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Assertion Detection in Multi-Label Clinical Text using Scope Localization | 2005.09246 | Table 2: Distribution of Assertion classes in the data. | ['[BOLD] Split', '[BOLD] Dataset-I [BOLD] Max', '[BOLD] Dataset-I [BOLD] Min', '[BOLD] Dataset-I [BOLD] Mean', '[BOLD] Dataset-II [BOLD] Max', '[BOLD] Dataset-II [BOLD] Min', '[BOLD] Dataset-II [BOLD] Mean'] | [['train', '661', '19', '440', '1028', '82', '610'], ['val', '642', '289', '452', '911', '82', '630'], ['test', '560', '228', '432', '968', '336', '642']] | The annotations were done using BRAT tool Rules for annotation were generated after consulting with the Radiologist supervising the annotators. Other Radiologists were consulted to annotate any mentions that were previously unseen or ambiguous and also for the final review. For a fair comparison with the baseline, the ... |
Assertion Detection in Multi-Label Clinical Text using Scope Localization | 2005.09246 | Table 2: Distribution of Assertion classes in the data. | ['[BOLD] Class', '[BOLD] Dataset-I [BOLD] Train', '[BOLD] Dataset-I [BOLD] Val', '[BOLD] Dataset-I [BOLD] Test', '[BOLD] Dataset-II [BOLD] Train', '[BOLD] Dataset-II [BOLD] Val', '[BOLD] Dataset-II [BOLD] Test'] | [['1', '3.36±2.7', '3.23±2.59', '3.39±2.95', '3.48±2.15', '3.38±2.06', '3.48±2.16'], ['2', '2.79±1.36', '2.68±1.25', '2.68±1.07', '3.15±2.26', '3.10±2.18', '3.09±2.13'], ['3', '2.85±1.04', '2.87±0.87', '2.68±0.65', '3.24±2.92', '3.20±1.95', '2.60±1.74'], ['4', '5.05±2.44', '4.5±2.44', '5.0±2.43', '2.19±1.21', '2.60±0.9... | The annotations were done using BRAT tool Rules for annotation were generated after consulting with the Radiologist supervising the annotators. Other Radiologists were consulted to annotate any mentions that were previously unseen or ambiguous and also for the final review. For a fair comparison with the baseline, the ... |
Assertion Detection in Multi-Label Clinical Text using Scope Localization | 2005.09246 | Table 2: Distribution of Assertion classes in the data. | ['[BOLD] Class', '[BOLD] Model [BOLD] Baseline', '[BOLD] Model [BOLD] Baseline', '[BOLD] Model [BOLD] Scope Localization model', '[BOLD] Model [BOLD] Scope Localization model'] | [['[BOLD] Class', '[BOLD] Dataset-I', '[BOLD] Dataset-II', '[BOLD] Dataset-I', '[BOLD] Dataset-II'], ['Present', '0.97', '0.92', '0.90', '0.84'], ['Absent', '0.27', '0.34', '0.84', '0.93'], ['Conditional', '0.39', '0.45', '0.74', '0.65'], ['Hypothetical', '0.76', '0.69', '0.87', '0.75'], ['Possibility', '0.0', '0.07', ... | The annotations were done using BRAT tool Rules for annotation were generated after consulting with the Radiologist supervising the annotators. Other Radiologists were consulted to annotate any mentions that were previously unseen or ambiguous and also for the final review. For a fair comparison with the baseline, the ... |
DisSent: Learning Sentence Representations from Explicit Discourse Relations | 1710.04334 | Table 9: Ngram Bag-of-words baseline sentence embeddings performance on DisSent training task: test recall / precision for each discourse marker on the classification task, and overall accuracy. Average metric reports the weighted average of all classes. | ['Marker', 'All Prec', 'All Rec', 'Books 8 Prec', 'Books 8 Rec', 'Books 5 Prec', 'Books 5 Rec'] | [['and', '60.1', '65.0', '65.6', '70.1', '70.1', '71.4'], ['but', '49.9', '65.3', '55.2', '69.4', '59.7', '69.9'], ['because', '34.7', '10.2', '42.1', '11.1', '42.8', '10.6'], ['if', '54.6', '56.9', '58.8', '56.4', '64.4', '60.0'], ['when', '43.2', '40.1', '52.1', '52.2', '58.4', '54.3'], ['so', '35.5', '11.3', '38.5',... | As a reference point for training task performance we present baseline performance. Note that a model which simply chose the most common class would perform with 21.79% accuracy on the ALL task, 28.35% on the Books 8 task, and 31.87% on the Books 5 task. Using either unigram, bigram and trigram bag of words or Arora et... |
DisSent: Learning Sentence Representations from Explicit Discourse Relations | 1710.04334 | Table 5: Discourse classification task performance: Unweighted average F1 across discourse markers on the test set, and overall accuracy. Ngram-bow is a bag-of-words model built on mixture of ngram features. GloVe-bow averages word embedding with correction to frequency Arora et al. (2017). BiLSTM is the DisSent senten... | ['Model', 'All F1', 'All Acc', 'Books 8 F1', 'Books 8 Acc', 'Books 5 F1', 'Books 5 Acc'] | [['GloVe-bow', '17.1', '41.8', '27.6', '47.3', '41.7', '52.5'], ['Ngram-bow', '28.1', '51.8', '44.0', '58.1', '54.1', '63.3'], ['BiLSTM', '47.2', '67.5', '64.4', '73.5', '72.1', '77.3'], ['BERT', '60.1', '77.5', '76.2', '82.9', '82.6', '86.1']] | , we achieve high levels of test performance for all discourse markers. (Though it is interesting that because, perhaps the conceptually deepest relation, is also systematically the hardest for our model.) The larger the set of discourse markers, the more difficult the task becomes, and we therefore see lower test accu... |
DisSent: Learning Sentence Representations from Explicit Discourse Relations | 1710.04334 | Table 6: SentEval Task Results Using Fixed Sentence Encoder. We report the best results for generalization tasks. † indicates models that we trained. FastSent, FastSent + AE Hill et al. (2016), SkipThought Kiros et al. (2015), SkipThought-LN, DictRep (bow), and InferSent are reported from Conneau et al. (2017). LSMTL i... | ['Model', 'MR', 'CR', 'SUBJ', 'MPQA', 'SST', 'TREC', 'SICK-R', 'SICK-E', 'MRPC'] | [['Self-supervised training methods', 'Self-supervised training methods', 'Self-supervised training methods', 'Self-supervised training methods', 'Self-supervised training methods', 'Self-supervised training methods', 'Self-supervised training methods', 'Self-supervised training methods', 'Self-supervised training meth... | Similar generalization performance was achieved when training on 5, 8, and all 15 discourse markers. |
DisSent: Learning Sentence Representations from Explicit Discourse Relations | 1710.04334 | Table 7: Discourse Generalization Tasks using PDTB: We report test accuracy for sentence embedding and state-of-the-art models. | ['Model', 'IMP', 'MVU'] | [['Sentence Encoder Models', 'Sentence Encoder Models', 'Sentence Encoder Models'], ['SkipThought Kiros et\xa0al. ( 2015 )', '9.3', '57.2'], ['InferSent Conneau et\xa0al. ( 2017 )', '39.3', '84.5'], ['Patterson and Kehler ( 2013 )', '—', '86.6'], ['DisSent Books 5', '40.7', '86.5'], ['DisSent Books 8', '41.4', '[BOLD] ... | Much to our surprise, fine-tuned BERT models are not able to perform better than the BiLSTM sentence encoder model. We leave explorations of this phenomenon to future work. |
