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Towards Generating Long and Coherent Text with Multi-Level Latent Variable Models | 1902.00154 | Table 5: Evaluation of diversity of 1000 generated sentences on self-BLEU scores (B-n), unique n-gram percentages (ngr), 2-gram entropy score. | ['[BOLD] Model', '[BOLD] Yelp [BOLD] B-2', '[BOLD] Yelp [BOLD] B-3', '[BOLD] Yelp [BOLD] B-4', '[BOLD] Yelp [BOLD] 2gr', '[BOLD] Yelp [BOLD] 3gr', '[BOLD] Yelp [BOLD] 4gr', '[BOLD] Yelp [BOLD] Etp-2'] | [['ARAE', '0.725', '0.544', '0.402', '36.2', '59.7', '75.8', '7.551'], ['AAE', '0.831', '0.672', '0.483', '33.2', '57.5', '71.4', '6.767'], ['[ITALIC] flat-VAE', '0.872', '0.755', '0.617', '23.7', '48.2', '69.0', '6.793'], ['[ITALIC] ml-VAE-S', '0.865', '0.734', '0.591', '28.7', '50.4', '70.7', '6.843'], ['[ITALIC] ml-... | A small self-BLEU score together with a large BLEU score can justify the effectiveness of a model, i.e., being able to generate realistic-looking as well as diverse samples. Among all the VAE variants, ml-VAE-D shows the smallest BLEU score and largest unique n-grams percentage, demonstrating the effectiveness of hiear... |
Bi-Decoder Augmented Network for Neural Machine Translation | 2001.04586 | Table 2: BLEU scores on WMT14 English-to-French for Transformer results. | ['[BOLD] NMT Models', '[BOLD] En→De', '[BOLD] De→En'] | [['Transformer', '27.5', '31.6'], ['Transformer + AD', '28.1', '32.0'], ['Transformer + AD (Denoising)', '28.0', '31.9'], ['Transformer + AD (RL)', '28.1', '[BOLD] 32.1'], ['[BOLD] BiDAN (Transformer)', '[BOLD] 28.1', '32.0']] | However, the improvement from reinforcement learning is quite modest, while the denoising part even declines the scores. We conjecture that this is because the positional encoding of the Transformer reduces the order dependency capture of the model, thus counteracts the effects of these approaches. |
Bi-Decoder Augmented Network for Neural Machine Translation | 2001.04586 | Table 1: BLEU scores for NMT models on WMT14 English-German and English-French and IWSLT 2015 English-Vietnamese dataset. “AD” denotes auxiliary decoder, “RL” denotes reinforcement learning. | ['[BOLD] NMT Models', '[BOLD] WMT14 [BOLD] En→De', '[BOLD] WMT14 [BOLD] De→En', '[BOLD] WMT14 [BOLD] En→Fr', '[BOLD] IWSLT15 [BOLD] En→Vi'] | [['Baseline', '22.6', '26.8', '32.3', '24.9'], ['Baseline + AD', '24.0', '28.2', '33.6', '26.2'], ['Baseline + AD (Denoising)', '24.3', '28.4', '34.0', '26.6'], ['Baseline + AD (RL)', '24.4', '28.5', '33.9', '26.8'], ['BiDAN (All modules converge)', '24.6', '28.7', '34.1', '27.0'], ['[BOLD] BiDAN', '[BOLD] 24.7', '[BOL... | In the medium part, we conduct an ablation experiment to evaluate the individual contribution of each component of our model. At first, we only add the auxiliary decoder D2 to the baseline model, and the BLEU scores on all the test sets rise about 1.4 point, which shows the effectiveness of our bi-decoder architecture ... |
Bi-Decoder Augmented Network for Neural Machine Translation | 2001.04586 | Table 3: BLEU scores on WMT14 English-to-French for NMT models with different encoders. | ['[BOLD] Encoder Source', '[BOLD] BLEU ( [ITALIC] p1)', '[BOLD] BLEU ( [ITALIC] p2)'] | [['Random Encoder', '7.2', '0.1'], ['En→De Encoder', '11.1', '0.2'], ['En→De Encoder (BiDAN)', '27.8', '2.5'], ['En→Fr Encoder (Original)', '[BOLD] 62.4', '[BOLD] 39.1']] | We train the NMT model on WMT14 English-to-French dataset, and then replace the well-trained encoder with different sources at the testing time. We first replace the encoder with random parameters and the results drop a lot, which is not surprising. Then we use the encoder trained on the WMT14 English-to-German dataset... |
Game-Based Video-Context Dialogue | 1809.04560 | Table 8: Ablation of cross-entropy loss vs. cross-entropy+maxmargin loss for our BiDAF-based generative model (on dev set). | ['Models', 'recall@1', 'recall@2', 'recall@5'] | [['Cross-entropy (XE)', '13.12', '23.45', '54.78'], ['XE+Max-margin', '15.61', '27.39', '57.02']] | Max-margin loss provides knowledge about the negative samples for the generative model, hence improves the retrieval-based recall@k scores. |
Game-Based Video-Context Dialogue | 1809.04560 | Table 3: Performance of our baselines, discriminative models, and generative models for recall@k metrics on our Twitch-FIFA test set. C and V represent chat and video context, respectively. | ['Models', 'r@1', 'r@2', 'r@5'] | [['Baselines', 'Baselines', 'Baselines', 'Baselines'], ['Most-Frequent-Response', '10.0', '16.0', '20.9'], ['Naive Bayes', '9.6', '20.9', '51.5'], ['Logistic Regression', '10.8', '21.8', '52.5'], ['Nearest Neighbor', '11.4', '22.6', '53.2'], ['Chat-Response-Cosine', '11.4', '22.0', '53.2'], ['Discriminative Model', 'Di... | We first discuss results of our simple non-trained and trained baselines (see Sec. The ‘Most-Frequent-Response’ baseline, which just ranks the 10-sized response retrieval list based on their frequency in the training data, gets only around 10% recall@1. Our other non-trained baselines: ‘Chat-Response-Cosine’ and ‘Neare... |
Game-Based Video-Context Dialogue | 1809.04560 | Table 4: Performance of our generative models on phrase matching metrics. | ['Models', 'METEOR', 'ROUGE-L'] | [['Multiple References', 'Multiple References', 'Multiple References'], ['Seq2seq + Atten. (C)', '2.59', '8.44'], ['Seq2seq + Atten. (V)', '2.66', '8.34'], ['Seq2seq + Atten. (C+V) ⊗', '3.03', '8.84'], ['⊗ + BiDAF (C+V)', '3.70', '9.82']] | Again, our BiDAF model is stat. significantly better than non-BiDAF model on both METEOR (p<0.01) and ROUGE-L (p<0.02) metrics. Since dialogue systems can have several diverse, non-overlapping valid responses, we consider a multi-reference setup where all the utterances in the 10-sec response window are treated as vali... |
Game-Based Video-Context Dialogue | 1809.04560 | Table 7: Ablation of classification vs. max-margin loss on our TriDAF discriminative model (on dev set). | ['Models', 'recall@1', 'recall@2', 'recall@5'] | [['Classification loss', '19.32', '33.72', '66.60'], ['Max-margin loss', '22.20', '35.90', '68.09']] | We observe that max-margin loss performs better than the classification loss, which is intuitive because max-margin loss tries to differentiate between positive and negative training example triples. |
Pretraining with Contrastive Sentence Objectives Improves Discourse Performance of Language Models | 2005.10389 | Table 1: Conpono improves the previous state-of-the-art on four DiscoEval tasks. The average accuracy across all tasks is also a new state-of-the-art, despite a small drop in accuracy for PDTB-E. BERT-Base and BERT-Large numbers are reported from Chen et al. (2019), while the rest were collected for this paper. We repo... | ['Model', 'SP', 'BSO', 'DC', 'SSP', 'PDTB-E', 'PDTB-I', 'RST-DT', 'avg.'] | [['BERT-Base', '53.1', '68.5', '58.9', '80.3', '41.9', '42.4', '58.8', '57.7'], ['BERT-Large', '53.8', '69.3', '59.6', '[BOLD] 80.4', '[BOLD] 44.3', '43.6', '59.1', '58.6'], ['RoBERTa-Base', '38.7', '58.7', '58.4', '79.7', '39.4', '40.6', '44.1', '51.4'], ['BERT-Base BSO', '53.7', '72.0', '71.9', '80.0', '42.7', '40.5'... | We report results from versions of Conpono using each of these encoding approaches, labeled isolated to represent separate encoding and uni-encoder to represent joint encoding of the anchor and target without a separate anchor encoding. Results: Our model excels in particular on the sentence ordering and coherence task... |
Pretraining with Contrastive Sentence Objectives Improves Discourse Performance of Language Models | 2005.10389 | Table 3: Our model improves accuracy over BERT-Base for RTE and COPA benchmarks. Improvements are comparable to BERT-Large but still lag behind much larger models trained on more data, such as ALBERT. All scores are on the validation set. | ['Model', 'RTE', 'COPA'] | [['BERT-Base', '66.4', '62.0'], ['BERT-Base BSO', '71.1', '67.0'], ['Conpono', '70.0', '69.0'], ['BERT-Large', '70.4', '69.0'], ['ALBERT', '86.6', '-']] | Results: We believe that the coherence and ordering aspects of these evaluation tasks are well fit to demonstrate the how our model can improve on strong baselines such as BERT-Base. Interestingly, we observe improvements over the baseline with BERT-Base BSO, showing that even simple discourse-level objectives could le... |
Pretraining with Contrastive Sentence Objectives Improves Discourse Performance of Language Models | 2005.10389 | Table 4: Conpono is more effective at classifying the most plausible sentence from the extended context than BERT-Base. We report the BERT-Large exact match score, where the model selects only the target entity from the context, for reference. All scores are on the validation set. | ['Model', 'Accuracy'] | [['BERT-Base', '61.2'], ['Conpono', '63.2'], ['BERT-Large', '69.8 [EM]']] | The task for the ReCoRD dataset is to select the correct entity from those that appear in the context to fill in the blank in the target. Previous models for ReCoRD have used a similar structure to SQuAD Rajpurkar et al. We, instead, generate all possible target sentences by filling the blank with each marked entity an... |
Pretraining with Contrastive Sentence Objectives Improves Discourse Performance of Language Models | 2005.10389 | Table 5: The ablation analysis shows the effects of different k values (ie. window sizes) in our objective, removing the MLM objective during pretraining and training with a small transformer encoder. | ['Model', 'SP', 'BSO', 'DC', 'SSP', 'PDTB-E', 'PDTB-I', 'RST-DT', 'avg.'] | [['k=4', '59.84', '76.05', '[BOLD] 73.62', '[BOLD] 80.65', '42.28', '44.25', '63.00', '62.81'], ['k=3', '60.47', '76.68', '72.74', '80.30', '[BOLD] 43.40', '44.28', '62.56', '62.92'], ['k=2', '[BOLD] 60.67', '[BOLD] 76.75', '72.85', '80.38', '42.87', '[BOLD] 44.87', '[BOLD] 63.13', '[BOLD] 63.07'], ['k=1', '47.56', '66... | We observe that using a window size for our objective that is larger than 1 is key to seeing downstream improvements. We believe that this is due to the objective being harder for the model because there is more variation farther from the anchor. At the same time, increasing the window size beyond 2 seems to result in ... |
Unsupervised Opinion Summarization with Noising and Denoising | 2004.10150 | Table 4: ROUGE-L of our model and versions thereof with less synthetic data (second block), using only one noising method (third block), and without some modules (fourth block). A more comprehensive table and discussion can be found in the Appendix. | ['Model', 'RT', 'Yelp'] | [['DenoiseSum', '16.27', '17.65'], ['10% synthetic dataset', '15.39', '16.22'], ['50% synthetic dataset', '15.76', '17.54'], ['no segment noising', '16.03', '16.88'], ['no document noising', '16.22', '16.67'], ['no explicit denoising', '16.06', '17.06'], ['no partial copy', '15.89', '16.31'], ['no discriminator', '15.8... | Our experiments confirm that increasing the size of the synthetic data improves performance, and that both segment and document noising are useful. We also show that explicit denoising, partial copy, and the discriminator help achieve best results. Finally, human-labeled categories (instead of LDA topics) decrease mode... |
Unsupervised Opinion Summarization with Noising and Denoising | 2004.10150 | Table 6: ROUGE-1/2/L F1 scores of our model and versions thereof with less synthetic data (second block), using only one noising method (third block), and without some modules (fourth block). | ['[EMPTY]', 'Rotten Tomatoes', 'Rotten Tomatoes', 'Rotten Tomatoes', 'Yelp', 'Yelp', 'Yelp'] | [['Model', 'ROUGE-1', 'ROUGE-2', 'ROUGE-L', 'ROUGE-1', 'ROUGE-2', 'ROUGE-L'], ['DenoiseSum', '[BOLD] 21.26', '4.61', '[BOLD] 16.27', '[BOLD] 30.14', '[BOLD] 4.99', '[BOLD] 17.65'], ['10% synthetic dataset', '20.16', '3.14', '15.39', '28.54', '3.63', '16.22'], ['50% synthetic dataset', '20.76', '3.91', '15.76', '29.16',... | The final model consistently performs better on all metrics when compared to versions with less synthetic data (second block), versions with only one type of noise (second block), and versions with a module removed (third block). When using human-labeled categories, we see a slight improvement in ROUGE-2 on Rotten Toma... |
