. Hypothetically, this means, were the models able to ingest large documents (web pages), their performance increase could have been much more significant. + +Do the models generalize across sources and languages? We observe that performance on $\alpha_{2}$ + +and $\alpha_{3}$ is worse than on $\alpha_{1}$ , not only highlighting the difficulty of these challenge sets, but also showing that models overfit both source-specific patterns $(\alpha_{2})$ and language-specific patterns $(\alpha_{3})$ . + +Importantly, these results underscore the utility of our challenge sets in assessing model generalizability as well as diagnosing overfitting. + +
Modelα1α2α3
X-FACT
Claim-Only + Meta39.4(0.9)15.4(0.8)16.7(1.1)
Attn-EA + Meta41.9(1.2)15.4(1.5)16.0(0.3)
X-FACT + English
Claim-Only + Meta37.1(2.7)14.5(0.5)14.4(0.3)
Attn-EA + Meta38.0(4.5)14.7(2.6)14.3(1.9)
+ +Table 4: Performance comparison when augmenting the dataset with 12,311 English claims from PolitiFact. Average F1 scores (and standard deviations) of the models are reported over four random runs. + +Can we improve performance by augmenting training data with English claims? Since X-FACT does not contain any examples from English, we answer this question by augmenting the training set with 12,311 claims from the PolitiFact subset of the MultiFC (Augenstein et al., 2019). Results are shown in table 4. Interestingly, we see that augmenting the models with English data hurts model performance. A possible cause is that the augmented data mostly contains political claims, while our dataset contains general claims. + +# 4 Conclusion + +We presented X-FACT, the currently largest multilingual dataset for fact-checking. Compared to the prior work, X-FACT is an order of magnitude larger, enabling the exploration of large transformer-based multilingual approaches to fact-checking. We presented results for several multilingual modeling methods and showed that the models find this new dataset challenging. We envision our dataset as an important benchmark in development and evaluation of multilingual approaches to fact-checking. + +# Acknowledgments + +We would like to thank members of the Utah NLP group for their valuable insights, reviewers for their helpful feedback, and the team of Factly, $^{8}$ speci + +ally Mr. Shashi Kiran Deshetti, for discussions in developing a rating scale compatible with most fact-checkers. The authors acknowledge the support of NSF grants #1801446 (SATC) and #1822877 (Cyberlearning) and an award from Verisk Inc. + +# References + +Roee Aharoni, Melvin Johnson, and Orhan First. 2019. Massively multilingual neural machine translation. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 3874-3884. +Isabelle Augenstein, Christina Lioma, Dongsheng Wang, Lucas Chaves Lima, Casper Hansen, Christian Hansen, and Jakob Grue Simonsen. 2019. MultiFC: A real-world multi-domain dataset for evidence-based fact checking of claims. In EMNLP-IJCNLP. +Samantha Bradshaw and Philip N Howard. 2019. The global disinformation order: 2019 global inventory of organised social media manipulation. Project on Computational Propaganda. +Wenhu Chen, Hongmin Wang, Jianshu Chen, Yunkai Zhang, Hong Wang, Shiyang Li, Xiyou Zhou, and William Yang Wang. 2019. TabFact: A large-scale dataset for table-based fact verification. In International Conference on Learning Representations. +Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171-4186. +Vivek Gupta, Maitrey Mehta, Pegah Nokhiz, and Vivek Srikumar. 2020. INFOTABS: Inference on tables as semi-structured data. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 2309–2324, Online. Association for Computational Linguistics. +Junjie Hu, Sebastian Ruder, Aditya Siddhant, Graham Neubig, Orhan First, and Melvin Johnson. 2020. XTREME: A massively multilingual multi-task benchmark for evaluating cross-lingual generalisation. In International Conference on Machine Learning, pages 4411-4421. PMLR. +Md Saiful Islam, Tonmoy Sarkar, Sazzad Hossain Khan, Abu-Hena Mostofa Kamal, SM Murshid Hasan, Alamgir Kabir, Dalia Yeasmin, Mohammad Ariful Islam, Kamal Ibne Amin Chowdhury, Kazi Selim Anwar, et al. 2020. Covid-19-related infodemic and its impact on public health: A global social media + +analysis. The American Journal of Tropical Medicine and Hygiene, 103(4):1621. +Dan Kondratyuk and Milan Straka. 2019. 75 languages, 1 model: Parsing universal dependencies universally. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 2779-2795. +Minh-Thang Luong, Hieu Pham, and Christopher D Manning. 2015. Effective approaches to attention-based neural machine translation. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 1412-1421. +Parth Patwa, Shivam Sharma, Srinivas PYKL, Vineeth Gupta, Gitanjali Kumari, Md Shad Akhtar, Asif Ekbal, Amitava Das, and Tanmoy Chakraborty. 2020. Fighting an infodemic: Covid-19 fake news dataset. arXiv preprint arXiv:2011.03327. +Verónica Pérez-Rosas, Bennett Kleinberg, Alexandra Lefevre, and Rada Mihalcea. 2018. Automatic detection of fake news. In Proceedings of the 27th International Conference on Computational Linguistics, pages 3391-3401. +Adam Poliak, Jason Naradowsky, Aparajita Haldar, Rachel Rudinger, and Benjamin Van Durme. 2018. Hypothesis only baselines in natural language inference. In Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics, pages 180-191. +Hannah Rashkin, Eunsol Choi, Jin Yea Jang, Svitlana Volkova, and Yejin Choi. 2017. Truth of varying shades: Analyzing language in fake news and political fact-checking. In Proceedings of the 2017 conference on empirical methods in natural language processing, pages 2931-2937. +Gautam Kishore Shahi and Durgesh Nandini. 2020. *FaKeCovid - a multilingual cross-domain fact check news dataset for pandemic-19.* In *Workshop Proceedings of the 14th International AAAI Conference on Web and Social Media*. +James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal. 2018. FEVER: a largescale dataset for fact extraction and VERIFICATION. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 809-819. +Andreas Vlachos and Sebastian Riedel. 2014. Fact checking: Task definition and dataset construction. In Proceedings of the ACL 2014 workshop on language technologies and computational social science, pages 18-22. +William Yang Wang. 2017. "Liar, Liar Pants on Fire": A new benchmark dataset for fake news detection. In Proceedings of the 55th Annual Meeting of the + +Association for Computational Linguistics (Volume 2: Short Papers), pages 422-426. +Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumont, Clement Delangue, Anthony Moi, Pierrick Cistac, Tim Rault, Rémi Louf, Morgan Funtopicz, et al. 2019. Huggingface's transformers: State-of-the-art natural language processing. arXiv preprint arXiv:1910.03771. +Shijie Wu and Mark Dredze. 2019. Beto, bentz, becas: The surprising cross-lingual effectiveness of bert. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 833-844. + +# A Details on Dataset Construction + +1. As mentioned in the paper, we omit several fact-checking websites from our data. A large number of these websites are not amenable to crawling and scraping the data. For instance, $\mathrm{AFP}^9$ is a prominent fact-checker for many Indo-European Romance languages, but the template on its website does not lend itself to automatic data extraction tools. We can try to access this websites using GFCE, but case many times, the ratings assigned are sentences instead of a single label. +2. Another common reason is that on a number of these websites, the claim statements are not well-specified. Take for example Faktograf10, a website performing fact-checking in Croatian. On this website, we can neither properly extract the claim statements nor do they clearly mention the rating assigned to the articles. +3. For a small percentage of the claim statements, Google search did not yield any results. We omitted all of these claims from our training, development, and test sets. These are only a very small percentage of claims, so we remove them from all models. + +Because of these reasons, a large number of websites in a number of languages could not be crawled. + +There are two ways we obtain our claims, labels, and other metadata. One is the Google's Fact Check Explorer (GFCE) $^{11}$ , and the other is by crawling from the respective fact-checking website. In case, the links are available on GFCE, we download other metadata by visiting the website. Also, we will release the label mapping we created along with the dataset. Appendix A provides more details on the dataset we collected. + +# B Reproducibility + +In this section, we provide details on our hyperparameter settings along with some comments on reproducibility. + +
DatasetModelRunTime
X-FACTClaim1.5 hr
X-FACTClaim+Meta1.5 hr
X-FACTAttn-EA2.3 hr
X-FACTAttn-EA + Meta2.3 hr
X-FACT + EngClaim+Meta2.5 Hr
X-FACT + EngAttn-EA + Meta4.1 Hr
+ +Table 5: Average Training time of the models trained + +# B.1 Models and Code + +As described in the main paper, we used multilingual BERT for performing our experiments. We implemented all our models in PyTorch using the transformers library (Wolf et al., 2019). + +# B.2 Computing Infrastructure Used + +All of our experiments required access to GPU accelerators. We ran our experiments on three machines: Nvidia Tesla V100 (16 GB VRAM), Nvidia Tesla P100 (16 GB VRAM), Tesla A100 (40 GB VRAM). Our experiments for the claim-only model were run on V100, and P100 GPUs and evidence-based models required larger VRAM, so they were run on A100 GPUs. + +# B.3 Hyperparameters and Fine-tuning Details + +1. We used the mBERT-base model for all of our experiments. This model has 12 layers each with hidden size of 768 and number of attention heads equal to 12. Total number of parameters in this model is 125 million. We set all the hyper-parameters as suggested by Devlin et al. (2019), except the batch size which is fixed to 8. +2. All our models were run with four random seeds (seed = [1, 2, 3, 4]) and the numbers reported in paper are the means of these four runs. We fine-tuned all models for ten epochs and the model performing the best on development set across all epochs was chosen as the final model. +3. Due to constraints on the VRAM of the GPUs, we restricted the number of evidence documents to five. + +Average Run times Average training times are presented in table 5. + +
LanguageISO 639-1 codeFactCheckerLanguage FamilyTrainDevα1α2α3
Arabicarmisbar.comAfro-Asiatic✓✓✓
Bengalibndailyo.inIE: Indo-Aryan✓
Spanisheschequeado.comIE: Romance✓✓✓
Persianfafactnameh.comIE: Iranian✓
Indonesianidcekfakta.comAustronesian✓✓✓
Indonesianidcekfakta.tempo.coAustronesian✓
Italianitpagellapolitica.itIE: Romance✓✓✓