DisSent: Learning Sentence Representations from Explicit Discourse Relations | 1710.04334 | Table 10: Corrected GloVe Bag-of-words sentence embeddings performance on DisSent training task: test recall / precision for each discourse marker on the classification task, and overall accuracy. Average metric reports the weighted average of all classes. | ['Marker', 'All Prec', 'All Rec', 'Books 8 Prec', 'Books 8 Rec', 'Books 5 Prec', 'Books 5 Rec'] | [['and', '46.9', '59.4', '52.9', '63.6', '58.0', '64.3'], ['but', '38.1', '57.9', '43.5', '62.3', '48.9', '62.4'], ['because', '24.1', '0.5', '20.2', '0.3', '27.7', '0.47'], ['if', '41.8', '37.1', '46.2', '37.9', '50.5', '38.2'], ['when', '36.8', '25.8', '45.6', '40.0', '58.3', '41.3'], ['so', '37.0', '2.5', '39.5', '2... | As a reference point for training task performance we present baseline performance. Note that a model which simply chose the most common class would perform with 21.79% accuracy on the ALL task, 28.35% on the Books 8 task, and 31.87% on the Books 5 task. Using either unigram, bigram and trigram bag of words or Arora et... |
Exploring Transformers for Large-Scale Speech Recognition | 2005.09684 | Table 4: Results of deeper Transformer models. L denotes the model depth. | ['Model', 'IC', 'Size(M)', '[ITALIC] L', 'Context', 'dev', 'eval'] | [['BLSTM', '–', '55.0', '6', '[-∞,∞]', '19.5', '12.7'], ['LC-BLSTM', '–', '55.0', '6', '[-1, 40]', '20.2', '12.9'], ['[EMPTY]', '✗', '53.5', '12', '[-∞,∞]', '18.4', '11.9'], ['Transformer', '✗', '97.0', '12', '[-∞,∞]', '18.3', '–'], ['[EMPTY]', '✗', '101.7', '24', '[-∞,∞]', '17.8', '11.7'], ['[EMPTY]', '✗', '53.5', '12... | We observe similar trend on the eval set. We also investigated the tradeoff between increasing the dimension of hidden state in self-attention layers and increasing the depth the model. For the offline 12-layer Transformer, we increased dk to 960, which resulted in a model with 97 million parameters. However, we only a... |
Exploring Transformers for Large-Scale Speech Recognition | 2005.09684 | Table 1: Results of the Transformers with convolution layers in the offline mode. The number of heads is 4, and the number of layers is 12. IC stands for interleaved 1D convolution. All models have around 50 million (M) model parameters. | ['Model', 'IC', 'Size(M)', 'Encode layer', '[ITALIC] dk', 'dev'] | [['[EMPTY]', '✗', '51.5', 'Linear', '620', '34.7'], ['Transformer', '✓', '50.0', 'Linear', '512', '20.2'], ['[EMPTY]', '✓', '51.5', 'VGG', '512', '19.6'], ['[EMPTY]', '✗', '52.0', 'VGG', '620', '19.4']] | The self-attention operation cannot maintain the monotonicity of input sequence, which is particularly harmful for time-synchronous acoustic model such as the hybrid model studies in this paper. The kernel size for the 1D convolution is 3, while the VGG net has 4 layers of 2D convolutions with 3x3 filters. When the VGG... |
Exploring Transformers for Large-Scale Speech Recognition | 2005.09684 | Table 2: Results of the 12-layer Transformer model with different number of attention heads. VGG net was used as the encoding layer for all the Transformers. N denotes the number of attention heads, and dk is the model dimension as in Eq(1). | ['Model', 'IC', 'Size (M)', '[ITALIC] N', '[ITALIC] dk', 'dev'] | [['[EMPTY]', '✓', '50.5', '4', '512', '19.6'], ['[EMPTY]', '✓', '50.5', '8', '512', '19.7'], ['[EMPTY]', '✓', '50.5', '16', '512', '18.8'], ['Transformer', '✗', '52.0', '4', '620', '19.4'], ['[EMPTY]', '✗', '53.5', '8', '624', '18.4'], ['[EMPTY]', '✗', '53.5', '16', '624', '18.6'], ['BLSTM', '–', '55.0', '–', '–', '19.... | With the interleaved convolution, the Transformer with 16 attention heads achieved the lowest WER, while for the vanilla Transformer, 8 attention heads are sufficient. We did not further increase the number of the attention heads in our experiments due to the memory constraint. Compared to the BLSTM with around 55 mill... |
Exploring Transformers for Large-Scale Speech Recognition | 2005.09684 | Table 3: Results of streaming Transformer models. The number of layers is 12. | ['Model', 'IC', 'Size (M)', '[ITALIC] N', '[ITALIC] dk', 'Context', 'dev'] | [['[EMPTY]', '✓', '50.5', '16', '512', '[-∞,∞]', '18.8'], ['[EMPTY]', '✓', '50.5', '16', '512', '[-∞,16]', '20.6'], ['[EMPTY]', '✓', '50.5', '16', '512', '[-∞,28]', '20.7'], ['[EMPTY]', '✓', '50.5', '16', '512', '[-∞,40]', '20.0'], ['Transformer', '✗', '53.5', '8', '624', '[-∞,∞]', '18.4'], ['[EMPTY]', '✗', '53.5', '8'... | The previous experiments focused on the offline scenario. In this section, we evaluate the accuracy of Transformers in the steaming condition. For instance, [−∞,40] corresponds to looking ahead 3 frames for each self-attention layer in a 12-layer Transformer without interleaved convolution, with an additional 4 frames ... |
A Multi-Type Multi-Span Network for Reading Comprehension that Requires Discrete Reasoning | 1908.05514 | Table 3: Ablation tests of different architecture choices using MTMSN\textscLARGE. | ['Model', 'EM', 'F1'] | [['MTMSN', '76.7', '80.5'], ['w/o Q/P Vectors', '75.1', '79.2'], ['w/o CLS Vector', '74.0', '78.4'], ['Q/P Vectors Using Last Hidden', '76.5', '80.2'], ['w/o Gated Span Prediction', '75.8', '79.7'], ['Combine Add/Sub with Negation', '75.5', '79.4']] | S4SS3SSS0Px2 Architecture ablation First, we investigate the effects of some “global vectors” used in our model. Specifically, we find that removing the question and passage vectors from all involved computation leads to 1.3 % drop on F1. Ablating the representation of [CLS] token leads to even worse results. We also t... |
A Multi-Type Multi-Span Network for Reading Comprehension that Requires Discrete Reasoning | 1908.05514 | Table 1: The performance of MTMSN and other competing approaches on DROP dev and test set. | ['Model', 'Dev EM', 'Dev F1', 'Test EM', 'Test F1'] | [['Heuristic Baseline\xa0Dua et\xa0al. ( 2019 )', '4.28', '8.07', '4.18', '8.59'], ['Semantic Role Labeling\xa0Carreras and Màrquez ( 2004 )', '11.03', '13.67', '10.87', '13.35'], ['BiDAF\xa0Seo et\xa0al. ( 2017 )', '26.06', '28.85', '24.75', '27.49'], ['QANet+ELMo\xa0Yu et\xa0al. ( 2018 )', '27.71', '30.33', '27.08', ... | MTMSN outperforms all existing approaches by a large margin, and creates new state-of-the-art results by achieving an EM score of 75.85 and a F1 score of 79.88 on the test set. Since our best model utilizes BERT\textscLARGE as encoder, we therefore compare MTMSN\textscLARGE with the NABERT\textscLARGE baseline. As we c... |
A Multi-Type Multi-Span Network for Reading Comprehension that Requires Discrete Reasoning | 1908.05514 | Table 2: Ablation tests of base and large models on the DROP dev set. | ['Model', 'BASE EM', 'BASE F1', 'LARGE EM', 'LARGE F1'] | [['MTMSN', '68.2', '72.8', '76.7', '80.5'], ['w/o Add/Sub', '46.7', '51.3', '53.8', '58.0'], ['w/o Count', '62.5', '66.4', '71.8', '75.6'], ['w/o Negation', '59.4', '63.6', '67.2', '70.9'], ['w/o Multi-Span', '67.5', '70.7', '75.6', '78.4'], ['w/o Reranking', '66.9', '71.2', '74.9', '78.7']] | S4SS3SSS0Px1 Component ablation To analyze the effect of the proposed components, we conduct ablation studies on the development set. Predicting count numbers is also an important component that contributes nearly 5% gain on both metrics. Moreover, enhancing the model with the negation type significantly increases the ... |