Incorporating Pragmatic Reasoning Communication into Emergent Language | 2006.04109 | Table 2: Pragmatics communication accuracy using virtual interlocutors. | ['[BOLD] Virtual Opponent', '[BOLD] SFide', '[BOLD] LFide', '[BOLD] ArgmaxL', '[BOLD] RSA', '[BOLD] IBR', '[BOLD] GT', '[BOLD] GTs'] | [['Exact copy', '100%', '100%', '55.6±2.5', '55.1±2.1', '80.6±2.8', '75.5±2.0', '94.0±0.6'], ['Training 100k rnd', '96.6%', '97.0%', '53.0±2.3', '54.3±1.4', '68.6±1.6', '58.1±2.2', '69.9±1.8']] | In classic pragmatic frameworks, it is often assumed that interlocutors know about each other very well. For example, the prior probabilites PS0 and PL0 are common knowledge to each other. However, in practice, game information may be incomplete and one’s assumptions about the other interlocutor may diverge from realit... |
Learning to Learn Morphological Inflection for Resource-Poor Languages | 2004.13304 | Table 2: Inflection accuracy on the test languages; best results in bold. Languages listed by language family; from top to bottom: Romance, Slavic, Uralic. | ['[EMPTY]', '[BOLD] PG [BOLD] MAML-PG', '[BOLD] PG [BOLD] MulPG+FT', '[BOLD] PG [BOLD] MulPG', '[BOLD] PG', '[BOLD] MED [BOLD] MAML-MED', '[BOLD] MED [BOLD] MulMED+FT', '[BOLD] MED [BOLD] MulMED', '[BOLD] UZH'] | [['Asturian', '72.08', '[BOLD] 73.28', '66.98', '69.82', '68.94', '65.54', '58.62', '71.56'], ['French', '[BOLD] 64.16', '61.54', '49.10', '53.98', '46.18', '43.64', '24.68', '64.04'], ['Friulian', '[BOLD] 80.00', '77.00', '66.00', '69.40', '63.40', '59.20', '35.40', '78.20'], ['Italian', '53.12', '[BOLD] 54.30', '40.1... | We make the following observations: For both MED and PG, MAML-trained models outperform all other models of the same architecture: MAML-PG (resp. MAML-MED) obtains a 1.21% (resp. 3.16%) higher accuracy on average over languages than the second best model MulPG+FT (resp. MulMED+FT). This demonstrates that MAML is more e... |
Investigation of Synthetic Speech Detection Using Frame- and Segment-Specific Importance Weighting | 1610.03009 | TABLE II: Performance of Each of the Sound-class Detectors Measured in Terms of Equal-error-rates (EERs) for the Development Data. Frequency of Observation in Development Utterances is also Shown for Each Class Type. | ['Class', 'S1', 'S2', 'S3', 'S4', 'S5', 'All', 'Freq.'] | [['Vowel', '3.52', '13.30', '[BOLD] 0.65', '[BOLD] 0.74', '7.26', '6.35', '0.542'], ['Nasal', '8.90', '20.82', '5.09', '5.86', '13.79', '11.62', '0.156'], ['Glide', '9.33', '21.69', '4.10', '4.44', '15.92', '12.15', '0.118'], ['Stop', '[BOLD] 2.24', '[BOLD] 4.78', '0.70', '0.78', '[BOLD] 6.77', '[BOLD] 3.68', '0.112'],... | Performance of each class is significantly different from each other and they change substantially depending on the attack method. Also note that, even though vowel class is observed more than other classes, their performance is better than other systems only for HMM-based TTS attacks. For the voice-conversion attacks,... |
Read + Verify: Machine Reading Comprehension with Unanswerable Questions | 1808.05759 | Table 5: Comparison of different readers with fixed answer verifier. | ['Configuration', 'All EM', 'All F1', 'NoAns ACC'] | [['DocQA', '61.9', '64.8', '69.1'], ['+ Model-III', '[BOLD] 66.5', '[BOLD] 69.2', '[BOLD] 75.2'], ['DocQA + ELMo', '65.1', '67.6', '70.6'], ['+ Model-III', '[BOLD] 68.0', '[BOLD] 70.7', '[BOLD] 76.1']] | We find that the absolute improvements are even larger: the no-answer accuracy roughly increases by 6 points when adding Model-III to DocQA (from 69.1 to 75.2), and 5.5 points when adding Model-III to DocQA + ELMo (from 70.6 to 76.1). |
Read + Verify: Machine Reading Comprehension with Unanswerable Questions | 1808.05759 | Table 1: Comparison of different approaches on the SQuAD 2.0 test set, extracted on Aug 28, 2018: Levy et al. Levy171, Clark et al. Clark182, Liu et al. liu2017stochastic3, Huang et al. Huang17b4 and Wang et al. wang2018multi5. † indicates unpublished works. | ['Model', 'Dev EM', 'Dev F1', 'Test EM', 'Test F1'] | [['BNA1', '59.8', '62.6', '59.2', '62.1'], ['DocQA2', '61.9', '64.8', '59.3', '62.3'], ['DocQA + ELMo', '65.1', '67.6', '63.4', '66.3'], ['ARRR†', '-', '-', '68.6', '71.1'], ['VS3−Net†', '-', '-', '68.4', '71.3'], ['SAN3', '-', '-', '68.6', '71.4'], ['FusionNet++(ensemble)4', '-', '-', '70.3', '72.6'], ['SLQA+5', '-', ... | We use Model-III as the default answer verifier, and only report the best result. As we can see, our system obtains state-of-the-art results by achieving an EM score of 71.7 and a F1 score of 74.2 on the test set. Notice that SLQA+ has reached a comparable result compared to our approach. |
Read + Verify: Machine Reading Comprehension with Unanswerable Questions | 1808.05759 | Table 2: Comparison of readers with different auxiliary losses. | ['Configuration', 'HasAns EM', 'HasAns F1', 'All EM', 'All F1', 'NoAns ACC'] | [['RMR', '72.6', '81.6', '66.9', '69.1', '73.1'], ['- indep-I', '71.3', '80.4', '66.0', '68.6', '72.8'], ['- indep-II', '72.4', '81.4', '64.0', '66.1', '69.8'], ['- both', '71.9', '80.9', '65.2', '67.5', '71.4'], ['RMR + ELMo', '79.4', '86.8', '71.4', '73.7', '77.0'], ['- indep-I', '78.9', '86.5', '71.2', '73.5', '76.7... | Next, we do an ablation study on the SQuAD 2.0 development set to show the effects of our proposed methods for each individual component. Removing the independent span loss (indep-I) results in a performance drop for all answerable questions (HasAns), indicating that this loss helps the model in better identifying the ... |
Read + Verify: Machine Reading Comprehension with Unanswerable Questions | 1808.05759 | Table 3: Comparison of different architectures for the answer verifier. | ['Configuration', 'NoAns ACC'] | [['Model-I', '74.5'], ['Model-II', '74.6'], ['Model-II + ELMo', '75.3'], ['Model-III', '[BOLD] 76.2'], ['Model-III + ELMo', '76.1']] | Model-III outperforms all of other competitors, achieving a no-answer accuracy of 76.2. This illustrates that the combination of two different architectures can bring in further improvement. Adding ELMo embeddings, however, does not boost the performance. |
Read + Verify: Machine Reading Comprehension with Unanswerable Questions | 1808.05759 | Table 4: Comparison of readers with different answer verifiers. | ['Configuration', 'All EM', 'All F1', 'NoAns ACC'] | [['RMR', '66.9', '69.1', '73.1'], ['+ Model-I', '68.3', '71.1', '76.2'], ['+ Model-II', '68.1', '70.8', '75.6'], ['+ Model-II + ELMo', '68.2', '70.9', '75.9'], ['+ Model-III', '[BOLD] 68.5', '[BOLD] 71.5', '[BOLD] 77.1'], ['+ Model-III + ELMo', '68.5', '71.2', '76.5'], ['RMR + ELMo', '71.4', '73.7', '77.0'], ['+ Model-... | The combination of base reader with any answer verifier can always result in considerable performance gains, and combining the reader with Model-III obtains the best result. We find that the improvement on no-answer accuracy is significant. This metric raises from 73.1 to 77.1 after adding Model-III to RMR, increasing ... |
Assigning Medical Codes at the Encounter Levelby Paying Attention to Documents | 1911.06848 | Table 1: Document-level F1-score calculated by comparing document attention from ELDAN and human coders on 20 CPT codes. #enc is the number of encounters that contains the code. #doc is the number of documents within those encounters. #source is the number of documents being labeled by human coders as the source docume... | ['CPT Codes', '#enc', '#doc', '#source', 'Attention', 'Chance', 'Diff'] | [['43239', '8', '19', '9', '88.89', '59.22', '29.67'], ['45380', '5', '11', '5', '90.91', '56.47', '34.44'], ['45385', '6', '13', '8', '85.71', '67.52', '18.20'], ['66984', '7', '13', '7', '100.00', '68.65', '31.35'], ['45378', '10', '20', '11', '90.91', '67.44', '23.47'], ['12001', '1', '3', '1', '100.00', '45.63', '5... | for the most frequent 20 encounter-level codes, with surprisingly strong results: 100% F1-score on 7 out of 19 available codes. However, even chance performance could be good if the number of possible documents to assign credit to is very small. Therefore we compare to the chance baseline. ELDAN is consistently better,... |
Assigning Medical Codes at the Encounter Levelby Paying Attention to Documents | 1911.06848 | Figure 1: Left: Encounter-level F1-scores of the 20 most frequent CPT codes. #Docs is the average number of documents found in the encounters that contain the code; prevalence is the percentage of all encounters that contain that code. Right: Macro average of encounter-level F1 scores for every 10 codes (from most to l... | ['CPT Codes', '#Docs', 'Prevalence', 'ELDN', 'ELDAN', 'ELDAN +transfer'] | [['43239', '3.13', '4.15%', '84.59', '[BOLD] 86.21', '84.93'], ['45380', '2.78', '3.56%', '72.68', '[BOLD] 75.14', '74.02'], ['45385', '2.75', '2.44%', '71.33', '[BOLD] 72.33', '70.31'], ['66984', '2.51', '1.90%', '92.15', '92.87', '[BOLD] 93.00'], ['45378', '2.40', '1.89%', '62.67', '65.45', '[BOLD] 67.57'], ['12001',... | To show the trend across the full range of codes we macro-average every 10 codes from most frequent to least frequent ELDAN with or without naïve transfer learning consistently outperforms ELDN, even for extremely rare codes (<0.1%). As codes become rarer, ELDAN+transfer tends toward outperforming ELDAN more substantia... |
Assigning Medical Codes at the Encounter Levelby Paying Attention to Documents | 1911.06848 | Figure 1: Left: Encounter-level F1-scores of the 20 most frequent CPT codes. #Docs is the average number of documents found in the encounters that contain the code; prevalence is the percentage of all encounters that contain that code. Right: Macro average of encounter-level F1 scores for every 10 codes (from most to l... | ['Average', 'Prevalence', 'ELDN', 'ELDAN', 'ELDAN +transfer', 'ΔELDAN'] | [['1st to 10th', '1.97%', '65.22', '66.93', '[BOLD] 67.14', '0.22'], ['11st to 20th', '0.78%', '50.82', '53.87', '[BOLD] 55.38', '1.50'], ['21st to 30th', '0.51%', '55.93', '[BOLD] 63.07', '62.23', '-0.85'], ['31st to 40th', '0.40%', '44.93', '51.92', '[BOLD] 55.24', '3.32'], ['41st to 50th', '0.30%', '32.08', '38.61',... | To show the trend across the full range of codes we macro-average every 10 codes from most frequent to least frequent ELDAN with or without naïve transfer learning consistently outperforms ELDN, even for extremely rare codes (<0.1%). As codes become rarer, ELDAN+transfer tends toward outperforming ELDAN more substantia... |
Assigning Medical Codes at the Encounter Levelby Paying Attention to Documents | 1911.06848 | Table 1: Document-level F1-score calculated by comparing document attention from ELDAN and human coders on 20 CPT codes. #enc is the number of encounters that contains the code. #doc is the number of documents within those encounters. #source is the number of documents being labeled by human coders as the source docume... | ['CPT Codes', '#enc', '#doc', '#source', 'Attention', 'Chance', 'Diff'] | [['12002', '4', '13', '6', '92.31', '56.02', '36.29'], ['59025', '0', '0', '0', '-', '-', '-'], ['11042', '5', '23', '16', '58.06', '64.89', '-6.82'], ['47562', '1', '5', '3', '100.00', '57.62', '42.38'], ['62323', '5', '11', '7', '87.50', '69.85', '17.65'], ['64483', '3', '8', '4', '100.00', '58.07', '41.93'], ['43235... | for the most frequent 20 encounter-level codes, with surprisingly strong results: 100% F1-score on 7 out of 19 available codes. However, even chance performance could be good if the number of possible documents to assign credit to is very small. Therefore we compare to the chance baseline. ELDAN is consistently better,... |