Italianitagi.itIE: Romance✓
Hindihiaajtak.inIE: Indo-Aryan✓✓✓
Hindihihindi.newschecker.inIE: Indo-Aryan✓
Gujaratigugujarati.newschecker.inIE: Indo-Aryan✓
Georgiankafactcheck.geKartvelian✓✓✓
Marathimrmarathi.newschecker.inIE: Indo-Aryan✓
Punjabipapunjabi.newschecker.in.txtIE: Indo-Aryan✓
Polishpldemagog.org.plIE: Slavic✓✓✓
Portugueseptpiaui.folha.uol.com.brIE: Romance✓✓✓
Portugueseptpoligrafo.sapo.ptIE: Romance✓✓✓
Romanianrofactual.roIE: Romance✓✓✓
Norwegiannofaktisk.noIE: Germanic✓
Sinhalasisrilanka+factcrescendo.comIE✓
Serbiansristinomer.rsIE: Slavic✓✓✓
Tamiltayouturn.inDravidian✓✓✓
Albaniansqkallxo.comIE: Albanian✓
Albaniansqfaktoje.alIE: Albanian✓
Russianrufactcheck.kzIE: Slavic✓
Turkishtrdogrulukpayi.comTurkic✓✓✓
Turkishtrteyiit.orgTurkic✓
Azerbaijaniazfaktyoxla.infoTurkic✓
Portugueseptaosfatos.orgIE: Romance✓
Germandecorrectiv.orgIE: Germanic✓✓✓
Dutchnlnieuwscheckers.nlIE: Germanic✓
Frenchfrfr.africacheck.orgIE: Romance✓
+ +Table 6: Details of the X-FACT dataset. Our dataset belongs to 25 typologically diverse languages across 11 language families. The table shows the composition of training, development, and three challenge sets. IE: denotes Indo-Aryan \ No newline at end of file diff --git a/xfactanewbenchmarkdatasetformultilingualfactchecking/images.zip b/xfactanewbenchmarkdatasetformultilingualfactchecking/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..0d491b711b25fac2300a781aad4b99ae6e32cdcc --- /dev/null +++ b/xfactanewbenchmarkdatasetformultilingualfactchecking/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fc32dbb08ae3a4462583616e0d06d01efeb70052355f596333abc50af76c1dab +size 357662 diff --git a/xfactanewbenchmarkdatasetformultilingualfactchecking/layout.json b/xfactanewbenchmarkdatasetformultilingualfactchecking/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..5c89d4446ced06878b3a6dffffc6bf38b4e32fa8 --- /dev/null +++ b/xfactanewbenchmarkdatasetformultilingualfactchecking/layout.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a293bab3c23ccdc79a54c51a58087f7fa274f0662444cd0ff7ae53705ce36f02 +size 235978 diff --git a/zeroshoteventextractionviatransferlearningchallengesandinsights/e5dc6313-400a-4012-9eeb-215f614580f1_content_list.json b/zeroshoteventextractionviatransferlearningchallengesandinsights/e5dc6313-400a-4012-9eeb-215f614580f1_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..2012d76e5fe6b31cd82ed4380c69efc2f1857415 --- /dev/null +++ b/zeroshoteventextractionviatransferlearningchallengesandinsights/e5dc6313-400a-4012-9eeb-215f614580f1_content_list.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:836c69ca5bf43de3c8b9b5aaabd4f52442821c8b99767507bea384f94c2ce6c4 +size 82894 diff --git a/zeroshoteventextractionviatransferlearningchallengesandinsights/e5dc6313-400a-4012-9eeb-215f614580f1_model.json b/zeroshoteventextractionviatransferlearningchallengesandinsights/e5dc6313-400a-4012-9eeb-215f614580f1_model.json new file mode 100644 index 0000000000000000000000000000000000000000..1c56ad7034c6f5ee0f601b8c4df3fb18bebf9ec9 --- /dev/null +++ b/zeroshoteventextractionviatransferlearningchallengesandinsights/e5dc6313-400a-4012-9eeb-215f614580f1_model.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3c39ef65cf3fd3db1c304c52c91c9ff140298a4fa05261c89769ef6061389daa +size 100755 diff --git a/zeroshoteventextractionviatransferlearningchallengesandinsights/e5dc6313-400a-4012-9eeb-215f614580f1_origin.pdf b/zeroshoteventextractionviatransferlearningchallengesandinsights/e5dc6313-400a-4012-9eeb-215f614580f1_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..725b0ae5f0021ca94211583d443015248d27a571 --- /dev/null +++ b/zeroshoteventextractionviatransferlearningchallengesandinsights/e5dc6313-400a-4012-9eeb-215f614580f1_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e4527c3343bfe931060d72d4f314c9e078422156082b57c91d05d516cd315476 +size 1440534 diff --git a/zeroshoteventextractionviatransferlearningchallengesandinsights/full.md b/zeroshoteventextractionviatransferlearningchallengesandinsights/full.md new file mode 100644 index 0000000000000000000000000000000000000000..bd81468462d01ced062bb87214f4c7e719465054 --- /dev/null +++ b/zeroshoteventextractionviatransferlearningchallengesandinsights/full.md @@ -0,0 +1,352 @@ +# Zero-shot Event Extraction via Transfer Learning: Challenges and Insights + +Qing Lyu $^{1}$ , Hongming Zhang $^{2*}$ , Elior Sulem $^{1}$ , Dan Roth $^{1}$ + +$^{1}$ Department of Computer and Information Science, UPenn $^{2}$ Department of Computer Science and Engineering, HKUST {lyuqing, eliors, danroth}@seas.upenn.edu hzhangal@cse.ust.hk + +# Abstract + +Event extraction has long been a challenging task, addressed mostly with supervised methods that require expensive annotation and are not extensible to new event ontologies. In this work, we explore the possibility of zero-shot event extraction by formulating it as a set of Textual Entailment (TE) and/or Question Answering (QA) queries (e.g. "A city was attacked" entails "There is an attack"), exploiting pretrained TE/QA models for direct transfer. On ACE-2005 and ERE, our system achieves acceptable results, yet there is still a large gap from supervised approaches, showing that current QA and TE technologies fail in transferring to a different domain. To investigate the reasons behind the gap, we analyze the remaining key challenges, their respective impact, and possible improvement directions1. + +# 1 Introduction + +Event extraction (EE) has long been an important and challenging NLP task. Figure 1 exemplifies a TRANSFER-OWNERSHIP event from the ACE-2005 dataset (Walker et al., 2006), where the trigger is "purchased" and the arguments include "China" (Buyer), "Russia" (Seller), etc. The subtasks of EE involve identifying and classifying event triggers and their corresponding arguments. + +The predominant approaches normally require supervision (e.g. Lin et al., 2020), which is both expensive and inflexible when moving to new event ontologies. Recent works (Chen et al., 2020; Du and Cardie, 2020) have pointed out the connection between Question Answering (QA) and EE in developing supervised systems. Meanwhile, several efforts have explored unsupervised methods. Peng et al. (2016) first attempted to extract event triggers with minimal supervision using similarity-based + +Event type: TRANSFER-OWNERSHIP China has purchased two nuclear submarines from Russia last month. Buyer-Arg Trigger Artifact-Arg Seller-Arg Time-Arg + +Q1: Who bought something? +Q2: Who sold something? +Q3: What is bought? +Q4: Where is the purchase? +A1: China +A2: Russia +A3: Two nuclear submarines +A4: No Answer + +·· + +Figure 1: An example of an event from ACE-2005, and how arguments are extracted via QA. + +heuristics. Huang et al. (2018) and Lai et al. (2020) explored both trigger and argument extraction under a slightly different setting: training on some event types and testing on unseen ones. Recently, Liu et al. (2020) proposed a QA-based zero-shot argument extraction method, which did not handle triggers. So far, no method has been proposed to extract both event triggers and arguments without any EE training data2. Moreover, the performance of existing zero-shot attempts, especially on arguments, is still far from satisfactory, yet little is known about possible underlying reasons. + +In this work, we investigate the possibility of zero-shot EE via transfer learning from Textual Entailment (TE) and QA. Observe that given pretrained TE/QA models, extracting events can be viewed as answering questions/verifying hypotheses about a text. For example, the sentence in Figure 1, taken as the premise, would entail the hypothesis "There is a transfer of ownership", therefore providing the event type. Then, by asking $Q_{1}$ "Who bought something?", we obtain "China" as the Buyer. Similarly, $Q_{2}, Q_{3}$ will yield the Seller and Artifact, and so on. + +Based on the observation above, we propose an intuitive zero-shot EE approach. It does not require any event training data, but we still make several design choices based on the development set. To demonstrate the level of generalization, we choose the optimal model with the ACE development set, and evaluate it on both ACE and ERE (LDC2015E29) test sets. The performance + +surpasses previous zero-shot approaches on every subtask when the gold trigger span is given, yet is still unsatisfying compared to supervised methods, revealing a large gap in using off-the-shelf TE/QA models for direct transfer. To shed light on why it is the case, we identify the key challenges behind the gap, and attribute each of them to the intrinsic weakness of pretrained models, our usage of them, or the task itself. We then anatomize their individual impact with an ablation study. + +Our contributions are: (1) We propose the first TE/QA-based event extraction system that tackles both triggers and arguments without any event training data; (2) We show that existing TE/QA models do not support direct domain transfer well; and (3) We provide insights into the remaining challenges, their individual influence, and possible directions for future research. + +# 2 Approach + +Our pipeline consists of two modules, trigger extraction and argument extraction, both relying on pretrained TE/QA models for direct transfer. + +The pretrained models we use are all BERT-based (Devlin et al., 2019; Liu et al., 2019; Lewis et al., 2020), including a TE model trained on MNLI (Williams et al., 2018), a Yes/No QA model trained on BoolQ (Clark et al., 2019), and an extractive QA model trained on QAMR (Michael et al., 2018) and/or SQuAD $2.0^{3}$ (Rajpurkar et al., 2018) $^{4}$ . The TE model, when given a premise and a hypothesis, predicts the relation between them ("entailment", "contradiction", or "neutral"). The Yes/No QA model takes as input a context and a Yes/No question, and returns either Yes or No. Finally, the extractive QA model is also given a context but with a Wh-question, and the answer is a span in the context. With these models, we design the two modules for event extraction. + +# 2.1 Trigger Extraction (T-Ext) + +We formulate Trigger Extraction (T-Ext) as a TE or a Yes/No QA task. Only the TE case is illustrated, since the other only differs in the query format. + +To obtain potential event triggers from a sentence, we first run Semantic Role Labeling (SRL) as a preprocessing step. We use a BERT-based Verb+Nominal SRL model5. The sentence is then + +
ArgumentQuestion
Artifact“What is bought?”
Buyer“Who buys something?”
Seller“Who sells something?”
Price“How much does something cost?”
Beneficiary“Who is something bought for?”
Time“When is the purchase?”
Place“Where is the purchase?”