A Multi-Type Multi-Span Network for Reading Comprehension that Requires Discrete Reasoning | 1908.05514 | Table 4: Performance breakdown of NABERT\textscLARGE and MTMSN\textscLARGE by gold answer types. | ['Type', '(%)', 'NABERT EM', 'NABERT F1', 'MTMSN EM', 'MTMSN F1'] | [['Date', '1.6', '55.7', '60.8', '55.7', '69.0'], ['Number', '61.9', '63.8', '64.0', '80.9', '81.1'], ['Single Span', '31.7', '75.9', '80.6', '77.5', '82.8'], ['Multi Span', '4.8', '0', '22.7', '25.1', '62.8']] | Performance breakdown We now provide a quantitative analysis by showing performance breakdown on the development set. Moreover, significant improvements are also obtained in the multi-span category, where the F1 score increases by more than 40 points. This result further proves the validity of our multi-span extraction... |
A Multi-Type Multi-Span Network for Reading Comprehension that Requires Discrete Reasoning | 1908.05514 | Table 5: Performance breakdown of NABERT\textscLARGE and MTMSN\textscLARGE by predicted answer types. | ['Type', 'NABERT (%)', 'NABERT EM', 'NABERT F1', 'MTMSN (%)', 'MTMSN EM', 'MTMSN F1'] | [['Span', '43.0', '67.9', '74.2', '42.7', '72.2', '81.0'], ['Add/Sub', '43.6', '62.0', '62.1', '32.4', '78.1', '78.2'], ['Count', '13.4', '62.4', '62.4', '13.4', '70.4', '70.4'], ['Negation', '0', '0', '0', '11.5', '96.3', '96.3']] | As shown in the Table, the main improvements are due to the addition/subtraction and negation types. We conjecture that there are two reasons for these improvements. First, our proposed expression reranking mechanism helps validate candidate expressions. Second, a new inductive bias that enables the model to perform lo... |
Vector Embedding of Wikipedia Concepts and Entities | 1702.03470 | Table 4: Comparing the results in Phrase Similarity dataset for common entries between all approaches. Rho is Spearmans’s correlation. | ['Datasets #', 'Datasets Dataset Name', 'Datasets #Pairs', 'Wikipedia Miner Rho', 'HitSim Rho', 'ConVec Rho', 'ConVec (Heuristic) Rho', 'ConVec (Only Anchors) Rho'] | [['1', 'WS-REL', '130', '0.6662', '0.5330', '0.6022', '0.6193', '0.6515'], ['2', 'SIMLEX', '406', '0.2405', '0.3221', '0.3011', '0.3087', '0.2503'], ['3', 'WS-MAN ', '224', '0.6762', '0.6854', '0.6331', '0.6371', '0.6554'], ['4', 'WS-411 ', '314', '0.7311', '0.7131', '0.7126', '0.7136', '0.7308'], ['5', 'WS-SIM', '108'... | We also compared the results with another structural based similarity approach called HitSim The comparable result of our approach to structural based methods is another proof that we could embed the Wikipedia link structure properly. The result of heuristic based approach is slightly better than our base model. This s... |
Vector Embedding of Wikipedia Concepts and Entities | 1702.03470 | Table 2: Comparing the results of three different versions of ConVec (trained on Wikipedia 2.1B tokens) with Google Freebase pre-trained vectors over Google 100B tokens news dataset in the Phrase Analogy task. The Accuracy (All), shows the coverage and performance of each approach for answering questions. The accuracy ... | ['Embedding Name', '#phrases', 'Accuracy (All) #found', 'Accuracy (All) Accuracy', 'Accuracy (Commons) #found', 'Accuracy (Commons) Accuracy'] | [['Google Freebase', 'Top 30,000', '1048', '55.7%', '89', '52.8%'], ['Google Freebase', 'Top 300,000', '1536', '47.0%', '800', '48.5%'], ['Google Freebase', 'Top 3,000,000', '1838', '42.1%', '1203', '42.7%'], ['ConVec', 'Top 30,000', '202', '81.7%', '89', '82.0%'], ['ConVec', 'Top 300,000', '1702', '68.0%', '800', '72.... | The first accuracy is to compare the coverage and performance of each approach over the all questions in the test dataset ( Accuracy All). The second accuracy is to compare the methods over only common questions (Accuracy commons). Only common questions between each method are used to compare the Accuracy for Commons s... |
Vector Embedding of Wikipedia Concepts and Entities | 1702.03470 | Table 3: Comparing the results in Phrase Similarity dataset. Rho is Spearman’s correlation to the human evaluators. !Found is the number of pairs not found in each approach dataset. | ['Datasets #', 'Datasets Dataset Name', 'Datasets #Pairs', 'Wikipedia Miner !Found', 'Wikipedia Miner Rho', 'Google Freebase !Found', 'Google Freebase Rho', 'ConVec !Found', 'ConVec Rho', 'ConVec (Heuristic) !Found', 'ConVec (Heuristic) Rho'] | [['1', 'WS-REL ', '251', '114', '0.6564', '87', '0.3227', '104', '0.5594', '57', '0.5566'], ['2', 'SIMLEX ', '961', '513', '0.2166', '369', '0.1159', '504', '0.3406', '357', '0.2152'], ['3', 'WS-SIM ', '200', '83', '0.7505', '58', '0.4646', '81', '0.7524', '41', '0.6101'], ['4', 'RW ', '1182', '874', '0.2714', '959', '... | In these datasets, each row consists of two words with their relatedness assigned by the human. The Spearman’s correlation is used for comparing the result of different approaches with the human evaluated results. These datasets contain words and not the Wikipedia concepts. We replaced all the words in these datasets w... |
Igbo-English Machine Translation:An Evaluation Benchmark | 2004.00648 | Table 3: Data Sources and Counts | ['[BOLD] Source', '[BOLD] Sentences', '[BOLD] Tokens', '[BOLD] UniqToks'] | [['eze-goes-to-school.txt', '1272', '25413', '2616'], ['mmadu-ka-a-na-aria.txt', '2023', '39731', '3292'], ['bbc-igbo.txt', '34056', '566804', '28459'], ['igbo-radio.txt', '5131', '191450', '13391'], ['jw-ot-igbo.txt', '32251', '712349', '13417'], ['jw-nt-igbo.txt', '10334', '253806', '6731'], ['jw-books.txt', '142753'... | A large chunk of the data is collected from the Jehova’s Though we included the Bible, more contemporary contents (books and magazine e.g. Teta! (Awake!), Ulo Nche! (WatchTower)) were the main focus. This phase is still on-going but we have so far collected and cleaned ≈ It is important to point out that we have also c... |
Igbo-English Machine Translation:An Evaluation Benchmark | 2004.00648 | Table 2: Splits of the Benchmark Evaluation Parallel Data | ['[BOLD] Type', '[BOLD] Sent pairs', '[BOLD] Sources'] | [['[ITALIC] Igbo-English', '5,836', 'https://www.bbc.com/igbo'], ['[ITALIC] English-Igbo', '5,748', 'Mostly from local newspapers (e.g. Punch)'], ['[ITALIC] Total', '11,584', '[EMPTY]']] | To achieve the objectives above, the task was broken down in the following phases: Phase 1: Raw data collection and pre-processing : This phase is to produce cleaned and pre-processed a minimum 10,000 sentences: 5,000 English and 5,000 Igbo. It involved the collection, cleaning and pre-processing (normalisation, diacri... |