Adversarial reconstruction for Multi-modal Machine Translation | 1910.02766 | Table 4: Ablation study of Q-WAAE model | ['[BOLD] Test sets', '[BOLD] COCO-ambiguous [BOLD] BLEU', '[BOLD] COCO-ambiguous [BOLD] METEOR'] | [['Baseline', '28.50', '48.80'], ['Baseline + [ITALIC] G + no [ITALIC] v', '29.43', '49.60'], ['Baseline + [ITALIC] G', '29.91', '49.24'], ['[ITALIC] Q-WAAE + no [ITALIC] v', '30.57', '50.15'], ['[ITALIC] Q-WAAE', '31.41', '50.95']] | To understand the success of Q-WAAE on the ambiguous COCO data-set, we perform an ablation study of the model. We first discard the adversarial discriminator so that we only train the reconstruction module with the MSE loss (+ G). We also discard the use of the features v in the translation model for both the ablated m... |
Adversarial reconstruction for Multi-modal Machine Translation | 1910.02766 | Table 2: Q-WAAE : Impact on the METEOR metric of the reconstruction and adversarial loss coefficient on the ambiguous COCO data-set | ['[EMPTY]', '[EMPTY]', '[ITALIC] λr 0.2', '[ITALIC] λr 0.5', '[ITALIC] λr 0.8'] | [['[origin=c]90 [ITALIC] λa', '0.2', '[BOLD] 50.95', '50.08', '49.33'], ['[origin=c]90 [ITALIC] λa', '0.5', '49.79', '49.62', '49.16'], ['[origin=c]90 [ITALIC] λa', '0.8', '49.70', '49.16', '48.02']] | The results show that if the auxiliary loss (adversarial and/or reconstruction) is made too important compared to the translation loss, the translation quality is impaired. |
Adversarial reconstruction for Multi-modal Machine Translation | 1910.02766 | Table 3: G-GWAN : Impact of the noise concatenated to the hidden state of size 512 | ['[EMPTY]', '[EMPTY]', '| [ITALIC] z| 64', '| [ITALIC] z| 128', '| [ITALIC] z| 256', '| [ITALIC] z| 512'] | [['[origin=c]90', 'METEOR', '50.35', '[BOLD] 50.43', '49.71', '49.48']] | The G-WGAN also shows improvements over the baseline and obtains similar results to Q-WAAE. Nonetheless, a small discrepancy is noticeable on the COCO-ambiguous. We believe that the main advantage of the Q-WAAE loss is the actual presence of a direct mean square error reconstruction loss along the adversarial loss. |
Query-Focused Opinion Summarization for User-Generated Content | 1606.05702 | Table 6: Effect of different dispersion functions, content coverage, and dissimilarity metrics on our system. [Left] JSD values for different combinations on Yahoo! data, using LDA with 100 topics. All systems are significantly different from each other at significance level α=0.05. Systems using summation of distances... | ['[BOLD] TAC 2008', '[BOLD] TAC 2008 Dispersion [ITALIC] sum', '[BOLD] TAC 2008 Dispersion [ITALIC] sum', '[BOLD] TAC 2008 Dispersion [ITALIC] min', '[BOLD] TAC 2008 Dispersion [ITALIC] min'] | [['Dissimi', 'Cont [ITALIC] tfidf', 'Cont [ITALIC] sem', 'Cont [ITALIC] tfidf', 'Cont [ITALIC] sem'], ['[ITALIC] Semantic', '0.2216', '0.2169', '0.2772', '0.2579'], ['[ITALIC] Topical', '0.2128', '0.2090', '[BOLD] 0.3234', '0.3056'], ['[ITALIC] Lexical', '0.2167', '0.2129', '0.3117', '0.3160']] | Given that the text similarity metrics and dispersion functions play important roles in the framework, we further study the effectiveness of different content coverage functions (Cosine using TFIDF vs. Semantic), dispersion functions (hsum vs. hmin), and dissimilarity metrics used in dispersion functions (Semantic vs. ... |
Query-Focused Opinion Summarization for User-Generated Content | 1606.05702 | Table 3: [Left] Summaries evaluated by Jensen-Shannon divergence (JSD) on Yahoo Answer for summaries of 100 words and 200 words. The average length of the best answer is 102.70. [Right] Value addition of each component in the objective function. The JSD on each line is statistically significantly lower than the JSD on ... | ['[EMPTY]', '[BOLD] Length 100', '[BOLD] Length 200'] | [['Best answer', '0.3858', '-'], ['Lin:2011:CSF:2002472.2002537', '0.3398', '0.2008'], ['Lin:2011:CSF:2002472.2002537 + q', '0.3379', '0.1988'], ['dasgupta-kumar-ravi:2013:ACL2013', '0.3316', '0.1939'], ['Our system', '[BOLD] 0.3017', '[BOLD] 0.1758']] | The topic number is tuned on the development set, and we find that varying the number of topics does not impact performance too much. Meanwhile, both our system and \newcitedasgupta-kumar-ravi:2013:ACL2013 produce better JSD scores than the two variants of the \newciteLin:2011:CSF:2002472.2002537 system, which implies ... |
Query-Focused Opinion Summarization for User-Generated Content | 1606.05702 | Table 3: [Left] Summaries evaluated by Jensen-Shannon divergence (JSD) on Yahoo Answer for summaries of 100 words and 200 words. The average length of the best answer is 102.70. [Right] Value addition of each component in the objective function. The JSD on each line is statistically significantly lower than the JSD on ... | ['[EMPTY]', '[BOLD] JSD100', '[BOLD] JSD200'] | [['Rel(evance)', '0.3424', '0.2053'], ['Rel + Aut(hor)', '0.3375', '0.2040'], ['Rel + Aut + TM (Topic Models)', '0.3366', '0.2033'], ['Rel + Aut + TM + Pol(arity)', '0.3309', '0.1983'], ['Rel + Aut + TM + Pol + Cont(ent Coverage)', '0.3102', '0.1851'], ['Rel + Aut + TM + Pol + Cont + Disp(ersion)', '[BOLD] 0.3017', '[B... | The topic number is tuned on the development set, and we find that varying the number of topics does not impact performance too much. Meanwhile, both our system and \newcitedasgupta-kumar-ravi:2013:ACL2013 produce better JSD scores than the two variants of the \newciteLin:2011:CSF:2002472.2002537 system, which implies ... |
Query-Focused Opinion Summarization for User-Generated Content | 1606.05702 | Table 6: Effect of different dispersion functions, content coverage, and dissimilarity metrics on our system. [Left] JSD values for different combinations on Yahoo! data, using LDA with 100 topics. All systems are significantly different from each other at significance level α=0.05. Systems using summation of distances... | ['[BOLD] Yahoo! Answer', '[BOLD] Yahoo! Answer Dispersion [ITALIC] sum', '[BOLD] Yahoo! Answer Dispersion [ITALIC] sum', '[BOLD] Yahoo! Answer Dispersion [ITALIC] min', '[BOLD] Yahoo! Answer Dispersion [ITALIC] min'] | [['Dissimi', 'Cont [ITALIC] tfidf', 'Cont [ITALIC] sem', 'Cont [ITALIC] tfidf', 'Cont [ITALIC] sem'], ['[ITALIC] Semantic', '0.3143', '0.324 3', '0.3129', '0.3232'], ['[ITALIC] Topical', '0.3101', '0.3202', '0.3106', '0.3209'], ['[ITALIC] Lexical', '[BOLD] 0.3017', '0.3147', '0.3071', '0.3172']] | Given that the text similarity metrics and dispersion functions play important roles in the framework, we further study the effectiveness of different content coverage functions (Cosine using TFIDF vs. Semantic), dispersion functions (hsum vs. hmin), and dissimilarity metrics used in dispersion functions (Semantic vs. ... |
Multilingual Neural Machine Translation with Knowledge Distillation | 1902.10461 | Table 6: BLEU scores of selective distillation (our method) and distillation all the time during the training process on the Ted talk dataset. | ['[EMPTY]', 'Bg', 'Et', 'Fi', 'Fr', 'Gl', 'Hi', 'Hy', 'Ka'] | [['distillation all the time', '28.07', '12.64', '15.13', '33.69', '30.28', '18.86', '19.88', '14.04'], ['selective distillation', '29.18', '15.63', '17.23', '34.32', '31.90', '21.00', '21.17', '18.27'], ['Δ', '+1.11', '+2.99', '+2.10', '+0.63', '+1.62', '+2.14', '+1.29', '+4.23'], ['[EMPTY]', 'Ku', 'Mk', 'My', 'Sl', '... | Selective Distillation We list the 16 languages on which the two methods (selective distillation, and distillation all the time) that have difference bigger than 0.5 in terms of BLEU score. It can be seen that selective distillation performs better on 13 out of 16 languages, with large BLEU score improvements, which de... |
Multilingual Neural Machine Translation with Knowledge Distillation | 1902.10461 | Table 14: BLEU scores of the individual and multilingual models on the 44 languages→English on the Ted talk dataset. | ['Language', 'Ar', 'Bg', 'Cs', 'Da', 'De', 'El', 'Es', 'Et', 'Fa'] | [['[ITALIC] Individual', '31.07', '38.64', '26.42', '38.21', '34.63', '36.69', '41.20', '7.43', '26.67'], ['[ITALIC] Multilingual (Baseline)', '27.84', '27.76', '27.17', '40.41', '32.85', '36.04', '39.80', '14.86', '24.93'], ['[ITALIC] Multilingual (Our method)', '29.57', '29.18', '28.30', '42.23', '34.53', '37.49', '4... | It can be seen that while multilingual baseline performs worse than the individual model, multilingual model based on our method nearly matches and even outperforms the individual model. Note that the multilingual model handles 44 languages in total, which means our method can reduce the model parameters size to 1/44 w... |
Graph based Neural Networks for Event Factuality Prediction using Syntactic and Semantic Structures | 1907.03227 | Table 1: Test set performance. * denotes the models trained on separate datasets while ** indicates those trained on multiple datasets. †specifies the models in Rudinger et al. (2018) that are significantly improved with BERT. | ['[EMPTY]', 'FactBank MAE', 'FactBank [ITALIC] r', 'UW MAE', 'UW [ITALIC] r', 'Meantime MAE', 'Meantime [ITALIC] r', 'UDS-IH2 MAE', 'UDS-IH2 [ITALIC] r'] | [['Lee et\xa0al. ( 2015 )*', '-', '-', '0.511', '0.708', '-', '-', '-', '-'], ['Stanovsky et\xa0al. ( 2017 )*', '0.590', '0.710', '[BOLD] 0.420', '0.660', '0.340', '0.470', '-', '-'], ['L-biLSTM(2)-S*†', '0.427', '0.826', '0.508', '0.719', '0.427', '0.335', '0.960', '0.768'], ['L-biLSTM(2)-MultiBal**†', '0.391', '0.821... | This section evaluates the effectiveness of the proposed model for EFP on the benchmark datasets. We compare the proposed model with the best reported systems in the literature with linguistic features Lee et al. ; Stanovsky et al. et al. Importantly, to achieve a fair comparison, we obtain the actual implementation of... |
Question-Answering with Grammatically-Interpretable Representations | 1705.08432 | Table 3: Symbol 27 | ['[BOLD] Token', '[BOLD] Similarity'] | [['annexed', '0.836'], ['Brisbane', '0.8359'], ['European', '0.8341'], ['Scotland', '0.8321'], ['Cyprus', '0.8275'], ['governments', '0.8266'], ['Commonwealth', '0.8261'], ['Britain', '0.8243'], ['flexibility', '0.8227'], ['territories', '0.8219'], ['Switzerland', '0.821'], ['countries', '0.8206'], ['freedom', '0.819']... | To interpret the lexical-semantic content of the TPR symbols s(t) learned by the TPRN network: s(t)=SaS(t)∈R10 is calculated for all (120,950) word tokens w(t)in the validation set. The cosine similarity is computed between aS(t) and the embedding vector of each symbol. The symbol with maximum (cosine) similarity is as... |
Question-Answering with Grammatically-Interpretable Representations | 1705.08432 | Table 1: Performance of the proposed TPRN model compared to BiDAF proposed in [Seo et al.2016] | ['Single Model', 'EM(dev)', 'F1(dev)', 'EM(test)', 'F1(test)'] | [['TPRN', '63.8', '74.4', '66.6', '76.3'], ['BiDAF', '62.8', '73.5', '67.1', '76.8']] | We compared the performance of single models. In the TPRN model tested here for performance comparison purposes, we set the number of symbols and roles to 600 and 100 respectively and the embedding size of symbols and roles to 15 and 10. Each experiment for the TPRN model took about 13 hours on a single Tesla P100 GPU.... |
Question-Answering with Grammatically-Interpretable Representations | 1705.08432 | Table 3: Symbol 27 | ['[BOLD] Token', '[BOLD] Similarity'] | [['printmaker', '0.9587'], ['composer', '0.8992'], ['who', '0.8726'], ['mathematician', '0.8675'], ['guitarist', '0.8622'], ['musician', '0.8055'], ['Whose', '0.7774'], ['engineer', '0.7753'], ['chemist', '0.7485'], ['how', '0.7335'], ['strict', '0.7207']] | To interpret the lexical-semantic content of the TPR symbols s(t) learned by the TPRN network: s(t)=SaS(t)∈R10 is calculated for all (120,950) word tokens w(t)in the validation set. The cosine similarity is computed between aS(t) and the embedding vector of each symbol. The symbol with maximum (cosine) similarity is as... |