+ +Table 1: The predefined question for each argument type in an TRANSFER-OWNERSHIP event. + +chunked into "text pieces", each containing an SRL predicate and its core arguments (e.g. $A_0, A_1, A_2$ ). + +Then, for each text piece, we pass it to the TE model as the premise, coupled with a hypothesis in the format of "This text is about ..." for each event type, inspired by Yin et al. (2019). For example, the hypothesis for BE-BORN is "This text is about someone's birth." Then, for each hypothesis, the model returns the probability that it is entailed by the premise. If the highest entailment probability across all event types surpasses a threshold, we output the corresponding SRL predicate as an event trigger of this type.[6] + +# 2.2 Argument Extraction (A-Ext) + +We formalize the task of Argument Extraction (A-Ext) as a sequence of QA interactions with the pretrained extractive QA model. + +Given an input sentence and the extracted trigger, we ask a set of questions based on the event type definition, and retrieve the QA model's answers as argument predictions. + +Consider the example in Figure 1. Assume that T-Ext has identified a TRANSFER-OWNERSHIP event with the trigger "purchased". With this information, we consult a predefined set of questions for each argument type in the current event type. For instance, Table 1 provides a full collection of questions for all arguments in TRANSFER-OWNERSHIP. Finally, to obtain the head of the argument (e.g. "submarines" in "two nuclear submarines"), we implement a simple heuristics-based head identifier based on the AllenNLP Dependency Parser as a post-processing step. + +An important caveat in the above process concerns missing arguments. Specifically, many argument types in the event template do not occur in every sentence, e.g. in Figure 1, there is no Place argument. For simplicity, we call questions with a non-empty gold answer "has-answer" (HA) ques + +
SettingSystemTITI+TCAIAI+AC
scratch(supervised)Lin et al. 2078.274.759.256.8
scratch(zero-shot)Huang et al. 18855.649.127.815.8
Zhang et al. 2058.353.516.36.3
Ours45.541.727.016.8
gold TI(zero-shot)Huang et al. 18-33.5-14.7
Zhang et al. 20-82.9--
Ours-83.738.924.2
gold TI+TC(zero-shot)Liu et al. 20---25.8
Ours--44.327.4
+ +tions and the rest "no-answer" (NA) questions. The QA model is considered to output NA when it predicts an empty span or the highest non-empty span confidence is lower than a threshold. + +# 3 Experimental Setup + +We evaluate our system on the ACE-2005 dataset. Its event ontology has 7 types and 33 subtypes, and we evaluate T-Ext directly on the subtypes. The same train/development/test split from Lin et al. (2020) is used. We make several design choices9 on the development and report results on the test, ignoring the training set. + +To demonstrate how our model generalizes, we also directly evaluate the optimal model on the ERE dataset (LDC2015E29). To adapt to ERE, we define a query for each new event type. + +There are four subtasks of event extraction: Trigger Identification (TI), Trigger Classification (TC), Argument Identification (AI), and Argument Classification (AC). We experiment under three settings: scratch, where the system performs all subtasks without any gold annotation; gold TI, where gold trigger spans are given; gold TC, where gold trigger spans and types are given10. + +Following Ji and Grishman (2008), Precision, Recall, and F1 are used for evaluation11. We evaluate argument spans on the head level, consistent with most prior work (Huang et al., 2018; Wadden et al., 2019; Lin et al., 2020; Zhang et al., 2021). + +# 4 Results + +We report results in comparison with several existing zero-shot methods (Huang et al., 2018; Liu + +Table 2: The F1 score on ACE-2005. Subtasks include Trigger Identification (TI), Trigger Classification (TC), Argument Identification (AI), and Argument Classification (AC). See Section 3 for setting definitions. SOTA results among zero-shot methods are in boldface. + +
SettingSystemTITI+TCAIAI+AC
scratch (supervised)Lin et al. 2068.457.050.146.5
scratch39.831.823.015.0
gold TI-58.430.818.8
gold TI+TCOurs--47.927.5
(zero-shot)
+ +Table 3: The F1 score on the ERE. The optimal model is chosen on ACE dev and directly evaluated on ERE. + +et al., 2020; Zhang et al., 2021), as well as a supervised SOTA system (Lin et al., 2020). + +As shown in Table 2, on the ACE test set, our system outperforms prior zero-shot methods in every subtask under both the "gold TI" and "gold $\mathrm{TI + TC}$ " settings. However, it fails in "scratch", indicating that the main bottleneck lies in identifying exact trigger spans. Compared with the supervised SOTA, our system is still notably worse on TI, AI, and AC in particular, like other zero-shot systems. + +Table 3 shows the results on ERE. Compared to ACE, our argument detection module generalizes well, whereas the trigger module does not. Under the gold TI setting, the TC F1 on overlapping event types is 70.4, whereas on new event types it is only 19.0, likely because the newly added event types in ERE have a finer definition. For example, a model needs to understand "whether a contact is in-person or not" to distinguish between MEET (in-person), CORRESPONDENCE (not in-person), and CONTACT (unsure). Further research should focus on how to effectively generalize to new event types with subtle definitions. + +# 5 Analysis + +Using the results on ACE, we now present an analysis of the remaining core challenges of the task, along with an ablation study on their individual impact. To further understand the challenges, we attribute each to the fragility of the pretrained models (M-Error), our usage of the models (U-Error), or the task itself (T-Error). + +# 5.1 Trigger Extraction + +# 5.1.1 Error Analysis + +We first analyze the distribution of error types. Specifically, we manually check 100 wrong predictions and show the counts in Figure 2(a). Only the most frequent types are discussed here, and the remaining can be found in Appendix E.1.1. + +Subtle trigger (M-Error): This is the main intrinsic error from the TE model $(17\%)$ . Event types like DIE & EXECUTE, ATTACK & INJURE, and MEET & PHONE-WRITE are especially confus + +![](images/0a7d586295cdcc11c9edef8ef9fd606e7c850e2eea85803bc852927f8d762177.jpg) +Figure 2: Error types in trigger and argument extraction in 100 wrong predictions. The count sum exceeds 100 since a prediction can contain multiple types of error. Colors/patterns indicate the origin of the error type. + +ing. Though their definitions slightly differ, the model fails to capture this level of subtlety. + +Distracting & Insufficient context (U-Error): Two other error types from our usage of the TE model concern distracting $(18\%)$ or insufficient $(19\%)$ contexts. An example of distracting context is "The woman's parents ... found the decomposing body". Given the word "decomposing", the model predicts it as a DIE event trigger, due to "body" in the premise. In contrast, insufficient contexts provide too little information. For example, in the sentence "Turkey sent 1,000 troops ... and said it would send more", the TE model is asked to predict the event type of "send" but only sees "it send more" as the premise, since "troops" is not part of the SRL arguments of "send". As a result, the model predicts a TRANSFER-MONEY instead of TRANSPORT event. + +Hypothetical event & Annotation ambiguity (T-Error): Finally, two error types stem from the task itself: "hypothetical events" $(10\%)$ and "annotation ambiguity" $(4\%)$ . Hypothetical events refer to sentences like "They will not buy it if it is too expensive", where the TE model predicts "buy" as a TRANSFER-OWNERSHIP event trigger. Though such events should be annotated as per the ACE Annotation Guideline (3.4), this is not always strictly followed. Other cases of inconsistent annotation also cause errors, e.g. among all occurrences of "give birth to", the trigger is "give" in some cases, while "birth" in others. + +# 5.1.2 Ablation Study + +We further explore the two U-Error types, by measuring their influence on the performance while + +controlling for other factors. Only one type is included in this section, and the remaining can be found in Appendix E.1.2. + +Premise design: To see the impact of insufficient & distracting context, we select all instances of these two types, and change the premise design. The re-prediction is done under gold-TI. For insufficient contexts, the premise is now the entire sentence. For distracting contexts, we adopt a "minimal-pair premise" strategy: Premise A is the original (e.g. "...decomposing body..."); Premise B is formed by deleting the candidate trigger from A (e.g. "...body..."). Then, we take the event type with the highest entailment probability difference between A and B as the prediction. Intuitively, this difference signifies the semantic contribution of the candidate trigger toward an event type. + +After re-prediction, $59\%$ errors are corrected on insufficient contexts. Among the remaining $41\%$ , it is either the case that the model still ignores the context, or that the longer context now brings distraction. + +On distracting contexts, only $18\%$ errors are corrected. The model still cannot overcome the distraction in most remaining errors, which suggests that a more complicated strategy is needed in addition to manipulating the premise. + +# 5.2 Argument Extraction + +# 5.2.1 Error Analysis + +Likewise, we analyze 100 wrong argument predictions and discuss several major error types. Figure 2(b) shows their respective counts. For a full explanation, see Appendix E.2.1. + +Competitive entity & Non-competitive NA ques + +tions (M-Error): The QA model is intrinsically weak on "competitive entities" $(24\%)$ and "non-competitive NA questions" $(13\%)$ . + +When identifying an argument for the target event, another entity of the same type, i.e. a "competitive entity", can co-occur in the context. For example, the sentence "A unit ... meets in confidential sessions to review terrorist activities in Europe" has a MEET event. When asked "Where is the meeting", our model answers "Europe" whereas the gold answer is empty, since "in Europe" is attached to "activities". We find that models trained on extractive QA data are easily fooled by such entities, if they are of the desired type asked by the question. Note that competitive entities can occur for both HA and NA questions. + +The other type involves NA questions without any competitive entity. For example, given the sentence "Iraqi forces responded with artillery fire", the question "When is the fire" has no answer, and there is no Time-type entity to distract the model. However, the model can still give arbitrary answers (e.g. "artillery") with very high confidence, due to its inherent incapacity for NA questions. + +Ungrammatical question (U-Error): This relatively frequent error type $(15\%)$ is attributable to our usage of the QA model. To facilitate the model to better locate the target event, we embed the trigger in the questions whenever possible, which sometimes unavoidably makes them ungrammatical. For example, our question for the Place argument in a TRANSFER-OWNERSHIP event is "Where is the {trigger}". This is only grammatical when the trigger is a noun. Thus, the QA model may be confused by such questions. + +# 5.2.2 Ablation Study + +To isolate A-Ext, we perform the ablation study under the gold $\mathrm{TI + TC}$ setting. We explore four error types involving both M-Error and U-Error, two of which are included in this section, the rest in Appendix E.2.2. + +Pretraining data: To examine the influence of NA questions, we compare QA models trained on QAMR (He et al., 2020) and SQuAD2.0, only the latter of which has NA questions. Results show that the one trained on QAMR greatly outperforms