Igbo-English Machine Translation:An Evaluation Benchmark | 2004.00648 | Table 2: Splits of the Benchmark Evaluation Parallel Data | ['[BOLD] Evaluation Splits', '[BOLD] IG-EN', '[BOLD] EN-IG'] | [['[ITALIC] Development Set', '5000', '5000'], ['[ITALIC] Test set', '500', '500'], ['[ITALIC] Hidden Test', '336', '248']] | To achieve the objectives above, the task was broken down in the following phases: Phase 1: Raw data collection and pre-processing : This phase is to produce cleaned and pre-processed a minimum 10,000 sentences: 5,000 English and 5,000 Igbo. It involved the collection, cleaning and pre-processing (normalisation, diacri... |
Integrate Image Representation to Text Model on Sentence Level: a Semi-supervised Framework | 1912.00336 | Table 5: Result on SICK. Here, * stands for multimodal NLU models. | ['[BOLD] Models', '[BOLD] Acc(%)'] | [['*Cap2Both 2018:NAACL_sentence_visual', '81.7'], ['BERT', '90.4'], ['BERT + STVF', '[BOLD] 90.6']] | Similar with performance on SNLI, we can also see the framework bring improvement to SOTA NLU model, and achieve higher performance after integrate to pre-trained NLU models. |
Integrate Image Representation to Text Model on Sentence Level: a Semi-supervised Framework | 1912.00336 | Table 1: Results on SemEval 2018 Task 11 (MCScript). Here, models with * are ensemble models. | ['[BOLD] Models', '[BOLD] Dev', '[BOLD] Test'] | [['TriAN', '82.78', '81.08'], ['TriAN + ConceptNet', '83.84', '81.94'], ['*TriAN + ConceptNet', '85.27', '83.84'], ['Integrated TriAN', '83.90', '82.10'], ['Integrated TriAN + ConceptNet', '[BOLD] 84.42', '[BOLD] 83.12'], ['*Integrated TriAN + ConceptNet', '[BOLD] 85.94', '[BOLD] 84.41']] | Integrated Reading Comprehension: Table. After integrating the semi-supervised visual integration framework, the TriAN model outperforms previous SOTA performance both on single and ensemble condition. Because we could not access the test data, we verify the improvement of the framework on develop set and see statistic... |
Integrate Image Representation to Text Model on Sentence Level: a Semi-supervised Framework | 1912.00336 | Table 2: Results on six most frequent question types. | ['[BOLD] Models', '[BOLD] y/n', '[BOLD] what', '[BOLD] why', '[BOLD] who', '[BOLD] where', '[BOLD] when'] | [['TriAN', '81.4', '85.4', '[BOLD] 84.9', '89.7', '84.7', '78.9'], ['Integrated', '[BOLD] 81.6', '[BOLD] 86.3', '84.4', '89.7', '[BOLD] 85.3', '78.9']] | Analysis by Question Categories: To figure out how the semi-supervised visual integration framework helps reading comprehension, we perform an analysis of performance change on six most frequent question types. The framework does not help answering abstract why, who and when questions, which supports our hypothesis tha... |
Integrate Image Representation to Text Model on Sentence Level: a Semi-supervised Framework | 1912.00336 | Table 3: Result of SemEval 2018 Task 11 (MCScript) on different train/test image bases. | ['[BOLD] Train Base', '[BOLD] Test Base', '[BOLD] Test'] | [['MSCOCO', 'MSCOCO', '83.12'], ['MSCOCO', 'Flicker 30k', '83.09'], ['Flicker 30k', 'MSCOCO', '82.81'], ['Flicker 30k', 'Flicker 30k', '82.81']] | In order to study the effect of image memory base size on experimental results, we alternate image base in train and test process. As shown in Table. When we shrink the test image memory base to the Flicker30k, the integrated model performance reduces, but is still higher than using 30k image both in train and test. Mo... |
Integrate Image Representation to Text Model on Sentence Level: a Semi-supervised Framework | 1912.00336 | Table 4: Result on SNLI. Here, * stands for multimodal NLU models. | ['[BOLD] Models', '[BOLD] Test'] | [['*GroundSent 2018:NAACL_sentence_visual', '76.1'], ['*Picturebook hinton:2018', '86.5'], ['ESIM + ELMo SNLI_ELMO', '88.7'], ['300D DMAN SNLI_MAN', '88.8'], ['SLRC SNLI_SLRC', '89.1'], ['LMTransformer SNLI_2', '89.9'], ['MT-DNN SNLI_1', '91.1'], ['BERT', '91.2'], ['BERT + STVF', '[BOLD] 91.3']] | Integrated NLI Model: In Table we shows the results of integrating the mechanism to language inference model based on BERT. We firstly report the state-of-the-art performance of BERT-large model (91.2%) on the SNLI dataset. Based on the trained BERT-large encoder, we then train the integrated visualization language inf... |
Improving Sparse Word Representations with Distributional Inference for Semantic Composition | 1608.06794 | Table 3: Effect of the magnitude of the shift parameter k in SPPMI on the word similarity tasks. Boldface means best performance per dateset. | ['Apt [BOLD] s', '[BOLD] MEN [ITALIC] without DI', '[BOLD] MEN [ITALIC] with DI', '[BOLD] SimLex-999 [ITALIC] without DI', '[BOLD] SimLex-999 [ITALIC] with DI', '[BOLD] WordSim-353 (rel) [ITALIC] without DI', '[BOLD] WordSim-353 (rel) [ITALIC] with DI', '[BOLD] WordSim-353 (sub) [ITALIC] without DI', '[BOLD] Wor... | [['[ITALIC] [ITALIC] k=1', '0.54', '0.52', '0.31', '0.30', '0.34', '0.27', '0.62', '0.60'], ['[ITALIC] [ITALIC] k=5', '0.64', '0.65', '0.35', '[BOLD] 0.36', '0.56', '0.51', '0.74', '0.73'], ['[ITALIC] [ITALIC] k=10', '0.63', '0.66', '0.35', '[BOLD] 0.36', '0.56', '0.55', '0.75', '0.74'], ['[ITALIC] [ITALIC] k=40', ... | For the Apt model, a value of k=40 performs best (except for SimLex-999, where smaller shifts give better results), with a performance drop-off for larger shifts. In our experiments we find that a shift of k=1 results in top performance for the untyped vector space model. It appears that shifting the PPMI scores in the... |
Improving Sparse Word Representations with Distributional Inference for Semantic Composition | 1608.06794 | Table 1: Example feature spaces for the lexemes white and clothes extracted from the dependency tree of Figure 1. Not all features are displayed for space reasons. Offsetting ¯¯¯¯¯¯¯¯¯¯¯amod:shoes by amod results in an empty dependency path, leaving just the word co-occurrence :shoes as feature. | ['[ITALIC] [BOLD] white [BOLD] Distributional Features', '[ITALIC] [BOLD] white [BOLD] Offset Features (by amod)', '[ITALIC] [BOLD] white [BOLD] Co-occurrence Count', '[ITALIC] [BOLD] clothes [BOLD] Distributional Features', '[ITALIC] [BOLD] clothes [BOLD] Co-occurrence Count'] | [['¯¯¯¯¯¯¯¯¯¯¯amod: [ITALIC] shoes', ': [ITALIC] shoes', '1', 'amod: [ITALIC] clean', '1'], ['¯¯¯¯¯¯¯¯¯¯¯amod:¯¯¯¯¯¯¯¯¯¯¯dobj: [ITALIC] bought', '¯¯¯¯¯¯¯¯¯¯¯dobj: [ITALIC] bought', '1', '¯¯¯¯¯¯¯¯¯¯¯dobj: [ITALIC] like', '1'], ['¯¯¯¯¯¯¯¯¯¯¯amod:¯¯¯¯¯¯¯¯¯¯¯dobj: [ITALIC] folded', '¯¯¯¯¯¯¯¯¯¯¯dobj: [ITALIC] folded', '1', ... | For composing the adjective white with the noun clothes via the dependency relation amod we need to consider how the adjective interacts with the noun in the vector space. The distributional features of white describe things that are white via their first order relations such as ¯¯¯¯¯¯¯¯¯¯¯amod, and things that can be ... |