Question-Answering with Grammatically-Interpretable Representations | 1705.08432 | Table 3: Symbol 27 | ['[BOLD] Token', '[BOLD] Similarity'] | [['phrase', '0.817'], ['wrong', '0.8146'], ['mean', '0.7972'], ['constitutes', '0.7771'], ['call', '0.7621'], ['happens', '0.752'], ['the', '0.7477'], ['God', '0.7425'], ['nickname', '0.7368'], ['spelled', '0.7162'], ['name', '0.712'], ['happened', '0.6889'], ['as', '0.6699'], ['defines', '0.647']] | To interpret the lexical-semantic content of the TPR symbols s(t) learned by the TPRN network: s(t)=SaS(t)∈R10 is calculated for all (120,950) word tokens w(t)in the validation set. The cosine similarity is computed between aS(t) and the embedding vector of each symbol. The symbol with maximum (cosine) similarity is as... |
Question-Answering with Grammatically-Interpretable Representations | 1705.08432 | Table 3: Symbol 27 | ['[BOLD] Token', '[BOLD] Similarity'] | [['abolished', '0.8777'], ['west', '0.8734'], ['nations', '0.8613'], ['Newcastle', '0.8588'], ['south', '0.8573'], ['Melbourne', '0.8558'], ['Australia', '0.8544'], ['World', '0.8526'], ['Belgium', '0.849'], ['donors', '0.8476'], ['Asian', '0.8404'], ['Greece', '0.8402'], ['Europe', '0.8397'], ['Thailand', '0.8393'], [... | To interpret the lexical-semantic content of the TPR symbols s(t) learned by the TPRN network: s(t)=SaS(t)∈R10 is calculated for all (120,950) word tokens w(t)in the validation set. The cosine similarity is computed between aS(t) and the embedding vector of each symbol. The symbol with maximum (cosine) similarity is as... |
Where’s My Head? Definition, Dataset and Models for Numeric Fused-Heads Identification and Resolution | 1905.10886 | Table 6: NFH Resolution accuracies on the development and test sets. | ['Model', 'Development', 'Test'] | [['Base', '65.6', '60.8'], ['+ Elmo', '[BOLD] 77.2', '[BOLD] 74.0']] | The complete model trained on the entire training data achieves 65.6% accuracy on the development set and 60.8% accuracy on the test set. The model with ELMo embeddings Peters et al. |
Where’s My Head? Definition, Dataset and Models for Numeric Fused-Heads Identification and Resolution | 1905.10886 | Table 2: NFH Identification corpus summary. The train and dev splits are noisy and the test set are gold annotations. | ['[EMPTY]', 'train', 'dev', 'test', 'all'] | [['pos', '71,821', '7865', '206', '79,884'], ['neg', '93,785', '10,536', '294', '104,623'], ['all', '165,606', '18,401', '500', '184,507']] | We improve the NFH identification using machine learning. We create a large but noisy data set by considering all the numbers in the corpus and treating the NFH identified by the rule-based approach as positive (79,678 examples) and all other numbers as negative (104,329 examples). We randomly split the dataset into tr... |
Keyphrase Generation: A Text Summarization Struggle | 1904.00110 | Table 2: Full-match scores of predicted keyphrases by various methods | ['[BOLD] Method', '[BOLD] Hulth (500) Krapivin (400) Meng (20K) OAGK (100K) [BOLD] F1@5', '[BOLD] Hulth (500) Krapivin (400) Meng (20K) OAGK (100K) [BOLD] F1@7', '[BOLD] Hulth (500) Krapivin (400) Meng (20K) OAGK (100K) [BOLD] F1@5', '[BOLD] Hulth (500) Krapivin (400) Meng (20K) OAGK (100K) [BOLD] F1@7', '[BOLD... | [['Yake!', '19.35', '21.47', '17.98', '17.4', '17.11', '15.19', '15.24', '14.57'], ['TopicRank', '16.5', '20.44', '6.93', '6.92', '11.93', '11.72', '11.9', '12.08'], ['Maui', '20.11', '20.56', '23.17', '23.04', '22.3', '19.63', '19.58', '18.42'], ['CopyRnn', '[BOLD] 29.2', '[BOLD] 33.6', '[BOLD] 30.2', '[BOLD] 25.2', '... | From the unsupervised models, we see that Yake! is consistently better than TopicRank. The next two supervised models perform even better, with CopyRnn being discretely superior than Maui. |
Keyphrase Generation: A Text Summarization Struggle | 1904.00110 | Table 3: Rouge scores of predicted keyphrases by various methods | ['[BOLD] Method', '[BOLD] Hulth (500) Krapivin (400) Meng (20K) OAGK (100K) [ITALIC] [BOLD] R [BOLD] 1 [ITALIC] [BOLD] F [BOLD] 1', '[BOLD] Hulth (500) Krapivin (400) Meng (20K) OAGK (100K) [ITALIC] [BOLD] R [BOLD] L [BOLD] F [BOLD] 1', '[BOLD] Hulth (500) Krapivin (400) Meng (20K) OAGK (100K) [ITALIC] [BOLD]... | [['Yake!', '37.48', '24.83', '26.19', '18.57', '26.47', '17.36', '20.38', '14.54'], ['TopicRank', '32.0', '20.36', '14.08', '11.47', '21.68', '15.94', '17.46', '13.28'], ['Maui', '36.88', '27.16', '28.29', '23.74', '34.33', '28.12', '32.16', '25.09'], ['CopyRnn', '[BOLD] 44.58', '[BOLD] 35.24', '[BOLD] 39.73', '[BOLD] ... | Abs works slightly better reaching scores from 10.24 to 14.75 %. PointCov is the best of the text summarizers producing keyphrase predictions that are usually clean and concise with few repetitions. This is probably the merit of the coverage mechanism. There is still a considerable gap between PointCov and CopyRnn. Cop... |
\csq@thequote@oinit\csq@thequote@oopenDid I Say Something Wrong?\csq@thequote@ocloseA Word-Level Analysis of Wikipedia Articles for Deletion Discussions | 1603.08048 | Table 2: This table shows the average performance of the classifiers with the various timeframes. The values are the arithmetic mean of the classifiers’ results. These are an SVM, a NB classifier and an LM classifier. A bold font highlights the best result considering this metric. The plus and minus symbols indicate wh... | ['[BOLD] time', '[BOLD] recall+', '[BOLD] recall−', '[BOLD] precision+', '[BOLD] precision−', '[BOLD] F1+', '[BOLD] F1−', '[BOLD] accuracy', '[BOLD] AUC'] | [['13 hours', '[BOLD] 49.81', '75.35', '66.68', '60.26', '56.8', '66.87', '62.58', '0.551'], ['1 day', '49.29', '[BOLD] 76.63', '[BOLD] 67.83', '[BOLD] 60.27', '[BOLD] 56.93', '[BOLD] 67.42', '[BOLD] 62.96', '[BOLD] 0.562'], ['1.5 days', '47.45', '76.03', '66.46', '59.22', '55.18', '66.52', '61.74', '0.542'], ['2 days'... | The figure shows how the values for all metrics peak at the 1 day timeframe and worsen the longer the timeframe becomes. The positive precision value is the only one which was not the highest using a 1 day timeframe. It was 49.81% for 13 hours and 49.29% for 1 day. Nevertheless, this is negligible considering that the ... |
\csq@thequote@oinit\csq@thequote@oopenDid I Say Something Wrong?\csq@thequote@ocloseA Word-Level Analysis of Wikipedia Articles for Deletion Discussions | 1603.08048 | Table 1: The table shows how commonly a term appears in disruptive or constructive posts. A bold font indicates that the term appears more frequently in that class. | ['[BOLD] term', '[BOLD] share of words from disruptive posts (‰)', '[BOLD] share of words from constructive posts (‰)'] | [['fucking', '[BOLD] 0.06', '0.00'], ['fuck', '[BOLD] 0.06', '0.01'], ['shit', '[BOLD] 0.09', '0.01'], ['i', '6.40', '[BOLD] 10.70'], ['you', '[BOLD] 10.64', '4.52'], ['me', '[BOLD] 2.43', '1.20'], ['my', '[BOLD] 3.00', '1.68'], ['your', '[BOLD] 3.05', '1.25'], ['myself', '[BOLD] 0.22', '0.13'], ['yourself', '[BOLD] 0.... | All 3,467,402 posts have been considered for these values. Common swear words like “fucking”, “fuck” and “shit” are hardly used in disruptive posts. With only 6.39‰ of disruptive posts containing any of the three swear words, the few posts that do, contain the terms multiple times. However, when they are used, they are... |
\csq@thequote@oinit\csq@thequote@oopenDid I Say Something Wrong?\csq@thequote@ocloseA Word-Level Analysis of Wikipedia Articles for Deletion Discussions | 1603.08048 | Table 3: This table shows the performance of the SVM, NB and LM classifiers using the independent posts approach. Function words classifications are marked with “FW”. All others were full text classifications. The plus and minus symbols indicate whether the performance metric was calculated for disruptive (+) or constr... | ['[BOLD] Classifier', '[BOLD] Recall+', '[BOLD] Recall−', '[BOLD] Precision+', '[BOLD] Precision−', '[BOLD] F1+', '[BOLD] F1−', '[BOLD] Accuracy', '[BOLD] AUC'] | [['SVM', '59.91', '75.56', '71.03', '65.33', '65.00', '70.07', '67.73', '0.750'], ['SVM (FW)', '31.11', '85.58', '68.33', '55.40', '42.75', '67.26', '58.34', '0.620'], ['NB', '42.42', '80.78', '68.82', '58.38', '52.49', '67.78', '61.60', '0.390'], ['NB (FW)', '26.04', '85.43', '64.12', '53.60', '37.04', '65.87', '55.73... | Overall, the support vector machine performs best, the naïve Bayes classifier ranks second and the language model ranks last. The SVM is outperformed only in negative recall by the NB classifier. However, due to lower negative precision, the NB classifier results in a worse negative F1 score than the SVM. The SVM is si... |
\csq@thequote@oinit\csq@thequote@oopenDid I Say Something Wrong?\csq@thequote@ocloseA Word-Level Analysis of Wikipedia Articles for Deletion Discussions | 1603.08048 | Table 4: This table shows the performance of the SVM, NB and LM classifiers using the sliding window approach with linear sampling. RapidMiner did not return AUC values for the SVM and NB classifier, so they had to be left out. Function words classifications are marked with “FW”. All others were full text classificatio... | ['[BOLD] Classifier', '[BOLD] Recall+', '[BOLD] Recall−', '[BOLD] Precision+', '[BOLD] Precision−', '[BOLD] F1+', '[BOLD] F1−', '[BOLD] Accuracy', '[BOLD] AUC'] | [['SVM', '20.97', '65.39', '37.73', '45.28', '26.96', '53.51', '43.18', '—'], ['SVM (FW)', '29.63', '37.92', '32.31', '35.01', '30.91', '36.41', '33.77', '—'], ['NB', '64.14', '46.39', '54.47', '56.40', '58.91', '50.91', '55.26', '—'], ['NB (FW)', '70.11', '51.28', '59.00', '63.18', '64.08', '56.61', '60.70', '—'], ['L... | The number of constructive and disruptive posts was increased compared to the independent posts classifications. This is conditioned by the sliding window algorithm which regards all merged posts containing a blocked post as disruptive. As this approach considers an editor’s post history, the data is different and the ... |
\csq@thequote@oinit\csq@thequote@oopenDid I Say Something Wrong?\csq@thequote@ocloseA Word-Level Analysis of Wikipedia Articles for Deletion Discussions | 1603.08048 | Table 5: This table shows the performance of the SVM, NB and LM classifiers using the independent posts approach on the oldest discussion. Function words classifications are marked with “FW”. All others were full text classifications. The plus and minus symbols indicate whether the performance metric was calculated for... | ['[BOLD] Classifier', '[BOLD] Recall+', '[BOLD] Recall−', '[BOLD] Precision+', '[BOLD] Precision−', '[BOLD] F1+', '[BOLD] F1−', '[BOLD] Accuracy', '[BOLD] AUC'] | [['SVM', '68.26', '89.98', '87.20', '73.93', '76.58', '81.17', '79.12', '0.890'], ['SVM (FW)', '29.86', '90.68', '76.21', '56.39', '42.91', '69.54', '60.27', '0.660'], ['NB', '49.10', '90.44', '83.70', '63.99', '61.89', '74.95', '69.77', '0.470'], ['NB (FW)', '37.30', '82.43', '67.98', '56.80', '48.17', '67.26', '59.86... | Although we only present the data of a single run each, all tests have been executed multiple times with newly sampled data sets. This confirmed that the observed performances were not the result of a sampling bias. The results always remained comparable but for one exception. When operating on a data set chronological... |