the one on SQuAD (+16.9 on AI; +13.6 on AC). To unveil why it is the case, we propose three hypotheses: (1) QAMR and ACE both have one-sentence contexts, while SQuAD has paragraphs. (2) The NA questions in SQuAD "confuses" the model, i.e. + +SQuAD and ACE have similar types of HA questions, while different types of NA questions. (3) The density of answers per sentence is high in both QAMR and ACE, while low in SQuAD. We test each hypothesis using controlled experiments, but none of them turns out to provide a full explanation of the performance difference12. + +Moreover, we train a binary classifier for HA and NA questions on a balanced sample of SQuAD, resulting in over 86 in-domain accuracy. On ACE, this number drops to 57. This shows that the QA model cannot even distinguish well between HA and NA questions when it comes to a new dataset, let alone answer them. + +Question grammaticality: To see the impact of ungrammatical questions, we manually correct the grammatical error and re-predict with the model. Among all relevant wrong predictions, $40\%$ are now correct. The rest $60\%$ are mostly also NA questions that prove to require more than just fixing the grammar to solve. + +# 6 Conclusions + +We propose the first complete zero-shot event extraction system via transfer learning from TE and QA. While QA/TE models perform exceptionally well on standard benchmarks (SQuAD, QAMR, MNLI), they do not generalize as expected when being used on EE datasets. We analyze the limited success and several main challenges of the current approach, and provide insights for future improvements. + +# Acknowledgments + +This work was supported in part by Contracts FA8750-19-2-1004 and FA8750-19-2-0201 with the US Defense Advanced Research Projects Agency (DARPA) and by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via IARPA Contracts No. 2019-19051600006 and 2019-19051600004 under the BETTER Program. The views expressed are those of the authors and do not reflect the official policy or position of the Department of Defense or the U.S. Government. + +We thank Celine Lee and Hangfeng He for providing the SRL and QAMR models respectively. We also thank Ying Lin, Jian Liu, Lifu Huang, Haochen Zhang, and the anonymous reviewers for their valuable help and/or feedback. + +# References + +Yunmo Chen, Tongfei Chen, Seth Ebner, Aaron Steven White, and Benjamin Van Durme. 2020. Reading the manual: Event extraction as definition comprehension. In Proceedings of the Fourth Workshop on Structured Prediction for NLP, pages 74-83. +Christopher Clark, Kenton Lee, Ming-Wei Chang, Tom Kwiatkowski, Michael Collins, and Kristina Toutanova. 2019. *Boolq: Exploring the surprising difficulty of natural yes/no questions.* In *Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*, Volume 1 (Long and Short Papers), pages 2924–2936. +Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171-4186, Minneapolis, Minnesota. Association for Computational Linguistics. +Xinya Du and Claire Cardie. 2020. Event extraction by answering (almost) natural questions. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 671-683. +Hangfeng He, Qiang Ning, and Dan Roth. 2020. Quase: Question-answer driven sentence encoding. In Proc. of the Annual Meeting of the Association for Computational Linguistics (ACL). +Lifu Huang, Heng Ji, Kyunghyun Cho, Ido Dagan, Sebastian Riedel, and Clare Voss. 2018. Zero-Shot Transfer Learning for Event Extraction. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2160-2170, Melbourne, Australia. Association for Computational Linguistics. +Heng Ji and Ralph Grishman. 2008. Refining event extraction through cross-document inference. In Proceedings of ACL-08: Hlt, pages 254-262. +Viet Dac Lai, Thien Huu Nguyen, and Frank Dernoncourt. 2020. Extensively matching for few-shot learning event detection. In Proceedings of the First Joint Workshop on Narrative Understanding, Storylines, and Events, pages 38-45. +Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020. BART: Denoising sequence-to-sequence pretraining for natural language generation, translation, and comprehension. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7871-7880, Online. Association for Computational Linguistics. + +Ying Lin, Heng Ji, Fei Huang, and Lingfei Wu. 2020. A joint neural model for information extraction with global features. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7999-8009. +Jian Liu, Yubo Chen, Kang Liu, Wei Bi, and Xiaojiang Liu. 2020. Event Extraction as Machine Reading Comprehension. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1641-1651, Online. Association for Computational Linguistics. +Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692. +Julian Michael, Gabriel Stanovsky, Luheng He, Ido Dagan, and Luke Zettlemoyer. 2018. Crowdsourcing question-answer meaning representations. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 560-568, New Orleans, Louisiana. Association for Computational Linguistics. +Haoru Peng, Yangqiu Song, and Dan Roth. 2016. Event Detection and Co-reference with Minimal Supervision. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 392-402, Austin, Texas. Association for Computational Linguistics. +Vasin Punyakanok, Dan Roth, and Wen-tau Yih. 2008. The importance of syntactic parsing and inference in semantic role labeling. Computational Linguistics, 34(2):257-287. +Pranav Rajpurkar, Robin Jia, and Percy Liang. 2018. Know what you don't know: Unanswerable questions for squad. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 784-789. +David Wadden, Ulme Wennberg, Yi Luan, and Hannaneh Hajishirzi. 2019. Entity, relation, and event extraction with contextualized span representations. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5784-5789, Hong Kong, China. Association for Computational Linguistics. +Christopher Walker, Stephanie Strassel, Julie Medero, and Kazuaki Maeda. 2006. Ace 2005 multilingual training corpus. Linguistic Data Consortium, Philadelphia, 57:45. +Adina Williams, Nikita Nangia, and Samuel Bowman. 2018. A broad-coverage challenge corpus for sentence understanding through inference. In Proceedings of the 2018 Conference of the North American + +Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 1112-1122, New Orleans, Louisiana. Association for Computational Linguistics. + +Wenpeng Yin, Jamaal Hay, and Dan Roth. 2019. Benchmarking zero-shot text classification: Datasets, evaluation and entailment approach. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 3914-3923, Hong Kong, China. Association for Computational Linguistics. + +Hongming Zhang, Haoyu Wang, and Dan Roth. 2021. Unsupervised label-aware event trigger and argument classification. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics (ACL) Findings. + +# A Dataset Statistics + +The pretraining datasets we use include MNLI (Williams et al., 2018), BoolQ (Clark et al., 2019), QAMR (Michael et al., 2018), and SQuAD2.0 (Rajpurkar et al., 2018). Our evaluation dataset is ACE2005 (LDC2006T06) and ERE (LDC2015E29). Table 4 shows the number of examples in each dataset. + +
DatasetTrainDevTest
MNLI392,70220,00020,000
BoolQ9,4273,2703,245
QAMR73,56127,53526,994
SQuAD2.0130,31911,8738,862
ACE-200517,172923832
ERE--2,069
+ +Table 4: Number of examples in all datasets used. + +# B Details on Pretrained Models + +We use three different pretrained representations, BERT (Devlin et al., 2019), RoBERTa (Liu et al., 2019), and BART (Lewis et al., 2020). All models are implemented with HuggingFace Transformers $^{13}$ . + +The pretrained model checkpoints we use include: bert-base-uncased (110M parameters), bert-large-uncased (336M + +parameters), roberta-base (125M parameters), roberta-large (335M parameters), facebook/bart-base (373M parameters), facebook/bart-large (406M parameters) $^{14}$ . + +For TE and Yes/No QA, we finetune the pretrained models using the standard SequenceClassification pipeline. For extractive QA, we finetune the models using the QuestionAnswering pipeline15. The finetuning scripts are adapted from the text-classification and question-answering examples in the HuggingFace Transformers repository16. The hyperparameter values and pretrained models will be made available via the HuggingFace model sharing service. + +We run our experiments on an NVIDIA GeForce RTX 2080 Ti GPU, with half-precision floating point format (FP16) with O1 optimization. The finetuning take 3 hours to 20 hours depending on the task. + +# C Details on Event Extraction System + +We include here a full list of hyperparameter configurations explored in building our event extraction system. To select the optimal configuration, we perform grid-search on the development set based on the F1 score. + +# C.1 Preprocessing + +We adapt the preprocessing script from Lin et al. (2020) $^{17}$ . In addition, we use several general-purpose NLP tools to further process the text, including a Part-of-Speech Tagger, a Dependency Parser, a Constituency Parser $^{18}$ . + +# C.2 Trigger Extraction Module + +Pretrained representation As said in Appendix B, we experiment with three representations (BERT, RoBERTa, and BART) with their base and large versions. + +Pretraining task We have two pretraining task choices, TE (using MNLI as training data) and Yes/No QA (using BoolQ as training data). + +SRL constituents in the premise For each predicate, we only include itself and a few core arguments to form the premise. The combinations we try include: Predicate only; Predicate, Arg0, Arg1, Arg2; Predicate and all arguments. + +Confidence threshold For an SRL predicate to be identified as an event trigger, we require that the confidence score of the TE model on the "Entailment" label (resp. the Yes/No QA model on the "Yes" label) exceeds a threshold. We search the threshold value within the range of [0.80, 0.85, 0.90, 0.95, 0.99]. + +Hypothesis format We experiment with two strategies to phrase the hypothesis: + +- **Topical:** The hypothesis is in the format of "This text is about {topic}", where the "\{topic\}" is predefined for each event type. For example, for ATTACK, the hypothesis is "This text is about an attack". +- Natural: The hypothesis is in a natural language format. For example, for ATTACK, it is "Someone is attacked". + +The optimal configuration for trigger extraction is: + +- Pretrained representation: RoBERTa-large; +- Pretraining task: TE; +- SRL arguments in the premise: Predicate, Arg0, Arg1, Arg2; +- Confidence threshold: 0.99; +- Hypothesis format: Topical. + +# C.3 Argument Extraction Module + +Pretrained representation As said in Appendix B, we experiment with three representations (BERT, RoBERTa, and BART) with their base and large versions. + +Pretraining data We have two extractive QA datasets for pretraining, SQuAD2.0 and QAMR (and also their combination). + +Question format We experiment with two question formats: + +- Static: The questions are fixed for each event type. For example, the question for the Place argument in an ATTACK event is always "Where is the attack?" + +- Contextualized: The questions are instantiated with the trigger of event instances when possible. For example, the question for the Place argument in an ATTACK event is "Where is the {trigger}?", where " $\{ \text{trigger} \}$ " is the specific trigger token(s) of the current event instance20. + +Confidence threshold For the extractive QA model to predict a non-empty answer, we require that its confidence score should be higher than a threshold. We search within the range of $[0.0, 0.1, 0.3, 0.5, 0.7, 0.9, 0.99]$ . + +The optimal configuration for argument extraction is: + +- Pretrained representation: RoBERTa-large; +- Pretraining data: QAMR; +- Question format: Contextualized; +- Confidence threshold: 0.0 (the threshold value makes almost no difference, since most model prediction confidence scores are over 0.99). + +# D Full Results + +Complementary to Section 4, Table 5 and Table 6 shows the full results including Precision, Recall, and F1 score on ACE and ERE respectively. + +# E Analysis (Continued) + +This section elaborates on the remaining error types and ablation study experiments not covered by Section 5. + +# E.1 Trigger Extraction + +# E.1.1 Error Analysis + +Ignoring context (M-Error): This is another prevalent error type $(11\%)$ , which can also be attributed to the TE model. The model focuses too much on the candidate trigger itself while disregarding the context. Consider the sentence "He was instrumental in creating such shows as 'married with children'..." The word "married" is wrongly predicted as a MARRY event trigger. The TE model identifies it as an actual event rather than the name of a show. + +SRL coverage (U-Error): Among all errors, $3\%$ originate from the fact that the target trigger is not covered by SRL in the first place. This is a matter + +