Improving Sparse Word Representations with Distributional Inference for Semantic Composition | 1608.06794 | Table 2: Comparison of composition by union and composition by intersection. Not all features are displayed for space reasons. | ['[BOLD] Composition by [ITALIC] union [BOLD] Distributional Features', '[BOLD] Composition by [ITALIC] union [BOLD] Co-occurrence Count', '[BOLD] Composition by [ITALIC] intersection [BOLD] Distributional Features', '[BOLD] Composition by [ITALIC] intersection [BOLD] Co-occurrence Count'] | [[': [ITALIC] shoes', '1', '[EMPTY]', '[EMPTY]'], ['amod: [ITALIC] clean', '1', '[EMPTY]', '[EMPTY]'], ['¯¯¯¯¯¯¯¯¯¯¯dobj: [ITALIC] bought', '1', '[EMPTY]', '[EMPTY]'], ['¯¯¯¯¯¯¯¯¯¯¯dobj: [ITALIC] folded', '2', '¯¯¯¯¯¯¯¯¯¯¯dobj: [ITALIC] folded', '2'], ['¯¯¯¯¯¯¯¯¯¯¯dobj: [ITALIC] like', '1', '[EMPTY]', '[EMPTY]'], ['¯¯¯... | We are then in a position to compose the offset representation of white with the vector for clothes by the union or the intersection of their features. It is worth noting that any arithmetic operation can be used to combine the counts of the aligned features, however for this paper we use pointwise addition for both co... |
Improving Sparse Word Representations with Distributional Inference for Semantic Composition | 1608.06794 | Table 4: Neighbour retrieval function comparison. Boldface means best performance on a dataset per VSM type. *) With 3 significant figures, the density window approach (0.713) is slightly better than the baseline without DI (0.708), static top n (0.710) and WordNet (0.710). | ['Apt [BOLD] s ( [ITALIC] k=40)', '[BOLD] No Distributional Inference', '[BOLD] Density Window', '[BOLD] Static Top [ITALIC] n', '[BOLD] WordNet'] | [['[ITALIC] MEN', '0.63', '0.67', '[BOLD] 0.68', '0.63'], ['[ITALIC] SimLex-999', '0.30', '0.32', '0.32', '[BOLD] 0.38'], ['[ITALIC] WordSim-353 (rel)', '0.55', '[BOLD] 0.62', '0.61', '0.56'], ['[ITALIC] WordSim-353 (sub)', '0.75', '[BOLD] 0.78', '0.76', '0.77'], ['[BOLD] Untyped VSM ( [ITALIC] k=1)', '[BOLD] No Distri... | The improvements are typically larger for the Apt model, suggesting that it is missing more distributional knowledge in its elementary representations than untyped models. The density window and static top n neighbour retrieval functions perform very similar, however the static approach is more consistent and never und... |
Improving Sparse Word Representations with Distributional Inference for Semantic Composition | 1608.06794 | Table 6: Neighbour retrieval function. Underlined means best performance per phrase type, boldface means best average performance overall. | ['Apt [BOLD] s', '[BOLD] No Distributional Inference [ITALIC] intersection', '[BOLD] No Distributional Inference [ITALIC] union', '[BOLD] Density Window [ITALIC] intersection', '[BOLD] Density Window [ITALIC] union', '[BOLD] Static Top [ITALIC] n [ITALIC] intersection', '[BOLD] Static Top [ITALIC] n [ITALIC] un... | [['[ITALIC] Adjective-Noun', '0.10', '0.41', '0.31', '0.39', '0.25', '0.40', '0.12', '0.41'], ['[ITALIC] Noun-Noun', '0.18', '0.42', '0.34', '0.38', '0.37', '0.45', '0.24', '0.36'], ['[ITALIC] Verb-Object', '0.17', '0.36–––––', '0.36', '0.36', '0.34', '0.35', '0.25', '0.36'], ['[ITALIC] [BOLD] Average', '0.15', '[BOLD... | For a quantitative analysis of distributional inference for semantic composition, we evaluate our model on the composition dataset of Mitchell and Lapata \shortciteMitchell_2010, consisting of 108 adjective-noun, 108 noun-noun, and 108 verb-object pairs. The task is to compare the model’s similarity estimates with the ... |
Data Augmentation for Hypernymy Detection | 2005.01854 | Table 2: Accuracy scores for the data augmentation and the two dataset extension strategies in comparison to the same FF model without any augmentation or extension. | ['[BOLD] Model', '[BOLD] Weeds', '[BOLD] LEDS', '[BOLD] HP4K'] | [['Baseline - No Augmentation/Extension', '0.72', '0.77', '0.67'], ['Distributional Composition Augmentation', '[BOLD] 0.76', '[BOLD] 0.83', '0.70'], ['[ITALIC] GANDALF Augmentation', '0.75', '0.80', '[BOLD] 0.71'], ['WordNet Extension', '0.75', '[BOLD] 0.83', '0.69'], ['Hearst Patterns Extension', '0.74', '0.81', '0.6... | Our techniques are able to outperform a non-augmented model by 4-6 points in accuracy, representing a relative error reduction of 14%-26%. While the primary objective in this work is to improve an existing model setup with data augmentation, our augmented models compare favourably with previously published results. |
Data Augmentation for Hypernymy Detection | 2005.01854 | Table 4: Accuracy for the hypernym-only and full models on the Weeds dataset with no, DC or GAN augmentation. | ['[BOLD] Augmentation', '[BOLD] Hypernym-Only', '[BOLD] Full Model'] | [['None', '0.59', '0.72'], ['DC (size=100)', '0.60', '[BOLD] 0.74'], ['DC (size=500)', '[BOLD] 0.57', '0.71'], ['GAN (size=500)', '[BOLD] 0.58', '[BOLD] 0.75'], ['GAN (size=1000)', '0.60', '0.73']] | Ideally, we would hope to see weak performance for the hypernym-only and strong performance on the full model. This would indicate that the classifier does not rely on prototypical features in the hypernym, but is able to focus on specific features in a given hyponym-hypernym pair. |
Natural Language Multitasking Analyzing and Improving Syntactic Saliency of Latent Representations | 1801.06024 | Table 3: Best-of-100 clustering errors with fewer training examples | ['Model', 'R-D-1024', 'R-D-256', 'R-D-F-1024', 'R-D-P-256'] | [['Full training set', '22', '29', '24', '3'], ['1/2 training set', '37', '33', '-', '-'], ['1/3 training set', '-', '-', '19', '1']] | Note that the perplexities these models achieved are all comparable to their reference models (trained with the full training set), and none of the models overfit the training data. |
Natural Language Multitasking Analyzing and Improving Syntactic Saliency of Latent Representations | 1801.06024 | Table 2: Clustering errors by model | ['Model', 'REP', 'REP-FR', 'REP-FR-DE', 'REP-DE', 'REP-POS', 'REP-DE-POS'] | [['Error', '51', '26', '24', '22', '8', '0']] | Each sentence prototype is randomly populated by common English words 100 times. The syntax of each sentence in such a category is very similar or identical to all others in the same category, but different from sentences in other categories. These sentences are then fed into our models. We record every resulting repre... |