\csq@thequote@oinit\csq@thequote@oopenDid I Say Something Wrong?\csq@thequote@ocloseA Word-Level Analysis of Wikipedia Articles for Deletion Discussions | 1603.08048 | Table 6: This table shows the performance of the SVM and NB classifier using the sliding window approach with stratified sampling. Function words classifications are marked with “FW”. All others were full text classifications. The plus and minus symbols indicate whether the performance metric was calculated for disrupt... | ['[BOLD] Classifier', '[BOLD] Recall+', '[BOLD] Recall−', '[BOLD] Precision+', '[BOLD] Precision−', '[BOLD] F1+', '[BOLD] F1−', '[BOLD] Accuracy', '[BOLD] AUC'] | [['SVM', '88.96', '84.60', '85.25', '88.46', '87.07', '86.49', '86.78', '0.950'], ['SVM (FW)', '78.73', '50.75', '61.52', '70.47', '69.07', '59.01', '64.74', '0.710'], ['NB', '69.57', '93.43', '91.37', '75.43', '78.99', '83.47', '81.50', '0.670'], ['NB (FW)', '42.27', '79.33', '67.16', '57.88', '51.88', '66.93', '60.80... | This bar chart shows the performance of the full text SVM in contrast to it only factoring in function words. The classifier used the sliding window approach with stratified sampling. Thus, it erroneously performed well. The values are in relation to a random classifier with 0% expressing equal performance. The percent... |
Morphosyntactic Tagging with a Meta-BiLSTM Modelover Context Sensitive Token Encodings | 1805.08237 | Table 8: F1 score of char models and their performance on the dev. set for selected languages with different gather strategies, concatenate to gi (Equation 1). DQM shows results for our reimplementation of DBLP:conf/conll/DozatQM17 (cf. §3.2), where we feed in only the characters. The final column shows the number of x... | ['[BOLD] dev. set. [BOLD] lang.', '[ITALIC] F [ITALIC] last [ITALIC] B1 [ITALIC] st', '[ITALIC] F1 [ITALIC] st [ITALIC] B [ITALIC] last', '[ITALIC] F [ITALIC] last [ITALIC] B [ITALIC] last', '[ITALIC] F1 [ITALIC] st [ITALIC] B1 [ITALIC] st', 'DQM', '|xpos|'] | [['el', '[BOLD] 96.6', '[BOLD] 96.6', '96.2', '96.1', '95.9', '16'], ['grc', '[BOLD] 87.3', '87.1', '87.1', '86.8', '86.7', '3130'], ['la_ittb', '91.1', '91.5', '[BOLD] 91.9', '91.3', '91.0', '811'], ['ru', '95.6', '95.4', '95.6', '95.3', '[BOLD] 95.8', '49'], ['tr', '93.5', '93.3', '93.2', '92.5', '[BOLD] 93.9', '37']... | Thus, the proposed model concatenates all four of these and passes it as input to an multilayer perceptron (MLP): gi = concat(F1st(w),Flast(w), (1) B1st(w),Blast(w)) mcharsi = MLP(gi) A tag can then be predicted with a linear classifier that takes as input the output of the MLP mcharsi, applies a softmax function and c... |
Morphosyntactic Tagging with a Meta-BiLSTM Modelover Context Sensitive Token Encodings | 1805.08237 | Table 3: Results on WSJ test set. | ['[BOLD] System', '[BOLD] Accuracy'] | [['Sogaard:2011:SCN', '97.50'], ['DBLP:journals/corr/HuangXY15', '97.55'], ['choi:16a', '97.64'], ['andor2016globally.', '97.44'], ['DBLP:conf/conll/DozatQM17', '97.41'], ['ours', '[BOLD] 97.96']] | We also performed experiments on the Penn Treebank with the usual split in train, development and test set. Our model significantly outperforms these systems, with an absolute difference of 0.32% in accuracy, which corresponds to a RRIE of 12%. |
Morphosyntactic Tagging with a Meta-BiLSTM Modelover Context Sensitive Token Encodings | 1805.08237 | Table 5: Comparison of optimization methods: Separate optimization of the word, character and meta model is more accurate on average than full back-propagation using a single loss function.The results are statistically significant with two-tailed paired t-test for xpos with p<0.001 and for morphology with p<0.0001. | ['[BOLD] Optimization', 'Avg. F1 Score morphology', 'Avg. F1 Score xpos'] | [['separate', '[BOLD] 94.57', '[BOLD] 94.85'], ['jointly', '94.15', '94.48']] | We first compare jointly training the three model components (Meta-BiLSTM, character model, word model) to training each separately. Separate optimization leads to better accuracy for 34 out of 40 treebanks for the morphological features task and for 30 out of 39 treebanks for xpos tagging. Separate optimization out-pe... |
Morphosyntactic Tagging with a Meta-BiLSTM Modelover Context Sensitive Token Encodings | 1805.08237 | Table 6: F1 score for selected languages on sentence vs. word level character models for the prediction of morphology using late integration. | ['[BOLD] dev. set', 'word char model', 'sentence char model'] | [['el', '89.05', '93.41'], ['la_ittb', '93.22', '95.69'], ['ru', '88.94', '92.31'], ['tr', '87.78', '90.77']] | We compared the setup with sentence-based character context We selected for these experiments a number of morphological rich languages. The accuracy of the word-based character model joint with a word-based model were significantly lower than a sentence-based character model. We conclude also from these results and com... |
Morphosyntactic Tagging with a Meta-BiLSTM Modelover Context Sensitive Token Encodings | 1805.08237 | Table 7: F1 score for the character, word and meta models. The standard deviation of 10 random restarts of each model is show in the last three columns. The differences in means are all statistically significant at p<0.001 (paired t-test). | ['[BOLD] dev. set [BOLD] lang.', 'num. exp.', 'mean char', 'mean word', 'mean meta', 'stdev char', 'stdev word', 'stdev meta'] | [['el', '10', '96.43', '95.36', '[BOLD] 97.01', '0.13', '0.11', '0.09'], ['grc', '10', '88.28', '73.52', '[BOLD] 88.85', '0.21', '0.29', '0.22'], ['la_ittb', '10', '91.45', '87.98', '[BOLD] 91.94', '0.14', '0.30', '0.05'], ['ru', '10', '95.98', '93.50', '[BOLD] 96.61', '0.06', '0.17', '0.07'], ['tr', '10', '93.77', '90... | The examples show that the combined model has significantly higher accuracy compared with either the character and word models individually. |
Don’t Forget the Long Tail! A Comprehensive Analysis of Morphological Generalization in Bilingual Lexicon Induction | 1909.02855 | Table 4: The results on the standard BLI task and BLI controlled for lexeme for the original Ruder et al. (2018)’s model (✗) and the same model trained with a morphological constraint (✓) (discussed in §4.6). | ['[EMPTY]', 'Normal In vocab', 'Normal In vocab', 'Normal +OOVs', 'Normal +OOVs', 'Lexeme In vocab', 'Lexeme In vocab', 'Lexeme +OOVs', 'Lexeme +OOVs', 'Dictionary Sizes In vocab', 'Dictionary Sizes +OOVs'] | [['Constraint', '✗', '✓', '✗', '✓', '✗', '✓', '✗', '✓', 'In vocab', '+OOVs'], ['Ukrainian–Russian', '[BOLD] 68.4', '61.1', '[BOLD] 63.7', '56.1', '[BOLD] 89.9', '89.1', '[BOLD] 88.6', '87.6', '786', '933'], ['Russian–Slovak', '[BOLD] 25.7', '21.1', '[BOLD] 20.9', '17.0', '[BOLD] 79.3', '76.8', '[BOLD] 76.0', '74.2', '1... | We report the accuracy on the in-vocabulary pairs as well as all the pairs in the dictionary, including OOVs. As expected, compared to standard BLI this task is much easier for the models—the performance is generally high. For Slavic languages numbers remain high even in the open-vocabulary setup, which suggests that t... |
Don’t Forget the Long Tail! A Comprehensive Analysis of Morphological Generalization in Bilingual Lexicon Induction | 1909.02855 | Table 2: The sizes of our morphologically complete dictionaries for Slavic and Romance language families. We present the sizes for 20 base dictionaries. We further split those to obtain 40 train, development and test dictionaries—one for each mapping direction to ensure the correct source language lemma separation. | ['[BOLD] Slavic', 'Czech', 'Russian', 'Slovak', 'Ukrainian', '[BOLD] Romance', 'Spanish', 'Catalan', 'Portuguese', 'Italian'] | [['Polish', '53,353', '128,638', '14,517', '12,361', 'French', '686,139', '381,825', '486,575', '705,800'], ['Czech', '-', '65,123', '10,817', '8,194', 'Spanish', '-', '343,780', '476,543', '619,174'], ['Russian', '[EMPTY]', '-', '128,638', '10,554', 'Catalan', '[EMPTY]', '-', '261,016', '351,609'], ['Slovak', '[EMPTY]... | For each language pair (L1,L2) we first generated lemma translation pairs by mapping all L1 lemmata to all L2 lemmata for each synset that appeared in both L1 and L2 WordNets. We then filtered out the pairs which contained lemmata not present in UniMorph and generated inflected entries from the remaining pairs: one ent... |
DEAN: Learning Dual Emotion for Fake News Detection on Social Media | 1903.01728 | Table 3: Performance Comparison of Fake News Detection on Two Datasets. | ['Dataset', 'Methods', 'Accuracy', 'Precision', 'Recall', 'F1-Score'] | [['Weibo', 'DTC', '0.756', '0.754', '0.758', '0.756'], ['Weibo', 'ML-GRU', '0.799', '0.810', '0.790', '0.800'], ['Weibo', 'Basic-GRU', '0.835', '0.830', '0.850', '0.840'], ['Weibo', 'CSI', '0.835', '0.735', '[BOLD] 0.996', '0.858'], ['Weibo', 'HSA-BLSTM', '0.843', '[BOLD] 0.860', '0.810', '0.834'], ['Weibo', 'SAME', '0... | In particular, the DEAN model achieves an overall accuracy of 87.2% on Weibo dataset and 75.1% on Twitter dataset, which outperforms all the baseline models on both datasets. The outstanding performance of the proposed model demonstrates that the incorporation of emotion through embedding representation and gated fusio... |
DEAN: Learning Dual Emotion for Fake News Detection on Social Media | 1903.01728 | Table 1: 19 Hand-crafted Emotion Features of News Content. | ['Description', 'Amount'] | [['Fraction of emotion positive and negative words', '2'], ['Fraction of negative adverbs', '1'], ['Fraction of adverbs of degree', '1'], ['Emotion score', '1'], ['Fraction of pronoun first, second and third', '3'], ['Fraction of punctuation ?, !, ?!, multi ? and multi !', '5'], ['Fraction of emoticons', '1'], ['Fracti... | Hand-crafted News Emotion Features The overall emotion information of news content is also important to help measure how much signal from the emotion part should be absorbed for each word. For example, the news content which expresses intense emotions could further strengthen the importance of emotion part in each word... |
DEAN: Learning Dual Emotion for Fake News Detection on Social Media | 1903.01728 | Table 4: Analysis of the Dual Emotion Modeling. | ['Module', 'Methods', 'Weibo Dataset Accuracy', 'Weibo Dataset F1-Score', 'Twitter Dataset Accuracy', 'Twitter Dataset F1-Score'] | [['Content ( [ITALIC] Publisher Emotion)', 'WE', '0.790', '0.801', '0.678', '0.627'], ['Content ( [ITALIC] Publisher Emotion)', 'EE', '0.700', '0.719', '0.639', '0.615'], ['Content ( [ITALIC] Publisher Emotion)', 'WEE(c)', '0.813', '0.810', '0.690', '0.709'], ['Content ( [ITALIC] Publisher Emotion)', 'WEE(att)', '0.799... | , we could make the following observations: 1) in content module, the overall performance rises while using emotion embeddings; especially on Twitter dataset, adding emotion information increases the f1-score by nearly 6%; 2) publisher emotion plays a more important role than social emotion on both datasets. It possibl... |
Toward Making the Most of Context in Neural Machine Translation | 2002.07982 | Table 5: Accuracy (\%) of discourse phenomena. * different data and system conditions, only for reference. | ['[BOLD] Model', 'deixis', 'lex.c.', 'ell.infl.', 'ell.VP'] | [['SentNmt', '50.0', '45.9', '52.2', '24.2'], ['Ours', '61.3', '46.1', '61.0', '35.6'], ['voita2018context\xa0[voita2018context]*', '81.6', '58.1', '72.2', '80.0']] | We also want to examine whether the proposed model actually learns to utilize document context to resolve discourse inconsistencies that context-agnostic models cannot handle. We use contrastive test sets for the evaluation of discourse phenomena for English-Russian by \citeauthorvoita2018context [\citeyearvoita2018con... |