SettingSystemTITI+TCAIAI+AC
PRFPRFPRFPRF
scratch (supervised)(Lin et al. 2020)--78.2--74.7--59.2--56.8
scratch (zero-shot)(Huang et al. 2018)21(Zhang et al. 2020)Ours85.758.934.741.257.866.355.658.345.575.554.631.736.353.560.649.154.041.728.219.820.227.338.940.427.826.327.016.19.412.615.618.525.215.812.516.8
gold TI (zero-shot)(Huang et al. 2018)(Zhang et al. 2020)Ours-----33.582.9-----14.7
-----83.735.143.738.921.827.224.2
gold TI+TC (zero-shot)(Liu et al. 2020)Ours---------25.524.426.031.425.827.4
+ +Table 5: The full performance on ACE-2005. + +
SettingSystemTITI+TCAIAI+AC
PRFPRFPRFPRF
scratch (supervised)(Lin et al. 2020)--68.4--57.0--50.1--46.5
scratch34.568.245.830.259.740.118.237.925.112.124.316.1
gold TI-----80.033.641.137.021.025.723.1
gold TI+TC (zero-shot)Ours------39.450.644.324.431.327.4
+ +Table 6: The full performance on ERE. + +of our usage of the TE model. Specifically, current SRL systems cannot handle nominal triggers perfectly, and cannot detect multi-word triggers like "step aside" or adjectival triggers like "dead" at all. + +Others: Other less-frequent error types besides those mentioned in the main text are related to coreference (e.g. when pronouns like "this" are triggers, ), proper names (e.g. historical events like "intifada"), confidence scores being too low (thus not identifying a gold trigger), ambiguity of the hypothesis (e.g. a "nuclear test" is predicted as a TRIAL-HEARING event because of the word "test" and the hypothesis "There is a trial or hearing"). + +# E.1.2 Ablation Study + +SRL models: To examine the influence of SRL coverage, we experiment with two more SRL models: Illinois SRL (Punyakanok et al., 2008) $^{22}$ , and one that identifies almost every verb and nominal $^{23}$ . None of the three can identify adjectival/multi-word predicates. In comparison, every model can cover over $90\%$ verb triggers, while the nominal trigger coverage varies from $60\%$ to $95\%$ . On T-Ext, the highest-coverage model performs the best (+4.0 F1 on TI, +6.8 on TC over the lowest-coverage model), proving that the gain from greedy identification does compensate for the cost in precision. + +Pretraining task: Our results show that the TE- + +based TC far outperforms its Yes/No QA counterpart (by $52.6\%$ ). One hypothesis is that the pretraining data for the TE model (MNLI; about 400K examples) is much larger than that for the QA model (BoolQ; about 9K). To verify that, we retrain a TE model on a portion of MNLI of the same size as BoolQ. As a result, the gap shrinks to $31.4\%$ , though still quite large. This proves the importance of the training data size. It also implies that in order to further improve the current TE-based method, using larger-scale training data might be promising. + +Hypothesis design: It is observed that the hypothesis format also plays a nontrivial role. As said in Appendix C.2, we experiment with two hypothesis designs, topical and natural. Experiments show that "topical" is better than "natural" by $1.9\%$ on TC, suggesting the sensitivity of current TE systems to the phrasing of texts. + +# E.2 Argument Extraction + +# E.2.1 Error Analysis + +Too broad argument type (M-Error/U-Error): For this error type $(9\%)$ , both the model and our usage are to blame. Though ACE has a strict definition of arguments, the QA model sometimes interprets them too broadly. For instance, with the context "A blindfolded woman was shot in the head by a hooded militant", given the question "Where is the shot", the model answers "in the head". This is not technically wrong, but certainly not the desired Place argument either. We cannot hold the QA model entirely accountable, since the questions + +are indeed too generic as well. + +Inexact span (U-Error): $15\%$ errors are because of the inexact match of gold and predicted argument spans. For instance, the gold is "Saturday morning" while the predicted is "morning". Though in our evaluation, we compare only heads of the phrases whenever possible, not all ACE arguments (i.e. those of the "value" type instead of the "entity" type) have head annotations. Under this circumstance, the current evaluation framework does not give credit to a partial match, which can be an imperfection for potential improvement. + +Insufficient context (U-Error): Like in trigger extraction, the model is sometimes given insufficient context when predicting arguments $(11\%)$ . The target argument can be entirely outside the SRL constituents of the predicate, thus making it impossible to extract. + +Coreference & Annotation ambiguity (T-Error): Error types ascribed to the task include "coreference" $(5\%)$ and "annotation ambiguity" $(17\%)$ . The former refers to the case when the model predicts a coreferent of the gold argument. However, the current evaluation framework still takes it as an error. The latter happens when the model makes a sensible prediction, yet it is inconsistent with the annotation. For example, in the sentence "Iraqi forces responded with artillery fire", the model recognizes "artillery" as the Instrument for the ATTACK event triggered by "fire". However, no Instrument is annotated. Future evaluation framework should consider allowing multiple correct answers in such cases of human disagreement. + +Others: Other errors are related to multiple arguments (i.e. the model only predicts one of them), lacking document-level knowledge (i.e. the sentence itself is not informative enough), and also arbitrary predictions with no obvious reason. + +# E.2.2 Ablation Study + +Pretraining data: Continuing from the "Pretraining data" paragraph in Section 5.2.2, we test three hypotheses for the gap between training on QAMR and SQuAD. + +Hypothesis(1): QAMR and ACE both have one-sentence contexts, while SQuAD has paragraphs. + +We try to verify it by retraining a QA model on a new version of QAMR with longer contexts, subject to the same length distribution of SQuAD. This is done by either a) adding random sentences, or b) repeating the original sentence. It is observed that + +a) almost doesn't hurt AI at all but AC a little $(3\%)$ , and b) lowers AI by $4\%$ and AC by $3\%$ . Therefore, though longer contexts do weaken the performance slightly, it is not the main reason behind the gap between QAMR and SQUAD. + +Hypothesis(2): The NA questions in SQuAD "confuses" the model, i.e. SQuAD and ACE have similar types of HA questions, while different types of NA questions. + +To test this hypothesis, we retain all HA questions in SQuAD to make a new dataset. We also construct a control set of the same size, but with both NA and HA questions randomly sampled from the original SQuAD. We retrain a QA model on each dataset, and find that the HA-only set brings about an increment by $7\%$ on AI but a drop by $2\%$ on AC, compared to the control set. This suggests that the addition of NA questions in SQuAD does have mixed effects on event extraction. Future research should focus on how to better transfer a model's ability to identify NA questions to a different domain. + +Hypothesis (3): The density of answers per sentence is high in both QAMR and ACE, while low in SQuAD. + +To see if this is the cause, we construct a new version of QAMR by retaining only one QA pair for each sentence. A control set of the same size, but with multiple QA pairs per sentence, is also constructed by randomly deleting sentences (along with all their QA pairs) from the original QAMR. Results show that the low-density set is only worse than the control set on AI by $0.5\%$ and on AC by $0.2\%$ , indicating that the density of answers is not a critical aspect. + +Type constraints in question: Since generic questions may have been a cause for too broad argument types, we experiment with a new set of question templates that contain specific entity-type requirements whenever possible. For example, instead of "Where is the shot", we ask "What is the location of the shot", which may prevent the model from answering "in the head". However, only $11\%$ errors are fixed after re-prediction, indicating that encoding type constraints is non-superficial. + +Question design: Like the hypothesis format in trigger extraction, the design of questions also makes a difference for arguments. As mentioned in Appendix C.3, we explore two formats, static and Contextualized. Experiments show that switching from "static" to "contextualized" boosts AI by $7\%$ + +while impairs AC by $3\%$ , suggesting that contextualized questions overall helps the model better locate the event. + +Context design: To measure the influence of insufficient context, we now use the entire sentence as the context on these instances, similar to trigger extraction. Results show that $27\%$ of them are now correct, and another $27\%$ are partially correct (inexact span). \ No newline at end of file diff --git a/zeroshoteventextractionviatransferlearningchallengesandinsights/images.zip b/zeroshoteventextractionviatransferlearningchallengesandinsights/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..610f8e794ec985452cde43a2cb3e2c3cc1d97cfd --- /dev/null +++ b/zeroshoteventextractionviatransferlearningchallengesandinsights/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:acb51722b7ca0c5ebebd15ac638f57e301dc34267d953f8c529f4871177dd909 +size 251246 diff --git a/zeroshoteventextractionviatransferlearningchallengesandinsights/layout.json b/zeroshoteventextractionviatransferlearningchallengesandinsights/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..ed2c715e61ecbbedc7e5e81fc2f7bad9630a2a94 --- /dev/null +++ b/zeroshoteventextractionviatransferlearningchallengesandinsights/layout.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:534cd90678d9257572fd3c2449f6c7d7aed6bc312ae635bc161f5cc7820e2cf3 +size 374527 diff --git a/zeroshotfactverificationbyclaimgeneration/327bc246-ed22-425b-8ace-6c77bf5a0037_content_list.json b/zeroshotfactverificationbyclaimgeneration/327bc246-ed22-425b-8ace-6c77bf5a0037_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..a67dc354ab3df619c3e1e2d20a2a9d8bebf88478 --- /dev/null +++ b/zeroshotfactverificationbyclaimgeneration/327bc246-ed22-425b-8ace-6c77bf5a0037_content_list.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:51c296c37ed5cd9c2c1fd4f5cb92c06f7e1ebea21d0dba7d33cdc36ddd4ce2cc +size 56760 diff --git a/zeroshotfactverificationbyclaimgeneration/327bc246-ed22-425b-8ace-6c77bf5a0037_model.json b/zeroshotfactverificationbyclaimgeneration/327bc246-ed22-425b-8ace-6c77bf5a0037_model.json new file mode 100644 index 0000000000000000000000000000000000000000..ab1a62d2f88450c56908c65948956209c41d5a95 --- /dev/null +++ b/zeroshotfactverificationbyclaimgeneration/327bc246-ed22-425b-8ace-6c77bf5a0037_model.