Data Augmenting Contrastive Learning of Speech Representationsin the Time Domain | 2007.00991 | Table S3: Additive noise augmented CPC, ABX errors (Libri-light dev set). Within- and across-speaker phoneme discriminability scores (lower is better) on the Libri-light clean and other dev sets for CPC training as a function of varying types of additive noise augmentation. | ['[EMPTY]', 'Within spk. dev', 'Within spk. dev', 'Across spk. dev', 'Across spk. dev'] | [['System', 'clean', 'other', 'clean', 'other'], ['MFCC Baseline', '10.95', '13.55', '20.94', '29.41'], ['CPC LL-60k', '6.11', '8.17', '8.05', '12.83'], ['[ITALIC] CPC2 – Trained on LibriSpeech clean 80h', '[ITALIC] CPC2 – Trained on LibriSpeech clean 80h', '[ITALIC] CPC2 – Trained on LibriSpeech clean 80h', '[ITALIC] ... | We discovered accidentally that for additive noise, low frequencies are more effective than high frequencies. We therefore explored systematically the effect of the spectral characteristics of noise by filtering sounds from the MUSAN dataset [musan2015] in successive frequency bands. We selected 5 broad bands, defined ... |
Data Augmenting Contrastive Learning of Speech Representationsin the Time Domain | 2007.00991 | Table 1: ABX errors on data-augmented CPC features (Libri-light dev set). Within- and across-speaker phoneme discriminability scores (lower is better) on the Libri-light clean and other dev sets for CPC training as a function of types of data augmentation, in isolation or combination (see Section 4.1). | ['[EMPTY]', 'Within spk. dev', 'Within spk. dev', 'Across spk. dev', 'Across spk. dev'] | [['System', 'clean', 'other', 'clean', 'other'], ['MFCC Baseline', '10.95', '13.55', '20.94', '29.41'], ['CPC LL-60k [kahn2020]', '6.11', '8.17', '8.05', '12.83'], ['[ITALIC] Single augmentations (CPC2 on LibriSpeech clean 100h)', '[ITALIC] Single augmentations (CPC2 on LibriSpeech clean 100h)', '[ITALIC] Single augmen... | The only augmentation performing better on past+future is add. According to their average performance, the individual augmentations can be sorted, from most to least useful: pitch, add, reverb, tdrop, and bandrej. Next, we study the performance of combinations of augmentations. We decided to drop bandrej from considera... |
Data Augmenting Contrastive Learning of Speech Representationsin the Time Domain | 2007.00991 | Table 3: ABX errors on the ZeroResource Speech Challenge 2017 (120s). Within- (“W.”) and across-speaker (“A.”) phoneme discriminability scores on English, French and Mandarin speech for CPC features with and without data augmentation. For comparison, the best systems plus supervised topline of the ZeroSpeech leaderboar... | ['[EMPTY]', 'English W.', 'English A.', 'French W.', 'French A.', 'Mandarin W.', 'Mandarin A.', 'AVG'] | [['[ITALIC] Trained on ZeroSpeech2017 (45h, 24h, 2h30, resp.)', '[ITALIC] Trained on ZeroSpeech2017 (45h, 24h, 2h30, resp.)', '[ITALIC] Trained on ZeroSpeech2017 (45h, 24h, 2h30, resp.)', '[ITALIC] Trained on ZeroSpeech2017 (45h, 24h, 2h30, resp.)', '[ITALIC] Trained on ZeroSpeech2017 (45h, 24h, 2h30, resp.)', '[ITALIC... | As can be seen, while noise augmentation improves the score on all three languages, we cannot reach the SOTA with the small training datasets provided from the challenge. We can however, be on par with or improve over best performing baseline with our out-of-domain train sets (same languages, larger datasets), in parti... |
Data Augmenting Contrastive Learning of Speech Representationsin the Time Domain | 2007.00991 | Table 4: Phone Error Rate (PER) in the semi-supervised setting. A linear classifier is added on top of Librispeech-100 pretrained CPC2 models and fine tuned with either 10min, 1h or 10h of Libri-light labelled data with a CTC loss. For comparison, reference Libri-light results plus the untrained CPC2 architecture fully... | ['System', 'Augmented fine-tuning', 'dev- clean', 'dev- other', 'test- clean', 'test- other'] | [['[ITALIC] Reference', '[ITALIC] Reference', '[ITALIC] Reference', '[ITALIC] Reference', '[ITALIC] Reference', '[EMPTY]'], ['CPC unlab-60k+train-10h-full', 'CPC unlab-60k+train-10h-full', '28.4', '41.4', '27.9', '43.6'], ['CPC no pretraining - 10h-full', 'CPC no pretraining - 10h-full', '45.9', '55.7', '43.7', '58.6']... | For the supervised fine-tuning phase, we found out that we got the best results by using only pitch augmentation. Other methods having low or negative effects in this case. The combined effects of data augmentation on pretraining and fine-tuning adds up to 12-15% relative improvement across the different training sets.... |
Data Augmenting Contrastive Learning of Speech Representationsin the Time Domain | 2007.00991 | Table S1: Architecture ablations, ABX errors (Libri-light dev set). We compare the original CPC model described in [riviere2020multi] with modifications including more LSTM layers and a single Multi-Head prediction model for all time steps (MH). The bottom model is the one we refer to as CPC2 in the paper. | ['[EMPTY]', 'Within spk. dev', 'Within spk. dev', 'Across spk. dev', 'Across spk. dev'] | [['System', 'clean', 'other', 'clean', 'other'], ['CPC LS-100 [riviere2020multi]', '6.81', '8.91', '8.46', '13.70'], ['CPC + 2 layers LSTM', '5.97', '8.12', '7.39', '12.79'], ['CPC + 3 layers LSTM', '5.93', '8.41', '7.76', '13.14'], ['CPC + MH', '6.84', '9.10', '8.68', '14.08'], ['CPC + MH + 2 layers LSTM', '[BOLD] 6.0... | CPC2 is a modified version of the CPC architecture in [kahn2020, riviere2020multi]. The encoder architecture is unchanged (5 convolutional layers with kernel sizes [10,8,4,4,4], strides [5,4,2,2,2] and hidden dimension 256). For the recurrent context nextwork, we use a 2-layer LSTM, as a tradeoff between feature qualit... |
Data Augmenting Contrastive Learning of Speech Representationsin the Time Domain | 2007.00991 | Table S2: Architecture ablations, ABX errors (Libri-light dev set). We compare modifications of the CPC architecture across different dataset sizes. In all cases, we apply the best data augmentation reported in the main text. | ['[EMPTY]', 'Within spk. dev', 'Within spk. dev', 'Across spk. dev', 'Across spk. dev'] | [['System', 'clean', 'other', 'clean', 'other'], ['[ITALIC] 3h Libri-light', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]'], ['CPC2 + 2 layers LSTM', '12.82', '13.81', '17.21', '20.85'], ['CPC2 + 3 layers LSTM', '13.22', '14.30', '18.30', '22.27'], ['[ITALIC] 45h Libri-light', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]'], ... | In this experiment, we test whether our data augmentation technique can build better speech features that can be used for downstream tasks. Here, we use the Libri-light limited supervision phone classification task [kahn2020], which contains intentionally small training sets (10 min, 1h or 10 hours of labelled data). W... |
Extracting Parallel Paragraphs from Common Crawl | 1804.10413 | Table 1: Prealigned Data (CzEng 1.0); Experiment: Effectiveness | ['[BOLD] Recall (%)', '63.02'] | [['[BOLD] Precision (%)', '93.74']] | In the preliminary alignments of the tail, 50.30% of all the top candidates are exact matches. However, this ratio is more satisfactory in the scored alignments—71.30%. This means that the scoring of the preliminary alignments is an important step in the whole process. Of all the existing pairs of parallel sentences 63... |