Toward Making the Most of Context in Neural Machine Translation | 2002.07982 | Table 1: Experiment results of our model in comparison with several baselines, including increments of the number of parameters over Transformer baseline (\Delta|{\bm{\theta}}|), training/testing speeds (v_{\rm{train}}/v_{\rm{test}}, some of them are derived from maruf2019selective [maruf2019selective]), and translatio... | ['[BOLD] Model', '\\Delta|{\\bm{\\theta}}|', 'v_{\\rm{train}}', 'v_{\\rm{test}}', 'Zh-En TED', 'En-De TED', 'En-De News', 'En-De Europarl', 'En-De avg.'] | [['SentNmt\xa0[Vaswani2017Attention]', '0.0m', '1.0\\times', '1.0\\times', '17.0', '23.10', '22.40', '29.40', '24.96'], ['DocT\xa0[zhang2018improving]', '9.5m', '0.65\\times', '0.98\\times', '[EMPTY]', '24.00', '23.08', '29.32', '25.46'], ['HAN [miculicich2018document]', '4.8m', '0.32\\times', '0.89\\times', '17.9', '2... | [maruf2019selective, SAN] and Query-guided Capsule Network [yang2019enhancing, QCN]. Among them, our model archives new state-of-the-art results on TED Zh-En and Europarl, showing the superiority of exploiting the whole document context. Though our model is not the best on TED En-De and News tasks, it is still comparab... |
Toward Making the Most of Context in Neural Machine Translation | 2002.07982 | Table 3: Ablation study on modeling context on TED Zh-En development set. ”Doc” means using a entire document as a sequence for input or output. BLEU{}_{\rm doc} indicates the document-level BLEU score calculated on the concatenation of all output sentences. | ['[BOLD] Model', 'BLEU (BLEU{}_{\\rm doc})'] | [['SentNmt\xa0[Vaswani2017Attention]', '11.4 (21.0)'], ['DocNmt (documents as input/output)', 'n/a (17.0)'], ['[ITALIC] Modeling source context', '[ITALIC] Modeling source context'], ['Doc2Sent', '6.8'], ['+ reset word positions for each sentence', '10.0'], ['+ segment embedding', '10.5'], ['+ segment-level relative at... | First of all, using the entire document as input and output directly cannot even generate document translation with the same number of sentences as source document, which is much worse than sentence-level baseline and our model in terms of document-level BLEU. For source context modeling, only casting the whole source ... |
UR-FUNNY: A Multimodal Language Dataset for Understanding Humor | 1904.06618 | Table 4: Binary accuracy for different variants of C-MFN and training scenarios outlined in Section 5. The best performance is achieved using all three modalities of text (T), vision (V) and acoustic (A). | ['Modality', 'T', 'A+V', 'T+A', 'T+V', 'T+A+V'] | [['C-MFN (P)', '62.85', '53.3', '63.28', '63.22', '64.47'], ['C-MFN (C)', '57.96', '50.23', '57.78', '57.99', '58.45'], ['C-MFN', '64.44', '57.99', '64.47', '64.22', '65.23']] | Results demonstrate that both context and punchline information are important as C-MFN outperforms C-MFN (P) and C-MFN (C) models. Punchline is the most important component for detecting humor as the performance of C-MFN (P) is significantly higher than C-MFN (C). Therefore, UR-FUNNY dataset presents new challenges to ... |
UR-FUNNY: A Multimodal Language Dataset for Understanding Humor | 1904.06618 | Table 1: Comparison between UR-FUNNY and notable humor detection datasets in the NLP community. Here, ‘pos’, ’neg’ , ‘mod’ and ‘spk’ denote positive, negative, modalities and speaker respectively. | ['Dataset', '#Pos', '#Neg', 'Mod', 'type', '#spk'] | [['16000 One-Liners', '16000', '16000', '{ [ITALIC] l}', 'joke', '-'], ['Pun of the Day', '2423', '2423', '{ [ITALIC] l}', 'pun', '-'], ['PTT Jokes', '1425', '2551', '{ [ITALIC] l}', 'political', '-'], ['Ted Laughter', '4726', '4726', '{ [ITALIC] l}', 'speech', '1192'], ['Big Bang Theory', '18691', '24981', '{ [ITALIC]... | Humor Analysis: Humor analysis has been among active areas of research in both natural language processing and affective computing. Pun of the Day” Yang et al. The above datasets have studied humor from different perspectives. For example, “16000 One-Liner” and “Pun of the Day” focus on joke detection (joke vs. not jok... |
Restricted Recurrent Neural Tensor Networks: Exploiting Word Frequency and Compositionality | 1704.00774 | Table 1: Comparison of validation and test set perplexity for r-RNTNs with f mapping (K=100 for PTB, K=376 for text8) versus s-RNNs and m-RNN. r-RNTNs with the same H as corresponding s-RNNs significantly increase model capacity and performance with no computational cost. The RNTN was not run on text8 due to the number... | ['Method', '[ITALIC] H', 'PTB # Params', 'PTB Test PPL', 'text8 # Params', 'text8 Test PPL', 'Method', '[ITALIC] H', 'PTB # Params', 'PTB Test PPL'] | [['s-RNN', '100', '2M', '146.7', '7.6M', '236.4', 'GRU', '244', '9.6M', '92.2'], ['r-RNTN [ITALIC] f', '100', '3M', '131.2', '11.4M', '190.1', 'GRU', '650', '15.5M', '90.3'], ['RNTN', '100', '103M', '128.8', '388M', '-', 'r-GRU [ITALIC] f', '244', '15.5M', '[BOLD] 87.5'], ['m-RNN', '100', '3M', '164.2', '11.4M', '895... | It is remarkable that even with K as small as 100, the r-RNTN approaches the performance of the RNTN with a small fraction of the parameters. This reinforces our hypothesis that complex transformation modeling afforded by distinct matrices is needed for frequent words, but not so much for infrequent words which can be ... |
Cyclical Annealing Schedule:A Simple Approach to Mitigating KL Vanishing | 1903.10145 | Table 5: Comparison on dialog response generation. BLEU (B) scores 1-4 are used for evaluation. Monotonic (M) and Cyclical (C) schedules are tested on two models. | ['[BOLD] Model [BOLD] Schedule', 'CVAE [BOLD] M', 'CVAE [BOLD] C', 'CVAE+BoW [BOLD] M', 'CVAE+BoW [BOLD] C'] | [['B1 prec', '0.326', '[BOLD] 0.423', '0.384', '[BOLD] 0.397'], ['B1 recall', '0.214', '[BOLD] 0.391', '0.376', '[BOLD] 0.387'], ['B2 prec', '0.278', '[BOLD] 0.354', '0.320', '[BOLD] 0.331'], ['B2 recall', '0.180', '[BOLD] 0.327', '0.312', '[BOLD] 0.323'], ['B3 prec', '0.237', '[BOLD] 0.299', '0.269', '[BOLD] 0.279'], ... | A4SS1SSS0Px2 Results The cyclical schedule outperforms the monotonic schedule in both settings. Under similar ELBO results, the cyclical schedule provide lower reconstruction errors, higher KL values, and higher BLEU values than the monotonic schedule. Interestingly, the monotonic schedule tends to overfit, while the c... |
Cyclical Annealing Schedule:A Simple Approach to Mitigating KL Vanishing | 1903.10145 | Table 3: Comparison on dialog response generation. Reconstruction perplexity (Rec-P) and BLEU (B) scores are used for evaluation. | ['[BOLD] Model [BOLD] Schedule', 'CVAE [BOLD] M', 'CVAE [BOLD] C', 'CVAE+BoW [BOLD] M', 'CVAE+BoW [BOLD] C'] | [['Rec-P ↓', '36.16', '[BOLD] 29.77', '18.44', '[BOLD] 16.74'], ['KL Loss ↑', '0.265', '[BOLD] 4.104', '14.06', '[BOLD] 15.55'], ['B4 prec', '0.185', '[BOLD] 0.234', '0.211', '[BOLD] 0.219'], ['B4 recall', '0.122', '[BOLD] 0.220', '0.210', '[BOLD] 0.219'], ['A-bow prec', '0.957', '[BOLD] 0.961', '0.958', '[BOLD] 0.961'... | (i) Smoothed Sentence-level BLEU Chen and Cherry : BLEU is a popular metric that measures the geometric mean of modified n-gram precision with a length penalty. We use BLEU-1 to 4 as our lexical similarity metric and normalize the score to 0 to 1 scale. (ii) Cosine Distance of Bag-of-word Embedding Liu et al. We used G... |
A Text Classification Framework for Simple and Effective Early Depression Detection Over Social Media Streams | 1905.08772 | Table 4: Results on the test set using all subject’s history as a single document, i.e. timeless classification. | ['[EMPTY]', '[ITALIC] F1', '[ITALIC] π', '[ITALIC] ρ'] | [['SS3', '[BOLD] 0.61', '[BOLD] 0.63', '0.60'], ['LOGREG', '0.59', '0.56', '0.63'], ['SVM', '0.55', '0.5', '0.62'], ['MNB', '0.39', '0.25', '[BOLD] 0.96'], ['KNN', '0.54', '0.5', '0.58']] | It is interesting to notice that we also performed classification of subjects on the test set using all subject’s writings as if it were a single document (i.e. classical timeless classification); results are shown in SS3 obtained the highest values for F1 (0.61) and Precision (0.63) measures, possibly due to the flexi... |
GLOSS: Generative Latent Optimization of Sentence Representations | 1907.06385 | Table 3: Effect of dimensionlity: additional results for the test performance of GLOSS-BoW and GLOSS-Pos models with different dimensionality of the latent vectors on unsupervised STS-(12-16, B) and supervised tasks. (∗) Following SentEval, STS-13 does not include the SMT dataset due to licensing issues. Best results f... | ['Model', 'Config. #Tok', 'Config. Dim', 'Unsupervised STS-() tasks 12', 'Unsupervised STS-() tasks 13*', 'Unsupervised STS-() tasks 14', 'Unsupervised STS-() tasks 15', 'Unsupervised STS-() tasks 16', 'Unsupervised STS-() tasks B', 'Unsupervised STS-() tasks Avg', 'Supervised tasks MR', 'Supervised tasks CR', 'Supervi... | [['GLOSS-BoW', '27M', '100', '54.8', '51.8', '68.4', '71.2', '[BOLD] 71.8', '[BOLD] 72.4', '65.1', '67.4', '72.0', '86.4', '79.5', '71.0', '75.3'], ['GLOSS-BoW', '27M', '300', '[BOLD] 55.9', '55.6', '[BOLD] 69.2', '73.4', '71.2', '72.1', '[BOLD] 66.2', '69.5', '74.7', '88.6', '82.3', '78.0', '78.6'], ['GLOSS-BoW', '27M... | Test performance on unsupervised STS-(12-16, B) and supervised tasks. (α) indicate results computed by us. (β) PV-DBOW results are taken from Pagliardini et al. (†) The unsupervised results for Skip-thought are taken from Arora et al. (∗) Following SentEval, STS-13 does not include the SMT dataset due to licensing issu... |
GLOSS: Generative Latent Optimization of Sentence Representations | 1907.06385 | Table 1: Test performance on unsupervised STS-(12-16, B) and supervised tasks. (α) indicate results computed by us. (β) PV-DBOW results are taken from Pagliardini et al. (2017). (†) The unsupervised results for Skip-thought are taken from Arora et al. (2017) and the supervised ones from Pagliardini et al. (2017). (∗) F... | ['Model', 'Config. #Tok', 'Config. Dim', 'Unsupervised STS-() tasks 12', 'Unsupervised STS-() tasks 13*', 'Unsupervised STS-() tasks 14', 'Unsupervised STS-() tasks 15', 'Unsupervised STS-() tasks 16', 'Unsupervised STS-() tasks B', 'Unsupervised STS-() tasks Avg', 'Supervised tasks MR', 'Supervised tasks CR', 'Supervi... | [['[ITALIC] Unsupervised methods trained on unordered corpus', '[ITALIC] Unsupervised methods trained on unordered corpus', '[ITALIC] Unsupervised methods trained on unordered corpus', '[ITALIC] Unsupervised methods trained on unordered corpus', '[ITALIC] Unsupervised methods trained on unordered corpus', '[ITALIC] Uns... | GLOSS-BoW performs particularly well on STS-Benchmark where it outperforms all methods. GLOSS-BoW is competitive to Sent2Vec, even though the latter was trained on 30 times more training data. In fact, it also matches the performance of InferSent which requires labeled training data. This shows that GLOSS is very data ... |
Part-of-Speech Tagging for Historical English | 1603.03144 | Table 3: Accuracy results for temporal adaptation in the PPCMBE and the PPCEME of historical English. Percentage error reduction is shown for the best-performing method, Fema-attribute. | ['Task', 'baseline SVM', 'baseline MEMM (Stanford)', 'SCL', 'Brown', 'word2vec', 'Fema single embedding', 'Fema attribute embeddings (error reduction)'] | [['[ITALIC] Modern British English (training from 1840-1914)', '[ITALIC] Modern British English (training from 1840-1914)', '[ITALIC] Modern British English (training from 1840-1914)', '[ITALIC] Modern British English (training from 1840-1914)', '[ITALIC] Modern British English (training from 1840-1914)', '[ITALIC] Mod... | English spelling had become mostly uniform and stable since around 1700 Among the two baseline systems, MEMM performs slightly better than SVM, showing a small benefit to structured prediction. Among the domain adaptation algorithms, Fema clearly outperforms SCL, Brown clustering and word2vec, with an averaged increase... |