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:82bb30c8258ba9d7d3a0f5bd1d61c6cf190b0d2f492c938bb39a167d7a29859c +size 67830 diff --git a/zeroshotfactverificationbyclaimgeneration/327bc246-ed22-425b-8ace-6c77bf5a0037_origin.pdf b/zeroshotfactverificationbyclaimgeneration/327bc246-ed22-425b-8ace-6c77bf5a0037_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..eec8ccb51790e9c7f58aaf5ca1c79deada131b3b --- /dev/null +++ b/zeroshotfactverificationbyclaimgeneration/327bc246-ed22-425b-8ace-6c77bf5a0037_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1922f2815edfc63136f7990739e2b05612f3bd188b306cb39421688281574574 +size 797693 diff --git a/zeroshotfactverificationbyclaimgeneration/full.md b/zeroshotfactverificationbyclaimgeneration/full.md new file mode 100644 index 0000000000000000000000000000000000000000..06e5531336e86a5a81e7102111b0a6c378ac6090 --- /dev/null +++ b/zeroshotfactverificationbyclaimgeneration/full.md @@ -0,0 +1,228 @@ +# Zero-shot Fact Verification by Claim Generation + +Liangming Pan1,2 Wenhu Chen3 Wenhan Xiong3 Min-Yen Kan2 William Yang Wang3 + +$^{1}$ NUS Graduate School for Integrative Sciences and Engineering + $^{2}$ School of Computing, National University of Singapore, Singapore + $^{3}$ University of California, Santa Barbara, CA, USA +liangmingpan@u.nus.edu +{wenhuchen, xwhan, william}@cs.ucsb.edu +kanmy@comp.nus.edu.sg + +# Abstract + +Neural models for automated fact verification have achieved promising results thanks to the availability of large, human-annotated datasets. However, for each new domain that requires fact verification, creating a dataset by manually writing claims and linking them to their supporting evidence is expensive. We develop QACG, a framework for training a robust fact verification model by using automatically-generated claims that can be supported, refuted, or unverifiable from evidence from Wikipedia. QACG generates question-answer pairs from the evidence and then convert them into different types of claims. Experiments on the FEVER dataset show that our QACG framework significantly reduces the demand for human-annotated training data. In a zero-shot scenario, QACG improves a RoBERTa model's $F_{1}$ from $50\%$ to $77\%$ , equivalent in performance to $2\mathrm{K}+$ manually-curated examples. Our QACG code is publicly available. $^{1}$ + +# 1 Introduction + +Fact verification aims to validate a claim in the context of evidence. This task has attracted growing interest with the rise in disinformation in news and social media. Rapid progress has been made by training large neural models (Zhou et al., 2019; Liu et al., 2020b; Zhong et al., 2020) on the FEVER dataset (Thorne et al., 2018), containing more than 100K human-crafted (evidence, claim) pairs based on Wikipedia. + +Fact verification is demanded in many domains, including news articles, social media, and scientific documents. However, it is not realistic to assume that large-scale training data is available for every new domain that requires fact verification. Creating training data by asking humans to write claims and + +search for evidence to support/refute them can be extremely costly. + +We address this problem by exploring the possibility of automatically generating large-scale (evidence, claim) pairs to train the fact verification model. We propose a simple yet general framework Question Answering for Claim Generation (QACG) to generate three types of claims from any given evidence: 1) claims that are supported by the evidence, 2) claims that are refuted by the evidence, and 3) claims that the evidence does Not have Enough Information (NEI) to verify. + +To generate claims, we utilize Question Generation (QG) (Zhao et al., 2018; Liu et al., 2020a; Pan et al., 2020), which aims to automatically ask questions from textual inputs. QG has been shown to benefit various NLP tasks, such as enriching QA corpora (Alberti et al., 2019), checking factual consistency for summarization (Wang et al., 2020), and data augmentation for semantic parsing (Guo et al., 2018). To the best of our knowledge, we are the first to employ QG for fact verification. + +As illustrated in Figure 1, given a passage $P$ as the evidence, we first employ a Question Generator to generate a question-answer pair $(Q, A)$ for the evidence. We then convert $(Q, A)$ into a claim $C$ (QA-to-Claim) based on the following logical assumptions: a) if $P$ can answer $Q$ and $A$ is the correct answer, then $C$ is a supported claim; b) if $P$ can answer $Q$ but $A$ is an incorrect answer, then $C$ is a refuted claim; c) if $P$ cannot answer $Q$ , then $C$ is a NEI claim. The Question Generator and the QA-to-Claim model are off-the-shelf BART models (Lewis et al., 2020), finetuned on SQuAD (Rajpurkar et al., 2016) and QA2D (Demszky et al., 2018) datasets. + +We generate 100K (evidence, claim) pairs for each type of claim, which we then use to train a RoBERTa (Liu et al., 2019) model for fact verification. We evaluate the model on three test sets + +![](images/694f198719d9b2b914997e21e78f74be52b90908d588d42b7b9ca3e23fe76ff4.jpg) +Figure 1: Overview of our QACG framework, consisting of two modules: 1) Question Generator generates questions from the evidence $\mathcal{P}$ and the extra contexts $\mathcal{P}_{ext}$ given different answers extracted from the passage (in green), and 2) QA-to-Claim converts question-answer pairs into claims with different labels. + +based on the FEVER dataset. Although we do not use any human-labeled training examples, the model achieves over $70\%$ of the $F_{1}$ performance of a fully-supervised setting. By finetuning the model with only 100 labeled examples, we further close the performance gap, achieving $89.1\%$ of fully-supervised performance. The above results show that pretraining the fact verification model with generated claims greatly reduces the demand for in-domain human annotation. When evaluating the model on an unbiased test set for FEVER, we find that training with generated claims also produces a more robust fact verification model. + +In summary, our contributions are: + +- To the best of our knowledge, this is the first work to investigate zero-shot fact verification. +- We propose QACG, a novel framework to generate high-quality claims via question generation. +- We show that the generated training data can greatly benefit the fact verification system in both zero-shot and few-shot learning settings. + +# 2 Methodology + +Given a claim $\mathcal{C}$ and a piece of evidence $\mathcal{P}$ as inputs, a fact verification model $\mathcal{F}$ predicts a label $\mathcal{Y} \in \{\text{supported, refuted, NEI}\}$ to verify whether $\mathcal{C}$ is supported, refuted, or can not be verified by the information in $\mathcal{P}$ . + +For the zero-shot setting, we assume no human-annotated training example is available. Instead, we generate a synthetic training set based on our QACG framework to train the model. + +# 2.1 Question Generator and QA-to-Claim + +As illustrated in Figure 1, our claim generation model QACG has two major components: a Question Generator $\mathcal{G}$ , and a QA-to-Claim model $\mathcal{M}$ . + +The Question Generator takes as input an evidence $\mathcal{P}$ and a text span $A$ from the given evidence and aims to generate a question $Q$ with $A$ as the answer. We implement this with the BART model (Lewis et al., 2020), a large transformer-based sequence-to-sequence model pretrained on 160GB of text. The model is finetuned on the SQuAD dataset processed by Zhou et al. (2017), where the model encodes the concatenation of the SQuAD passage and the answer text and then learns to decode the question. We evaluate the question generator using automatic and human evaluation and investigate its impact on fact verification in Appendix A. + +The QA-to-Claim Model takes as inputs $Q$ and $A$ , and outputs the declarative sentence $C$ for the $(Q, A)$ pair, as shown in Figure 1. We also treat this as a sequence-to-sequence problem and finetune the BART (Lewis et al., 2020) model on the QA2D dataset (Demszky et al., 2018), which contains the human-annotated declarative sentence for each $(Q, A)$ pair in SQuAD. + +# 2.2 Claim Generation + +Given the pretrained question generator $\mathcal{G}$ and the QA-to-Claim model $\mathcal{M}$ , we then formally introduce how we generate claims with different labels. + +Supported claim generation. Given an evidence $P$ , we use named entity recognition to identify all entities within $P$ , denoted as $\mathcal{E}$ . For each + +entity $a \in \mathcal{E}$ , we treat each $a$ in turn as an answer and generate a question $q = \mathcal{G}(\mathcal{P}, a)$ with the question generator. The question-answer pair $(q, a)$ are then sent to the QA-to-Claim model to generate the supported claim $c = \mathcal{M}(q, a)$ . + +Refuted claim generation. To generate a refuted claim, after we generate the question-answer pair $(q,a)$ , we use answer replacement (shown in Figure 1) to replace the answer $a$ with another entity $a^{\prime}$ with the same type such that $a^\prime$ becomes an incorrect answer to the question $q$ . Using $a$ as the query, we randomly sample a phrase from the top-5 most similar phrases in the pretrained Sense2Vec (Trask et al., 2015) as the replacing answer $a^\prime$ . The new pair $(q,a^{\prime})$ is then fed to the QA-to-Claim model to generate the refuted claim. + +To avoid the case that $a'$ is still the correct answer, we define rules to ensure that the $a'$ has less lexical overlap with $a$ . However, this problem is sometimes non-trivial and cannot be completely avoided. For example, for the QA pair: ("Who is the producer of Avatar?"; "James Cameron"), another valid answer $a'$ is "Jon Landau", who happens to be another producer of Avatar. However, we observe that such coincidences rarely happen: among the 100 randomly sampled claims, we only observed 2 such cases. Therefore, we leave them as the natural noise of the generation model. + +NEI claim generation. We need to generate a question $q'$ which is relevant but cannot be answered by $\mathcal{P}$ . To this end, we link $\mathcal{P}$ back to its original Wikipedia article $\mathcal{W}$ and expand the evidence with additional contexts $\mathcal{P}_{ext}$ , which are five randomly-retrieved sentences from $\mathcal{W}$ that are not present in $\mathcal{P}$ . In our example in Figure 1, one additional context retrieved is "By the time the pilots ended, 63 people had been killed". We then concatenate $\mathcal{P}$ and $\mathcal{P}_{ext}$ as the expanded evidence, based on which we generate a supported claim given an entity in $\mathcal{P}_{ext}$ as the answer (e.g., "63"). This results in a claim relevant to but unverifiable by the original evidence $\mathcal{P}$ . + +# 3 Experiments + +By applying our QACG model to each of the 18,541 Wikipedia articles in the FEVER training set, we generate a total number of 176,370 supported claims, 360,924 refuted claims, and 258,452 NEI claims. Our generated data is around five times the size of the human-annotated + +claims in FEVER. We name this generated dataset as QACG-Full. We then create a balanced dataset QACG-Filtered by randomly sampling 100,000 samples for each class. Statistics of the FEVER and the generated dataset are in Appendix B. + +Evaluation Datasets. We evaluate fact verification on three different test sets based on FEVER: 1) FEVER-S/R: Since only the supported and refuted claims are labeled with gold evidence in FEVER, we take the claim-evidence pairs of these two classes from the FEVER test set for evaluation. 2) FEVER-Symmetric: this is a carefully-designed unbiased test set designed by Schuster et al. (2019) to detect the robustness of the fact verification model. Note that only supported and refuted claims are present in this test set. 3) FEVER-S/R/N: The full FEVER test set are used for a three-class verification. We follow Atanasova et al. (2020) to use the system of Malon (2019) to retrieve evidence sentences for NEI claims. + +Fact Verification Models. As shown in Table 1, we take a BERT model (S1) and a RoBERTa model (S2) fine-tuned on the FEVER training set as the supervised models. Their corresponding zero-shot settings are Rows U5 and U6, where the models are trained on our generated QACG-Filtered dataset. Note that for binary classification (FEVER-S/R and FEVER-Symmetric), only the supported and refuted claims are used for training, while for FEVER-S/R/N, the full training set is used. + +We employ four baselines that also do not need any human-annotated claims to compare with our method. Random Guess (U1) is a weak baseline that randomly predicts the class label. GPT2 Perplexity (U2) predicts the class label based on the perplexity of the claim under a pretrained GPT2 (Radford et al., 2019) language model, following the assumption that "misinformation has high perplexity" (Lee et al., 2020a). MNLI-Transfer (U3) trains a BERT model for natural language inference on the MultiNLI corpus (Williams et al., 2018) and applies it for fact verification. LM as Fact Checker (Lee et al., 2020b) (U4) leverages the implicit knowledge stored in the pretrained BERT language model to verify a claim. The implementation details are given in Appendix C. + +# 3.1 Main Results + +Table 1 summarizes the fact verification performance, measured by the macro Precision $(P)$ , Recall $(R)$ , and F1 Score $(F_1)$ . + +