Towards Hate Speech Detection at Large via Deep Generative Modeling | 2005.06370 | Table 4: Cross-Dataset performance, comparing detector training using baseline to augmented-baseline training sets | ['Trainset- Testset', 'Accuracy Baseline', 'Accuracy Augmented', 'Accuracy (%)', 'Precision Baseline', 'Precision Augmented', 'Precision (%)', 'Recall Baseline', 'Recall Augmented', 'Recall (%)', 'F1 Baseline', 'F1 Augmented', 'F1 (%)'] | [['FN-SE', '0.613', '0.645', '+5.22', '0.689', '0.570', '-17.27', '0.155', '0.644', '+315.48', '0.253', '0.605', '+139.13'], ['FN-WH', '0.846', '0.850', '+0.47', '0.636', '0.528', '-16.98', '0.063', '0.507', '+704.76', '0.114', '0.517', '+353.50'], ['DV-WH', '0.794', '0.820', '+3.27', '0.278', '0.441', '+58.63', '0.189... | In another set of experiments, we re-trained the detector with each one of the corresponding augmented training set, and evaluated on the same cross-dataset combinations of the first experiment. The average metrics of all cross-dataset pairs reveals a consistent increase in accuracy (+6.9%), precision (+3.5%), recall (... |
Towards Hate Speech Detection at Large via Deep Generative Modeling | 2005.06370 | Table 2: ROUGE-L scores of data generated by the GPT-2 language model per dataset. These values indicate on low similarity between the generated sequences and the sequences on which the model has been fine-tuned, for each dataset and class. | ['Source dataset', 'Generated hate', 'Generated non-hate'] | [['WS', '0.12', '0.05'], ['DV', '0.07', '0.05'], ['FN', '0.11', '0.14'], ['WH', '0.09', '0.16'], ['SE', '0.05', '0.03']] | We further wished to verify that the generated sequences depart from the seed labeled examples, forming new and diverse language usage, as opposed to duplicating or repeating parts of the input examples. (nothing in common) to 1 (identical sequences). As shown, the similarity scores are low, indicating that the rich la... |
Towards Hate Speech Detection at Large via Deep Generative Modeling | 2005.06370 | Table 3: Intra-Dataset classification results, comparing the baseline and augmented-baseline training sets | ['Dataset', 'Accuracy Baseline', 'Accuracy Augmented', 'Accuracy (%)', 'Precision Baseline', 'Precision Augmented', 'Precision (%)', 'Recall Baseline', 'Recall Augmented', 'Recall (%)', 'F1 Baseline', 'F1 Augmented', 'F1 (%)'] | [['WS', '0.967', '0.977', '+1.03', '0.968', '0.989', '+2.17', '0.936', '0.943', '+0.75', '0.952', '0.966', '+1.47'], ['WH', '0.891', '0.872', '-2.13', '0.862', '0.600', '-30.40', '0.375', '0.582', '+55.20', '0.523', '0.591', '+13.00'], ['SE', '0.715', '0.764', '+6.85', '0.901', '0.767', '-14.87', '0.367', '0.635', '+73... | Intra-Dataset Experiments. The results indicate that augmenting the training sets with automatically generated examples leads to improvement in Recall and F1 in most cases, peaking at +73.0% and +33.1%, respectively, for the SE dataset. Precision on the other hand is decreased for most datsets, and Accuracy changes mil... |
The Importance of Automatic Syntactic Features in Vietnamese Named Entity Recognition | 1705.10610 | Table 4: Performance of our model when using one and two layers | ['Entity', 'Bi-LSTM Pre.', 'Bi-LSTM Rec.', 'Bi-LSTM [ITALIC] F1', 'LSTM Pre.', 'LSTM Rec.', 'LSTM [ITALIC] F1'] | [['LOC', '83.63', '82.48', '83.05', '74.60', '77.38', '75.96'], ['MISC', '84.14', '78.37', '81.07', '2.15', '2.04', '2.09'], ['ORG', '49.85', '50.51', '50.07', '32.22', '34.60', '33.60'], ['PER', '72.77', '65.73', '69.06', '67.95', '60.73', '64.12'], ['ALL', '75.88', '72.26', '[BOLD] 74.02', '66.61', '65.04', '65.80']] | S4SS3SSS0Px2 Effect of Bidirectional Learning In the second experiment, we examine the benefit of accessing both past and future contexts by comparing the performances of RNN, LSTM and Bi-LSTM models. In this task, RNN model fails because it faces the gradient vanishing/exploding problem when training with long-range d... |
The Importance of Automatic Syntactic Features in Vietnamese Named Entity Recognition | 1705.10610 | Table 5: Performance of our model when using one and two layers | ['Entity', 'Two layers Pre.', 'Two layers Rec.', 'Two layers [ITALIC] F1', 'One layer Pre.', 'One layer Rec.', 'One layer [ITALIC] F1'] | [['LOC', '83.63', '82.48', '83.05', '82.22', '80.64', '81.41'], ['MISC', '84.14', '78.37', '81.07', '85.15', '74.29', '79.32'], ['ORG', '49.85', '50.51', '50.07', '44.10', '40.88', '42.39'], ['PER', '72.77', '65.73', '69.06', '72.70', '62.15', '66.91'], ['ALL', '75.88', '72.26', '[BOLD] 74.02', '74.83', '68.91', '71.74... | S4SS3SSS0Px3 Number of Bi-LSTM Layers In the third experiment, we investigate the improvement when adding more Bi-LSTM layers. We observe a significant improvement when using two layers of Bi-LSTM. The performance is increased from 71.74% to 74.02% |
The Importance of Automatic Syntactic Features in Vietnamese Named Entity Recognition | 1705.10610 | Table 7: Performance of our model when adding more features | ['Features', 'Pre.', 'Rec.', '[ITALIC] F1'] | [['Word', '75.88', '72.26', '74.02'], ['Word+POS', '84.23', '87.64', '85.90'], ['Word+Chunk', '90.73', '83.18', '86.79'], ['Word+Case', '83.68', '84.45', '84.06'], ['Word+Regex', '76.58', '71.86', '74.13'], ['Word+POS+Chunk+Case+Regex', '90.25', '92.55', '91.39'], ['Word+POS+Chunk+Regex', '91.09', '93.03', '[BOLD] 92.0... | As shown in the previous experiments, using only word features in deep learning models is not enough to achieve the state-of-the-art result. In particular, the accuracy of this model is only 74.02%. This result is far lower in comparison to that of state-of-the-art systems for Vietnamese NER. In the following experimen... |
FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarization | 2005.03754 | Table 8: Pearson (P) and Spearman (S) correlation between human-annotated faithfulness scores and ROUGE scores of content selection (computed between the reference and the output sentence). High content selection scores (typical ROUGE score for summarization) do not necessarily imply faithfulness of the summary. | ['Metric', 'CNN/DM P', 'CNN/DM S', 'XSum P', 'XSum S'] | [['ROUGE-1', '15.31^{**}', '14.92^{**}', '5.44', '5.79'], ['ROUGE-2', '15.10^{**}', '16.39^{**}', '8.25', '6.79'], ['ROUGE-L', '13.33^{**}', '13.35^{**}', '4.61', '3.97']] | Current evaluation metrics for summarization produce a single measure of the overall quality of the summary. Typically, the output summary is compared against the reference summary in terms of n-gram overlap. These metrics mainly evaluate content selection, i.e. whether the content of the output is similar to the conte... |