Part-of-Speech Tagging for Historical English | 1603.03144 | Table 4: Accuracy results for adapting from the PTB to the PPCMBE and the PPCEME of historical English. ∗Error reduction for the normalized PPCEME is computed against the unnormalized SVM accuracy, showing total error reduction. | ['Target', 'Normalized', 'baseline SVM', 'baseline MEMM (Stanford)', 'SCL', 'Brown', 'word2vec', 'Fema single embedding', 'Fema attribute embeddings (error reduction)'] | [['ppcmbe', 'No', '81.12', '81.35', '81.66', '81.65', '81.75', '82.34', '[BOLD] 82.46 (7%)'], ['ppceme', 'No', '74.15', '74.34', '75.89', '76.04', '75.85', '77.77', '[BOLD] 77.92 (15%)'], ['ppceme', 'Yes', '76.73', '76.87', '77.61', '77.65', '77.76', '78.85', '[BOLD] 79.05 (19%∗)']] | Nonetheless, domain adaptation can help: Fema improves performance by 1.3% on the PPCMBE data, and by 3.8% on the unnormalized PPCEME data. Spelling normalization also helps, improving the baseline systems by more than 2.5%. The combination of spelling normalization and domain adaptation gives an overall improvement in... |
Part-of-Speech Tagging for Historical English | 1603.03144 | Table 5: Tagging accuracies of adaptation of our baseline SVM tagger from the PTB to the PPCEME in ablation experiments. | ['Feature set', 'IV', 'OOV', 'All'] | [['All features', '81.68', '48.96', '74.15'], ['– word context', '79.69', '38.62', '70.23'], ['– prefix', '81.61', '46.11', '73.43'], ['– suffix', '81.36', '38.13', '71.40'], ['– affix', '81.22', '34.40', '70.44'], ['– orthographic', '81.68', '48.92', '74.14']] | Word context features are important for obtaining good accuracies on both IV and OOV tokens. Affix features, particularly suffix features, are crucial for the OOV tokens. The orthographic features are shown to be nearly irrelevant, as long as affix features are present. Overall, the high percentage of OOV tokens can be... |
Part-of-Speech Tagging for Historical English | 1603.03144 | Table 7: Tagging accuracies of domain adaptation models from the PTB to the PPCEME. | ['System', 'IV', 'OOV', 'All'] | [['SVM', '81.68', '48.96', '74.15'], ['SCL', '82.01', '55.45', '75.89'], ['Brown', '81.81', '56.76', '76.04'], ['word2vec', '81.79', '56.00', '75.85'], ['Fema-single', '82.30', '62.63', '77.77'], ['Fema-attribute', '82.34', '63.16', '77.92']] | Compared against the baseline tagger, Fema-attribute achieves an absolute improvement of 14% in accuracy on OOV tokens. SCL performs slightly better than Brown clustering and word2vec on IV tokens, but worse on OOV tokens. By incorporating metadata attributes, Fema-attribute performs better than Fema-single on OOV toke... |
A language score based output selection methodfor multilingual speech recognition | 2005.00851 | Table 3: WER results | ['[EMPTY]', '1st decoding with LM0', 'Rescoring with LM2', 'Rescoring with LM1', 'Proposed method'] | [['Test-clean', '18.8', '-', '11.50', '11.50'], ['Test-other', '33.35', '-', '22.59', '22.59'], ['Reading-test', '3.84', '2.42', '-', '2.42'], ['Conversation-test', '20.54', '19.00', '-', '19.2'], ['YouTube-test', '24.55', '21.57', '-', '21.83'], ['VLSP2018', '10.12', '8.16', '-', '8.16']] | One pass decoding with the proposed method To experiment that our method can automatically select the best result without having to identify the input language, we did two experiments. The first experiment was to simulate the case of the input language is known in advance. We firstly decoded all 6 testing sets to obtai... |
A language score based output selection methodfor multilingual speech recognition | 2005.00851 | Table 1: Audio datasets | ['[BOLD] Set', '[BOLD] Language', '[BOLD] Corpus', '[BOLD] Hours', '[BOLD] Speakers', '[BOLD] Sentences', '[BOLD] Foreign words', '[BOLD] Style'] | [['Training', 'English', 'Librispeech-360hr', '363.6', '921', '104,014', '[EMPTY]', 'Reading'], ['Training', 'English', 'Mozilla CommonVoice', '780', '31,858', '644,120', '-', 'Reading'], ['Training', 'English', 'TED-LIUM', '452', '2,351', '268,263', '-', 'Spontaneous'], ['Training', 'Vietnamese', 'VinBDI-set', '2,500'... | 4 testing sets were used to evaluate for Vietnamese performance. They were named Reading-test, Conversation-test, YouTube-test, and VLSP2018. For English evaluation, testing sets were Test-clean and Test-other [librispeech]. Those sets were randomly selected from the same raw data at the beginning to construct training... |
A language score based output selection methodfor multilingual speech recognition | 2005.00851 | Table 2: Language model evaluation | ['[EMPTY]', '[BOLD] Multilinguage LM0', '[BOLD] English LM1', '[BOLD] Vietnamese LM2'] | [['[BOLD] Test-clean', '569.3', '187.1', '-'], ['[BOLD] Test-other', '522.2', '174.3', '-'], ['[BOLD] Reading-test', '136.6', '-', '87.2'], ['[BOLD] Conversation-test', '95.2', '-', '62.7'], ['[BOLD] YouTube-test', '199.5', '-', '111.4'], ['[BOLD] VLSP2018', '75.7', '-', '47.5']] | After building LM1 and LM2 as above, the multilingual model LM0 was created by linearly interpolating LM1 and LM2 with α=0.5. LM0 was further cut off all n-grams that have a probability of less than 2E-8 to reduce its size and speed up the first decoding step. After this step, the size of the unpruned LM0 from 800MB ha... |
Identifying Semantic Divergences in Parallel Text without Annotations | 1803.11112 | Table 2: Intrinsic evaluation on crowdsourced semantic equivalence vs. divergence testsets. We report overall F-score, as well as precision (P), recall (R) and F-score (F) for the equivalent (+) and divergent (-) classes separately. Semantic similarity yields better results across the board, with larger improvements on... | ['[BOLD] Divergence Detection [BOLD] Approach', '[BOLD] OpenSubtitles +P', '[BOLD] OpenSubtitles +R', '[BOLD] OpenSubtitles +F', '[BOLD] OpenSubtitles -P', '[BOLD] OpenSubtitles -R', '[BOLD] OpenSubtitles -F', '[BOLD] OpenSubtitles Overall F', '[BOLD] Common Crawl +P', '[BOLD] Common Crawl +R', '[BOLD] Common Crawl +F... | [['Sentence Embeddings', '65', '60', '62', '56', '61', '58', '60', '78', '58', '66', '52', '[BOLD] 74', '61', '64'], ['MT Scores (1 epoch)', '67', '53', '59', '54', '68', '60', '60', '54', '65', '59', '17', '11', '14', '42'], ['Non-entailment', '58', '78', '66', '53', '30', '38', '54', '73', '49', '58', '48', '72', '57... | The break down per class shows that both equivalent and divergent examples are better detected. The improvement is larger for divergent examples with gains of about 10 points for F-score for the divergent class, when compared to the next-best scores. |
Data Diversification: An Elegant Strategy For Neural Machine Translation | 1911.01986 | Table 13: The average value of E[1N∑NipMi(yij|yi | ['[EMPTY]', '[BOLD] En-De', '[BOLD] De-En', '[BOLD] En-Fr', '[BOLD] Fr-En'] | [['[BOLD] Teacher models [ITALIC] Mi', '[BOLD] Teacher models [ITALIC] Mi', '[BOLD] Teacher models [ITALIC] Mi', '[BOLD] Teacher models [ITALIC] Mi', '[BOLD] Teacher models [ITALIC] Mi'], ['E[1 [ITALIC] N∑ [ITALIC] NipMi( [ITALIC] yij| [ITALIC] yi< [ITALIC] j)]', '0.76', '0.78', '0.76', '0.79'], ['E[max [ITALIC] y... | Through certain experiments we will discuss later, we observe that our method is able to achieve high performance gain under the following conditions: Both sides of Eqn. This can be realized when the teacher models are well-trained from the parallel data. ,N} and yij=yej∀yj. First, when the constituent models Mi are we... |
Data Diversification: An Elegant Strategy For Neural Machine Translation | 1911.01986 | Table 4: Performances on low-resource translations. As done by flores, the from-English pairs are measured in tokenized BLEU, while to-English are measured in detokenized SacreBLEU. | ['[BOLD] Method', '[BOLD] En-Ne', '[BOLD] Ne-En', '[BOLD] En-Si', '[BOLD] Si-En'] | [['flores', '4.3', '7.6', '1.0', '6.7'], ['Data Diversification', '[BOLD] 5.7', '[BOLD] 8.9', '[BOLD] 2.2', '[BOLD] 8.2']] | Specifically, the method achieves 5.7, 8.9, 2.2, and 8.2 BLEU for En-Ne, Ne-En, En-Si and Si-En tasks, respectively. In absolute terms, these are 1.4, 1.3, 2.2 and 1.5 BLEU improvements over the baseline model (flores). Without any monolingual data involved, our method establishes a new state of the art in all four low... |
Data Diversification: An Elegant Strategy For Neural Machine Translation | 1911.01986 | Table 9: BLEU scores for models with and without back-translation (BT) on the IWSLT’14 English-German (En-De), German-English (De-En) and WMT’14 En-De tasks. Column |D| shows the total data used in back-translation compared to the original parallel data. | ['[BOLD] Task', '[BOLD] No back-translation [BOLD] Baseline', '[BOLD] No back-translation [BOLD] Ours', '[BOLD] With back-translation | [ITALIC] D|', '[BOLD] With back-translation [BOLD] Baseline', '[BOLD] With back-translation [BOLD] Ours'] | [['IWSLT’14 En-De', '28.6', '30.6', '29×', '30.0', '[BOLD] 31.8'], ['IWSLT’14 De-En', '34.7', '37.0', '29×', '37.1', '[BOLD] 38.5'], ['WMT’14 En-De', '29.3', '30.7', '2.4×', '30.8', '[BOLD] 31.8']] | Our method is also complementary to back-translation (BT) (backtranslate_sennrich-etal-2016-improving). To demonstrate this, we conducted experiments on the IWSLT’14 En-De and De-En tasks with extra monolingual data extracted from the WMT’14 En-De corpus. In addition, we also compare our method against the back-transla... |
Data Diversification: An Elegant Strategy For Neural Machine Translation | 1911.01986 | Table 10: WMT’14 English-German (En-De) and English-French (En-Fr) diversity performances in BLEU and Pairwise-BLEU scores. Lower Pairwise-BLEU means more diversity, higher BLEU means better quality. | ['[BOLD] Method', '[BOLD] Pairwise-BLEU [BOLD] En-De', '[BOLD] Pairwise-BLEU [BOLD] En-Fr', '[BOLD] BLEU [BOLD] En-De', '[BOLD] BLEU [BOLD] En-Fr'] | [['Sampling', '24.1', '32.0', '37.8', '46.5'], ['Beam', '73.0', '77.1', '69.9', '79.8'], ['Div-beam', '53.7', '64.9', '60.0', '72.5'], ['hMup', '50.2', '64.0', '63.8', '74.6'], ['Human', '35.5', '46.5', '69.6', '76.9'], ['Ours', '57.1', '70.1', '69.5', '77.0']] | By just training multiple models with different seeds, their translations from the training set yield only 14% and 22% duplicates for En-De and En-Fr. These results may be surprising as we might expect more duplicates. To evaluate the diversity the teacher models used in data diversification, we compare them with the B... |
Data Diversification: An Elegant Strategy For Neural Machine Translation | 1911.01986 | Table 11: Improvements of data diversification under conditions with- and without- dropout in the IWSLT’14 English-German and German-English. | ['[BOLD] Task', '[BOLD] Baseline', '[BOLD] Ours', '[BOLD] Gain'] | [['[BOLD] Dropout=0.3', '[BOLD] Dropout=0.3', '[BOLD] Dropout=0.3', '[BOLD] Dropout=0.3'], ['En-De', '28.6', '30.1', '+1.5 (5%)'], ['De-En', '34.7', '36.5', '+1.8 (5%)'], ['[BOLD] Dropout=0', '[BOLD] Dropout=0', '[BOLD] Dropout=0', '[BOLD] Dropout=0'], ['En-De', '25.7', '27.5', '+1.8 (6%)'], ['De-En', '30.7', '32.5', '... | First, given that parameter initialization affects diversity, it is logical to assume that dropout will magnify the diversification effects. However, our empirical results did not support this. We ran experiments to test whether non-zero dropout magnify the improvements of our method over the baseline. We trained the s... |
Data Diversification: An Elegant Strategy For Neural Machine Translation | 1911.01986 | Table 12: Improvements of data diversification under conditions maximum likelihood and beam search in the IWSLT’14 English-German and German-English. | ['[BOLD] Task', '[BOLD] Baseline', '[BOLD] Ours [BOLD] Beam=1', '[BOLD] Ours [BOLD] Beam=5'] | [['En-De', '28.6', '30.3', '30.4'], ['De-En', '34.7', '36.6', '36.8']] | We hypothesized that beam search will generate more diverse synthetic translations of the original dataset, thus increases the diversity and improves generalization. We tested this hypothesis by using greedy decoding (beam size=1) to generate the synthetic data and compare its performance against beam search (beam size... |