ModelFEVER-SymmetricFEVER-S/RFEVER-S/R/N
P / R / F1P / R / F1P / R / F1
SupervisedS1. BERT-base (Devlin et al., 2019)81.5 / 81.3 / 81.292.8 / 92.6 / 92.685.7 / 85.6 / 85.6
S2. RoBERTa-large (Liu et al., 2019)85.5 / 85.5 / 85.595.2 / 95.1 / 95.188.0 / 87.9 / 87.8
Zero-shotU1. Random Guess50.0 / 50.0 / 50.050.0 / 50.0 / 50.033.3 / 33.3 / 33.3
U2. GPT2 Perplexity52.7 / 52.7 / 52.755.6 / 55.6 / 55.635.3 / 35.3 / 35.3
U3. MNLI-Transfer62.2 / 55.5 / 58.763.6 / 60.5 / 61.841.4 / 39.6 / 40.7
U4. LM as Fact Checker (Lee et al., 2020b)71.2 / 64.5 / 67.877.9 / 65.6 / 70.264.3 / 54.6 / 49.8
U5. QACG (BERT-base)73.2 / 73.0 / 72.974.2 / 74.0 / 74.156.5 / 55.7 / 55.9
U6. QACG (RoBERTa-large)77.3 / 77.0 / 77.178.1 / 78.1 / 78.164.6 / 62.0 / 62.6
+ +Table 1: Fact verification performance for supervised models and zero-shot models on three different settings. + +Comparison with supervised settings. The zero-shot setting with RoBERTa-large (U6) attains $78.1F_{1}$ on the FEVER-S/R and $62.6F_{1}$ on the FEVER-S/R/N. The $F_{1}$ gap to the fully-supervised RoBERTa-large (S2) is only 17.0 and 15.2 on these two settings, respectively. These results demonstrate the effectiveness of QACG in generating good (evidence, claim) pairs for training the fact verification model. The RoBERTa model (S2, U6) is more effective than the BERT model (S1, U5) for both the zero-shot and the supervised setting. + +Comparison with zero-shot baselines. Our model (U6) achieves the best results among all the zero-shot baselines across all three test sets. We find that validating a claim by its perplexity (U2) only works slightly better than random guess (U1) $(+3.43F_{1})$ , showing that misinformation does not necessary to have high perplexity. Although natural language inference seems highly correlated with fact verification, directly transferring the model trained on the MNLI dataset (U3) only outperforms random guess by $9.30F_{1}$ . We believe this is due to the domain gap between FEVER (from Wikipedia) and the MNLI (from fiction, letters, etc.) dataset. As a generation framework, our model can avoid the domain gap issue by generating pseudo training data from the same domain (Wikipedia). Another reason is the "task gap" between NLI and fact verification, in which the former makes inference about the situation described in a sentence, while the latter focuses on claims about entities in Wikipedia. + +Model Robustness. We observe a large performance drop when the supervised model is evaluated on the FEVER-Symmetric test set for both the BERT model $(-11.4F_{1})$ and the RoBERTa model $(-9.6F_{1})$ . However, the models trained with our generated data (U2, U3) drop only 1.2 and $1.0F_{1}$ drop. This suggests that the wide range of different claims we generate as training data helps eliminate + +![](images/61293641bae5262e3802f5858ccec3a6c13c7db5ed3f37ad9ed021af417bb431.jpg) +Figure 2: The few-shot learning experiment. The figure shows the $F_{1}$ score on FEVER-Symmetric for progressively larger training dataset sizes. + +some of the annotation artifacts present in FEVER, leading to a more robust fact verification model. + +# 3.2 Few-shot Fact Verification + +We then explore QACG's effectiveness in the few-shot learning setting where only a few human-labeled (evidence, claim) pairs are available. We first train the RoBERT-large fact verification model with our generated dataset QACG-Filtered. Then we fine-tune the model with a limited amount of human-labeled claims in FEVER. The blue solid line in Figure 2 shows the $F_{1}$ scores on FEVER-Symmetric after finetuning with different numbers of labeled training data. We compare this with training the model from scratch with the human-labeled data (grey dashed line). + +Our model performs consistently better than the model without pretraining, regardless of the amount of labeled training data. The improvement is especially prominent in data-poor regimes; for example, our approach achieves $78.6F_{1}$ with only 50 labeled claims for each class, compared with $52.9F_{1}$ without pretraining $(+25.7)$ . This only leaves a $7.9F_{1}$ gap to the fully-supervised setting $(86.5F_{1})$ with over 100K training samples. The results show pretraining fact verification with QACG + +
EvidenceGenerated Claim
Budapest is cited as one of the most beautiful cities in Europe, ranked as the most liveable Central and Eastern European city on EIU's quality of life index, ranked as “the world's second best city” by Conde Nast Traveler, and “Europe's 7th most idyllic place to live” by Forbes.SUPPORTED claims +Budapest is ranked as the most liveable city in central Europe. +Budapest ranks 7th in terms of idyllic places to live in Europe.
REFUTED claims +Budapest ranks in 11th in terms of idyllic places to live in Europe. +Budapest is ranked the most liveable city in Asia.
NEI claims +Budapest is one of the largest cities in the European Union. +Budapest is the capital of Hungary.
Alia Bhatt received critical acclaim for portraying emotionally intense characters in the road drama Highway (2014), which won her the Filmfare Critics Award for Best Actress, and the crime drama Udta Punjab (2016), which won her the Filmfare Award for Best Actress.SUPPORTED claims +Bhatt won the Filmfare Award for Best Actress in Udta Punjab. +Bhatt received the Filmfare Critics Award for her role in Highway.
REFUTED claims +Alia Bhatt won the Best Original Screenplay award in Highway. +2 States (2014) won Alia Bhatt the Filmfare Award for Best Actress.
NEI claims +Alia Bhatt made her acting debut in the 1999 thriller Sangharsh. +Bhatt played her first leading role in Karan Johar's romantic drama.
+ +Table 2: Examples of evidence and claims generated by QACG, categorized by class labels. In the evidence, the identified answers for question generation are highlighted in blue. For claims, the correct answers are highlighted in blue for SUPPORTED claims and the replaced wrong answers are in red for REFUTED claims. + +
Evidence:Roman Atwood is best known for his vlogs, where he posts updates about his life.
Claim:Roman Atwood is a content creator.
Evidence:In 2004, Slovenia entered NATO and the European Union.
Claim:Slovenia uses the euro.
Evidence:He has traveled to Chad and Uganda to raise awareness about conflicts in the regions.
Claim:Ryan Gosling has been to a country in Africa.
+ +Table 3: Examples of claims in FEVER that require commonsense or world knowledge (underlined). + +greatly reduces the demand for in-domain human-annotated data. Our method can provide a "warm start" for fact verification system when applied to a new domain where training data are limited. + +# 3.3 Analysis of Generated Claims + +Table 2 shows representative claims generated by our model. The claims are fluent, label-cohesive, and exhibit encouraging language variety. However, one limitation is that our generated claims are mostly lack of deep reasoning over the evidence. This is because we finetune the question generator on the SQuAD dataset, in which more than $80\%$ of its questions are shallow factoid questions. + +To better understand whether this limitation brings a domain gap between the generated claims and the human-written claims, we randomly sampled 100 supported claims and 100 refuted and analyze whether reasoning is involved to verify those claims. We find that $38\%$ of the supported + +claims and $16\%$ of the refuted claims in FEVER require either commonsense reasoning or world knowledge to verify. Table 3 show three typical examples. Therefore, we believe this domain gap is the main bottleneck of our system. Future studies are required to generate more complex claims which involves multi-hop, numerical, and commonsense reasoning, such that we can apply our model to more complex fact checking scenario. + +# 4 Conclusion and Future Work + +We utilize the question generation model to ask different questions for given evidence and convert question-answer pairs into claims with different labels. We show that the generated claims can train a well-performing fact verification model in both the zero-shot and the few-shot learning setting. Potential future directions could be: 1) generating more complex claims that require deep reasoning; 2) extending our framework to other fact checking domains beyond Wikipedia, e.g., news, social media; 3) leveraging generated claims to improve the robustness of fact checking systems. + +# Acknowledgments + +This research is supported by the National Research Foundation, Singapore under its International Research Centres in Singapore Funding Initiative. The UCSB authors are not supported by any of the projects above. They thank Google, Amazon, Facebook, and JP Morgan for their generous support. + +# Ethical Considerations + +We discuss two potential issues of claim generation, showing how our work sidesteps these issues. While individuals may express harmful or biased claims, our work only focuses on generating factoid claims from a corpus. In this work, we take Wikipedia as the source for objective fact. Practicing this technique thus requires the identification of an appropriate source of objective truth to generate claims from. Another potential misuse of claim generation is to generate refuted claims and subsequently spread such misinformation. We caution practitioners to treat the generated claims with care. In our case, we use the generated claims only to optimize for the downstream fact verification task. We advise against releasing generated claims for public use — especially on public websites, where they may be crawled and then subsequently used for inference. As such, we will release the model code but not the output in our work. Practitioners can re-run the training pipeline to replicate experiments accordingly. + +# References + +Chris Alberti, Daniel Andor, Emily Pitler, Jacob Devlin, and Michael Collins. 2019. Synthetic QA corpora generation with roundtrip consistency. In Annual Meeting of the Association for Computational Linguistics (ACL), pages 6168-6173. +Pepa Atanasova, Dustin Wright, and Isabelle Augenstein. 2020. Generating label cohesive and well-formed adversarial claims. In Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 3168-3177. +Dorottya Demszky, Kelvin Guu, and Percy Liang. 2018. Transforming question answering datasets into natural language inference datasets. CoRR, abs/1809.02922. +Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: pre-training of deep bidirectional transformers for language understanding. In Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT), pages 4171-4186. +Li Dong, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu, Yu Wang, Jianfeng Gao, Ming Zhou, and Hsiao-Wuen Hon. 2019. Unified language model pre-training for natural language understanding and generation. In Annual Conference on Neural Information Processing Systems (NeurIPS), pages 13042-13054. + +Han Guo, Ramakanth Pasunuru, and Mohit Bansal. 2018. Soft layer-specific multi-task summarization with entailment and question generation. In Annual Meeting of the Association for Computational Linguistics (ACL), pages 687-697. +Alon Lavie and Abhaya Agarwal. 2007. METEOR: an automatic metric for MT evaluation with high levels of correlation with human judgments. In Proceedings of the Second Workshop on Statistical Machine Translation (WMT@ACL), pages 228-231. +Nayeon Lee, Yejin Bang, Andrea Madotto, and Pascale Fung. 2020a. Misinformation has high perplexity. CoRR, abs/2006.04666. +Nayeon Lee, Belinda Z. Li, Sinong Wang, Wen-tau Yih, Hao Ma, and Madian Khabsa. 2020b. Language models as fact checkers? CoRR, abs/2006.04102. +Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020. BART: denoising sequence-to-sequence pretraining for natural language generation, translation, and comprehension. In Annual Meeting of the Association for Computational Linguistics (ACL), pages 7871-7880. +Chin-Yew Lin. 2004. Rouge: A package for automatic evaluation of summaries. Text Summarization Branches Out. +Bang Liu, Haojie Wei, Di Niu, Haolan Chen, and Yancheng He. 2020a. Asking questions the human way: Scalable question-answer generation from text corpus. In International World Wide Web Conference (WWW). +Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized BERT pretraining approach. CoRR, abs/1907.11692. +Zhenghao Liu, Chenyan Xiong, Maosong Sun, and Zhiyuan Liu. 2020b. Fine-grained fact verification with kernel graph attention network. In Annual Meeting of the Association for Computational Linguistics (ACL), pages 7342-7351. +Christopher Malon. 2019. Team papelo: Transformer networks at FEVER. CoRR, abs/1901.02534. +Liangming Pan, Yuxi Xie, Yansong Feng, Tat-Seng Chua, and Min-Yen Kan. 2020. Semantic graphs for generating deep questions. In Annual Meeting of the Association for Computational Linguistics (ACL), pages 1463-1475. +Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. Bleu: a method for automatic evaluation of machine translation. In Annual Meeting of the Association for Computational Linguistics (ACL), pages 311-318. + +Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. Language models are unsupervised multitask learners. OpenAI blog, 1(8):9. +Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016. Squad: 100, $000+$ questions for machine comprehension of text. In *Conference on Empirical Methods in Natural Language Processing (EMNLP)*, pages 2383-2392. +Tal Schuster, Darsh J. Shah, Yun Jie Serene Yeo, Daniel Filizzola, Enrico Santus, and Regina Barzilay. 2019. Towards debiasing fact verification models. In *Conference on Empirical Methods in Natural Language Processing (EMNLP)*, pages 3417-3423. +James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal. 2018. FEVER: a large-scale dataset for fact extraction and verification. In Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT), pages 809-819. +Andrew Trask, Phil Michalak, and John Liu. 2015. sense2vec - A fast and accurate method for word sense disambiguation in neural word embeddings. CoRR, abs/1511.06388. +Alex Wang, Kyunghyun Cho, and Mike Lewis. 2020. Asking and answering questions to evaluate the factual consistency of summaries. In Annual Meeting of the Association for Computational Linguistics (ACL), pages 5008-5020. +Adina Williams, Nikita Nangia, and Samuel R. Bowman. 2018. A broad-coverage challenge corpus for sentence understanding through inference. In Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT), pages 1112-1122. +Yao Zhao, Xiaochuan Ni, Yuanyuan Ding, and Qifa Ke. 2018. Paragraph-level neural question generation with maxout pointer and gated self-attention networks. In Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 3901-3910. +Wanjun Zhong, Jingjing Xu, Duyu Tang, Zenan Xu, Nan Duan, Ming Zhou, Jiahai Wang, and Jian Yin. 2020. Reasoning over semantic-level graph for fact checking. In Annual Meeting of the Association for Computational Linguistics (ACL), pages 6170-6180. +Jie Zhou, Xu Han, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun. 2019. GEAR: graph-based evidence aggregating and reasoning for fact verification. In Annual Meeting of the Association for Computational Linguistics (ACL), pages 892-901. + +Qingyu Zhou, Nan Yang, Furu Wei, Chuanqi Tan, Hangbo Bao, and Ming Zhou. 2017. Neural question generation from text: A preliminary study. In CCF International Conference of Natural Language Processing and Chinese Computing (NLPCC), pages 662-671. + +# A Evaluation of Question Generation + +To implement the question generator, we finetune the pretrained BART model provided by Hugging-Face library on the SQuAD dataset. The codes are based on the SimpleTransformers $^2$ library. The success of our QACG framework heavily relies on whether we can generate fluent and answerable questions given the evidence. Therefore, we separately evaluate the question generator using both automatic and human evaluation and investigate its impact to zero-shot fact verification. + +# A.1 Automatic Evaluation + +We employ BLEU-4 (Papineni et al., 2002), METEOR (Lavie and Agarwal, 2007), and ROUGE-L (Lin, 2004) to evaluate the performance of our implementation. We compare the BART model with several state-of-the-art QG models, using their reported performance on the Zhou split of SQuAD. + +Table 4 shows the evaluation results comparing against all baseline methods. The BART model achieves a BLEU-4 of 21.32, outperforming NQG++, S2ga-mp-gsa, and CGC-QG by large margins. This is as expected since these three baselines are based on Seq2Seq and do not apply language model pretraining. Compared with the current state-of-the-art model UniLM, the BART model achieves comparable results, with slightly lower BLEU-4 but higher METEOR. + +
ModelB4MRRL
NQG++ (Zhou et al., 2017)13.518.241.6
S2ga-mp-gsa (Zhao et al., 2018)15.819.744.2
CGC-QG (Liu et al., 2020a)17.621.244.5
UniLM (Dong et al., 2019)23.825.652.0
BART (Lewis et al., 2020)21.327.143.6
+ +Table 4: Performance evaluation of the Question Generator with different model implementations. We adopt the BART model in our QACG framework. B4: BLEU-4, MR: METEOR, $R_L$ : ROUGE-L. + +# A.2 Impact of Answerability + +Given the evidence $P$ and the answer $A$ , the generated question $Q$ must be answerable by $P$ and + +
ModelAnswerable RateFV Performance P / R / F1
NQG++63.0%62.2 / 62.4 / 62.3
BART89.5%76.3 / 76.0 / 76.1
+ +take $A$ as its correct answer. This is the premise of generating a correct SUPPORTED claim. Therefore, we specially evaluate this answerability property via human ratings. We randomly sample 100 generated question-answer pairs with their corresponding evidence and ask two workers to judge the answerability of each sample. We do this for both the NQG++ model and the BART model. To investigate the impact of question quality on the fact verification performance, we separately use the NQG++ and BART as the question generator to generate claims and train the RoBERTa model. The performance is summarized in Table 5. + +We find that the ratio of answerable questions generated by the BART model is $89.5\%$ , significantly outperforms the $63.5\%$ achieved by the NQG++ model. When switching the question generator to NQG++, the fact verification $F_{1}$ drops to 62.3 $(-22.1\%)$ compared with BART. This shows that answerability plays an important role in ensuring the validity of the generated claims and has a huge impact on the fact verification performance. + +# B Dataset Statistics + +Table 6 shows the basic data statistics of the FEVER, FEVER-Symmetric, and our generated dataset by QACG. We use the balanced dataset QACG-Filtered sampled from QACG-Full to train the fact verification model in the zero/few-shot setting. Compared with the original FEVER dataset, our generated QACG-Filtered dataset has a balanced number of claims for each class. Moreover, because QACG can generate three different types of claims for the same given evidence (shown in Figure 1), it results in a more "unbiased" dataset in which the model must rely on the (evidence, claim) pair rather than the evidence itself to make an inference of the class label. + +# C Model Implementation Details + +BERT-base and RoBERTa-large (S1, S2, U5, U6). We use the bert-base-uncased + +Table 5: Answerable Rate: the ratio of answerable questions generated by the NQG++ and the BART model. FV Performance: the zero-shot fact verification performance on the FEVER-Symmetric. + +
DatasetSupportedRefutedNEI
FEVERTrain80,03529,77535,517
Test6,6666,6666,666
FEVER-Symmetric710710-
QACGFull176,370360,924258,452
Filtered100,000100,000100,000
+ +Table 6: Basic statistics of the FEVER dataset and the dataset generated by QACG. + +(110M parameters) and the roberta-large (355M parameters) model provided by Hugging-Face library to implement the BERT model and the RoBERTa model, respectively. The model is fine-tuned with a batch size of 16, learning rate of 1e-5 and for a total of 5 epochs, where the epoch with the best performance is saved. + +GPT2 Perplexity (U2). To measure the perplexity, we use the HuggingFace implementation of the medium GPT-2 model (gpt2-medium, 345M parameters). We then rank the claims in the FEVER test set by their perplexity under the GPT-2 model. We then predict the label for each claim based on the assumption that misinformation has high perplexity. However, manually setting the perplexity threshold is difficult. Since the FEVER test set contains an equal number of claims for each class, we predict the claims in the top 1/3 of the ranking list as refuted, and the bottom 1/3 as supported. The rest claims are set as NEI. Therefore, the number of predicted labels for each class is also equal. + +MNLI-Transfer (U3). We use the HuggingFace - BERT base model (110M parameters) fine tuned on the Multi-Genre Natural Language Inference (MNLI) corpus3, a crowd-sourced collection of 433K sentence pairs annotated with textual entailment information. We then directly apply this model for fact verification in the FEVER test set. The class label entailment, contradiction, and neutral in the NLI task is mapped to supported, refuted, and NEI, respectively, for the fact verification task. + +LM as Fact Checker (U4). Since there is no public available code for this model, we implement our own version following the settings described in Lee et al. (2020b). We use Hugging-Face's bert-base as the language model to predict the masked named entity, and use the NLI model described in U3 as the entailment model. \ No newline at end of file diff --git a/zeroshotfactverificationbyclaimgeneration/images.zip b/zeroshotfactverificationbyclaimgeneration/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..df992cc9364d5a7839b9efcda6ff1cd7ad867237 --- /dev/null +++ b/zeroshotfactverificationbyclaimgeneration/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6b6624ce46c64f5631d27cf4c111a53aa587136e9b07c0a43e0e70703e727810 +size 436060 diff --git a/zeroshotfactverificationbyclaimgeneration/layout.json b/zeroshotfactverificationbyclaimgeneration/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..b79f23de35d6d4d781ef33c8b308324ad0c088e5 --- /dev/null +++ b/zeroshotfactverificationbyclaimgeneration/layout.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1c53e1bf99cb4f768ed8f7381c3100425223b3e927fe0e14cf94a964451c9b32 +size 301416