Do Not Have Enough Data? Deep Learning to the Rescue! | 1911.03118 | Table 6: Accuracy of LAMBADA with or without label vs. unlabeled data for ATIS dataset with 5 samples per class. Significant improvement for BERT and SVM classifiers (*McNemar, p−value<0.01). | ['Classifier', 'Base.', 'Unlab. Data', 'Unlab. GPT', 'LAMBADA'] | [['BERT', '53.3', '54.5', '73.2', '[BOLD] 75.7*'], ['SVM', '35.6', '23.5', '47.2', '[BOLD] 56.5*'], ['LSTM', '29.0', '[BOLD] 40.1*', '23.2', '33.7']] | Our augmentation framework does not require additional unlabeled data. As such, it can be applied when unlabeled data is unavailable or costly. To test the expected LAMBADA performance in such a scenario, we compared it to a semi-supervised approach [ruder2018strong] that uses unlabeled data. To create an unlabeled dat... |
Do Not Have Enough Data? Deep Learning to the Rescue! | 1911.03118 | Table 5: Accuracy of LAMBADA vs. other generative approaches over all datasets and classifiers. LAMBADA is statistically (* McNemar, p−value<0.01) superior to all models on each classifier and each dataset (on par to EDA with SVM on TREC). | ['Dataset', '[EMPTY]', 'BERT', 'SVM', 'LSTM'] | [['ATIS', 'Baseline', '53.3', '35.6', '29.0'], ['ATIS', 'EDA', '62.8', '35.7', '27.3'], ['ATIS', 'CVAE', '60.6', '27.6', '14.9'], ['ATIS', 'CBERT', '51.4', '34.8', '23.2'], ['ATIS', 'LAMBADA', '[BOLD] 75.7*', '[BOLD] 56.5*', '[BOLD] 33.7*'], ['TREC', 'Baseline', '60.3', '42.7', '17.7'], ['TREC', 'EDA', '62.6', '[BOLD] ... | S5SSx4SSSx3 Comparison of Generative Models We compared our approach to other leading text generator approaches. In the TREC dataset, the results for BERT are significantly better than all other methods. On the TREC dataset with SVM classifier, our method is on par with EDA. |
Research Paper | 1802.05934 | Table 2. Hyper-parameters which were used in experiments for News20, BBC & BBC-Sports datasets | ['[BOLD] Hyper-parameter', 'News20', 'BBC', 'BBC-Sports'] | [['Batch size', '256', '32', '16'], ['Learning rate', '0.01', '0.01', '0.01'], ['Word vector dim', '300', '300', '300'], ['Latent vector dim ( [ITALIC] m)', '50', '50', '50'], ['# Nearest neighbours ( [ITALIC] k)', '5', '5', '5'], ['Scaling factor ( [ITALIC] λ)', '10−4', '10−4', '10−4'], ['# Epochs per fold', '30', '20... | All experiments were carried on a Dell Precision Tower 7910 server with Quadro M5000 GPU with 8 GB of memory. The word embeddings were randomly initialized and trained along with the model. For testing purposes the algorithm was tested using 10-fold cross-validation scheme. The learning rate is regulated over the train... |
Research Paper | 1802.05934 | Table 1. Dataset Specifications | ['Dataset', 'Train Size', 'Test Size', '# Classes'] | [['News20', '18000', '2000', '20'], ['BBC', '2000', '225', '5'], ['BBC Sports', '660', '77', '5']] | For our experiments, we have chosen three popular publicly-available news classification datasets. The dataset is partitioned almost evenly across 20 different classes: comp.graphics, comp.os.ms-windows.misc, comp.sys.ibm.pc.hardware, comp.sys.mac. hardware, comp. windows.x, rec.autos, rec.motorcycles, rec.sport. baseb... |
Research Paper | 1802.05934 | Table 3. Classification accuracies and F1-Scores for news arcticle classifications for different source and target domains. The first row corresponds to the baseline performance trained on only the target dataset. The following two rows shows the performance of instance-infusion method with and without the usage of pen... | ['[BOLD] METHOD', 'Target Source', 'News20 BBC', 'News20 BBC', 'BBC News20', 'BBC News20', 'BBC Sports BBC', 'BBC Sports BBC'] | [['[BOLD] METHOD', '[EMPTY]', 'Accuracy', 'F1-Score', 'Accuracy', 'F1-Score', 'Accuracy', 'F1-Score'], ['Bi-LSTM (Target Only)', '[EMPTY]', '65.17', '0.6328', '91.33', '0.9122', '84.22', '0.8395'], ['Instance-Infused Bi-LSTM', '[EMPTY]', '76.44', '0.7586', '95.35', '0.9531', '88.78', '0.8855'], ['Instance-Infused Bi-LS... | The source and target datasets are chosen in a manner such that the source dataset is able to provide relevant information. 20Newsgroups contains news articles from all categories, so a good choice for source dataset is BBC which also encompasses articles from similar broad categories. For the same reason BBC also has ... |
Research Paper | 1802.05934 | Table 5. Test Accuracy for proposed model using instances from multiple source datasets with 50% target dataset | ['Dataset', 'Single Source Accuracy', 'Single Source F1-Score', 'Multiple Sources Accuracy', 'Multiple Sources F1-Score'] | [['News20', '61.72', '0.6133', '67.32', '0.6650'], ['BBC', '91.01', '0.9108', '91.41', '0.9120'], ['BBC Sports', '81.72', '0.7990', '82.81', '0.8027']] | Target Dataset Reduction with Multiple Source. In this section, we design an experimental setup in which only 0.5 fraction of the target dataset is utilized and study the influence of multiple source dataset infusion. The results improve as and when more source datasets are used in the infusion process. This can be eff... |
Research Paper | 1802.05934 | Table 6. Comparison of results using other learning schemes on News20, BBC and BBC Sports datasets. The proposed model using a deep learning model as a baseline achieves competitive performance for all the three datasets. | ['Model', 'News20 Accuracy', 'News20 F1-Score', 'BBC Accuracy', 'BBC F1-Score', 'BBC Sports Accuracy', 'BBC Sports F1-Score'] | [['kNN-ngrams', '35.25', '0.3566', '74.61', '0.7376', '94.59', '0.9487'], ['Multinomial NB-bigram', '[BOLD] 79.21', '[BOLD] 0.7841', '95.96', '0.9575', '95.95', '0.9560'], ['SVM-bigram', '75.04', '0.7474', '94.83', '0.9456', '93.92', '0.9393'], ['SVM-ngrams', '78.60', '0.7789', '95.06', '0.9484', '95.95', '0.9594'], ['... | Comparative Study. Literature involving these datasets mainly focus on non-deep learning based approaches. Thereby, we compare our results with some popular conventional learning techniques. For the k-NN-ngram experiments, the number of nearest neighbours k was set to 5. For the mentioned datasets, conventional models ... |
AMR Parsing as Sequence-to-Graph Transduction | 1905.08704 | Table 1: Hyper-parameter settings | ['[BOLD] GloVe.840B.300d embeddings dim', '[BOLD] GloVe.840B.300d embeddings 300'] | [['[BOLD] BERT embeddings', '[BOLD] BERT embeddings'], ['source', 'BERT-Large-cased'], ['dim', '1024'], ['[BOLD] POS tag embeddings', '[BOLD] POS tag embeddings'], ['dim', '100'], ['[BOLD] Anonymization indicator embeddings', '[BOLD] Anonymization indicator embeddings'], ['dim', '50'], ['[BOLD] Index embeddings', '[BOL... | Both encoder and decoder embedding layers have GloVe and POS tag embeddings as well as CharCNN, but their parameters are not tied. We apply dropout (dropout_rate = 0.33) to the outputs of each module. |
AMR Parsing as Sequence-to-Graph Transduction | 1905.08704 | Table 3: Fine-grained F1 scores on the AMR 2.0 test set. vN’17 is van Noord and Bos (2017b); L’18 is Lyu and Titov (2018); N’19 is Naseem et al. (2019). | ['Metric', 'vN’18', 'L’18', 'N’19', 'Ours'] | [['Smatch', '71.0', '74.4', '75.5', '[BOLD] 76.3±0.1'], ['Unlabeled', '74', '77', '[BOLD] 80', '79.0±0.1'], ['No WSD', '72', '76', '76', '[BOLD] 76.8±0.1'], ['Reentrancies', '52', '52', '56', '[BOLD] 60.0±0.1'], ['Concepts', '82', '[BOLD] 86', '[BOLD] 86', '84.8±0.1'], ['Named Ent.', '79', '[BOLD] 86', '83', '77.9±0.2'... | , we assess the quality of each subtask using the AMR-evaluation tools Damonte et al. We see a notable increase on reentrancies, which we attribute to target-side copy (based on our ablation studies in the next section). Significant increases are also shown on wikification and negation, indicating the benefits of using... |
AMR Parsing as Sequence-to-Graph Transduction | 1905.08704 | Table 4: Ablation studies on components of our model. (Scores are sorted by the delta from the full model.) | ['Ablation', 'AMR 1.0', 'AMR 2.0'] | [['Full model', '70.2', '76.3'], ['no source-side copy', '62.7', '70.9'], ['no target-side copy', '66.2', '71.6'], ['no coverage loss', '68.5', '74.5'], ['no BERT embeddings', '68.8', '74.6'], ['no index embeddings', '68.5', '75.5'], ['no anonym. indicator embed.', '68.9', '75.6'], ['no beam search', '69.2', '75.3'], [... | Ablation Study Removing target-side copy also leads to a large drop. Specifically, the subtask score of reentrancies drops down to 38.4% when target-side copy is disabled. Coverage loss is useful with regard to discouraging unnecessary repetitive nodes. In addition, our model benefits from input features such as langua... |
AMR Parsing as Sequence-to-Graph Transduction | 1905.08704 | Table 5: Smatch scores of full models trained and tested based on different node linearization strategies. | ['Node Linearization', 'AMR 1.0', 'AMR 2.0'] | [['Pre-order + Alphanum', '70.2', '76.3'], ['Pre-order + Alignment', '61.9', '68.3'], ['Pure Alignment', '64.3', '71.3']] | Alignments are created using the tool by Pourdamghani et al. Clearly, our linearization strategy leads to much better results than the two alternates. We also tried other traversal strategies such as combining in-order traversal with alphanumerical sorting or alignment-based sorting, but did not get scores even compara... |
Modeling Word Emotion in Historical Language: Quantity Beats Supposed Stability in Seed Word Selection | 1806.08115 | Table 4: Results of the synchronic evaluation in Pearson’s r averaged over all three VAD dimensions. The best system for each seed lexicon and those with statistically non-significant differences (p ≥ 0.05) are in bold. | ['Induction Method', 'Seed Selection', 'SVDPPMI', 'SGNS'] | [['kNN', 'full', '[BOLD] 0.548', '0.487'], ['ParaSimNum', 'full', '[BOLD] 0.557', '0.489'], ['RandomWalkNum', 'full', '[BOLD] 0.544', '0.436'], ['kNN', 'limited', '0.181', '0.166'], ['ParaSimNum', 'limited', '0.249', '0.191'], ['RandomWalkNum', 'limited', '[BOLD] 0.330', '0.181']] | SGNS embeddings are worse than SVDPPMI embeddings for both full and limited seed lexicons. SVDPPMI embeddings seem to be better suited for induction based on the full seed set, leading to the highest observed correlation with ParaSimNum. However, results with other induction algorithms are not significantly different. ... |
Modeling Word Emotion in Historical Language: Quantity Beats Supposed Stability in Seed Word Selection | 1806.08115 | Table 2: Inter-annotator agreement for our English (goldEN) and German (goldDE) gold standard, as well as the lexicon by Warriner13 for comparision; Averaged standard deviation of ratings for each VAD dimension and mean over all dimensions. | ['[EMPTY]', 'Valence', 'Arousal', 'Dominance', 'Mean'] | [['goldEN', '1.20', '1.08', '1.41', '1.23'], ['goldDE', '1.72', '1.56', '2.31', '1.86'], ['Warriner', '1.68', '2.30', '2.16', '2.05']] | We measure inter-annotator agreement (IAA) by calculating the standard deviation (SD) for each word and dimension and averaging these, first, for each dimension alone, and then over these aggregate values, thus constituting an error-based score (the lower the better). In comparison with the lexicon by \newciteWarriner1... |
Neural Aspect and Opinion Term Extraction with Mined Rules as Weak Supervision | 1907.03750 | Table 6: Aspect and opinion term extraction performance. | ['Approach', 'SE14-R Aspect', 'SE14-R Opinion', 'SE14-L Aspect', 'SE14-L Opinion', 'SE15-R Aspect', 'SE15-R Opinion'] | [['BiLSTM-CRF + word2vec', '84.06', '84.59', '73.47', '75.41', '66.17', '68.16'], ['BERT fine-tuning', '84.36', '85.50', '75.67', '79.75', '65.84', '74.21'], ['BERT feature-based', '85.14', '85.74', '76.81', '81.41', '66.84', '73.92'], ['RINANTE+BERT', '[BOLD] 85.51', '[BOLD] 86.82', '[BOLD] 79.93', '[BOLD] 82.09', '[B... | We can see that using BERT yields better performance than using word2vec. RINANTE is still able to further improve the performance when contextual embeddings obtained with BERT are used. |
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