Neural Naturalist: Generating Fine-Grained Image Comparisons | 1909.04101 | Table 2: Experimental results for comparative paragraph generation on the proposed dataset. For human captions, mean and standard deviation are given for a one-vs-rest scheme across twenty-five runs. We observed that CIDEr-D scores had little correlation with description quality. The Neural Naturalist model benefits fr... | ['[EMPTY]', 'Dev BLEU-4', 'Dev ROUGE-L', 'Dev CIDEr-D', 'Test BLEU-4', 'Test ROUGE-L', 'Test CIDEr-D'] | [['Most Frequent', '0.20', '0.31', '[BOLD] 0.42', '0.20', '0.30', '[BOLD] 0.43'], ['Text-Only', '0.14', '0.36', '0.05', '0.14', '0.36', '0.07'], ['Nearest Neighbor', '0.18', '0.40', '0.15', '0.14', '0.36', '0.06'], ['CNN + LSTM Vinyals et al. ( 2015 )', '0.22', '0.40', '0.13', '0.20', '0.37', '0.07'], ['CNN + Attn. + L... | We observe improvement across BLEU-4 and ROUGE-L scores compared to baselines. Curiously, we observe that the CIDEr-D metric is susceptible to common patterns in the data; our model, when stopped at its highest CIDEr-D score, outputs a variant of, “these animals appear exactly the same” for 95% of paragraphs, nearly mi... |
Neural Naturalist: Generating Fine-Grained Image Comparisons | 1909.04101 | Table 4: Human evaluation results on 120 test set samples, twenty per column. Scale: -1 (perfectly wrong) to 1 (perfectly correct). Columns are ordered left-to-right by increasing distance. Our model outperforms baselines for several distances, though highly similar comparisons still prove difficult. | ['[EMPTY]', 'Visual', 'Species', 'Genus', 'Family', 'Order', 'Class'] | [['Freq.', '0.00', '0.00', '0.00', '0.00', '0.00', '0.00'], ['Text-Only', '0.00', '-0.10', '-0.05', '0.00', '0.15', '-0.15'], ['CNN + LSTM', '-0.15', '[BOLD] 0.20', '0.15', '[BOLD] 0.50', '0.40', '0.15'], ['CNN + Attn. + LSTM', '[BOLD] 0.15', '0.15', '0.15', '-0.05', '0.05', '0.20'], ['Neural Naturalist', '0.10', '-0.1... | In this measure, we see the frequency and text- only baselines now fall flat, as expected. The frequency baseline never receives any points, and the text-only baseline is often penalized for incorrectly guessing. Our model is successful at making distinctions between visually distinct species (Genus column and ones fur... |
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer | 1701.06538 | Table 8: Model comparison on 100 Billion Word Google News Dataset | ['Model', 'Test Perplexity', 'Test Perplexity', 'ops/timestep (millions)', '#Params excluding embed. & softmax', 'Total #Params', 'TFLOPS per GPU'] | [['[EMPTY]', '.1 epochs', '1 epoch', '[EMPTY]', '(millions)', '(billions)', '(observed)'], ['Kneser-Ney 5-gram', '67.1', '45.3', '0.00001', '[EMPTY]', '76.0', '[EMPTY]'], ['4xLSTM-512', '54.5', '47.0', '8.4', '8.4', '0.1', '[BOLD] 1.23'], ['MoE-32', '48.5', '40.4', '8.4', '37.8', '0.1', '0.83'], ['MoE-256-h', '42.8', '... | : We evaluate our model using perplexity on a holdout dataset. Perplexity after 100 billion training words is 39% lower for the 68-billion-parameter MoE model than for the baseline model. It is notable that the measured computational efficiency of the largest model (0.30 TFLOPS/GPU) is very low compared to the other mo... |
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer | 1701.06538 | Table 6: Experiments with different combinations of losses. | ['[ITALIC] wimportance', '[ITALIC] wload', 'Test Perplexity', '[ITALIC] CV( [ITALIC] Importance( [ITALIC] X))', '[ITALIC] CV( [ITALIC] Load( [ITALIC] X))', '[ITALIC] max( [ITALIC] Load( [ITALIC] X)) [ITALIC] mean( [ITALIC] Load( [ITALIC] X))'] | [['0.0', '0.0', '39.8', '3.04', '3.01', '17.80'], ['0.2', '0.0', '[BOLD] 35.6', '0.06', '0.17', '1.47'], ['0.0', '0.2', '35.7', '0.22', '0.04', '1.15'], ['0.1', '0.1', '[BOLD] 35.6', '0.06', '0.05', '1.14'], ['0.01', '0.01', '35.7', '0.48', '0.11', '1.37'], ['1.0', '1.0', '35.7', '0.03', '0.02', '[BOLD] 1.07']] | All the combinations containing at least one the two losses led to very similar model quality, where having no loss was much worse. Models with higher values of wload had lower loads on the most overloaded expert. |
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer | 1701.06538 | Table 7: Model comparison on 1 Billion Word Language Modeling Benchmark. Models marked with * are from (Jozefowicz et al., 2016). | ['Model', 'Test Perplexity', 'Test Perplexity', 'ops/timestep (millions)', '#Params excluding embed. & softmax', 'Total #Params', '[ITALIC] Drop- [ITALIC] Prob', 'TFLOPS per GPU'] | [['[EMPTY]', '10 epochs', '(final)', '[EMPTY]', '(millions)', '(billions)', '[EMPTY]', '(observed)'], ['Kneser-Ney 5-gram*', '[EMPTY]', '67.6', '0.00001', '[EMPTY]', '1.8', '[EMPTY]', '[EMPTY]'], ['LSTM-512-512*', '[EMPTY]', '54.1', '2.4', '2.4', '0.8', '0.1', '[EMPTY]'], ['LSTM-1024-512*', '[EMPTY]', '48.2', '4.7', '4... | For our baseline models wtih no MoE, observed computational efficiency ranged from 1.07-1.29 TFLOPS/GPU. For our low-computation MoE models, computation efficiency ranged from 0.74-0.90 TFLOPS/GPU, except for the 4-expert model which did not make full use of the available parallelism. Our highest-computation MoE model ... |
We Built a Fake News & Click-bait Filter:What Happened Next Will Blow Your Mind! | 1803.03786 | Table 2: Performance of the individual groups of hand-crafted features. | ['[BOLD] Features', '[BOLD] P', '[BOLD] R', '[BOLD] F1', '[BOLD] Acc'] | [['Lexical', '75.53', '74.59', '75.02', '79.89'], ['Stylometric', '74.35', '65.99', '67.68', '77.52'], ['Grammatical', '73.23', '50.60', '42.99', '71.48'], ['Embeddings', '61.48', '53.95', '51.67', '71.22']] | We can see that, among the hand-crafted features, the lexical features yield the best results, i.e., words are the most indicative features. The good results of the stylometric features indicate that the intricacies of language use are highly discriminative. The next group is the one with the grammatical features, whic... |
We Built a Fake News & Click-bait Filter:What Happened Next Will Blow Your Mind! | 1803.03786 | Table 1: Words most strongly associated with the fake news class. | ['[BOLD] Original word', '[BOLD] Translation', '[BOLD] PMI'] | [['chemtrails', 'chemtrails', '0.92'], ['феноменните', 'the phenomenal', '0.94'], ['следете в', 'follow in', '0.97'], ['тайнствена', 'mysterious', '0.95'], ['скрит', 'hidden', '0.84']] | Fact-checking lexicon: Using lexicons of sentiment words has been shown to be very successful for the task of sentiment analysis Mohammad and Turney , and we applied the same idea to extract a fact-checking lexicon. In particular, we use point-wise mutual information (PMI) to find terms (words, word bi-grams, and named... |
We Built a Fake News & Click-bait Filter:What Happened Next Will Blow Your Mind! | 1803.03786 | Table 3: Performance of different models. | ['[BOLD] Feature Group', '[BOLD] P', '[BOLD] R', '[BOLD] F1', '[BOLD] Acc'] | [['Baseline', '35.61', '50.00', '41.59', '71.22'], ['TF.IDF', '75.53', '74.59', '75.02', '79.89'], ['AttNN', '78.52', '78.74', '78.63', '81.99'], ['TF.IDF &AttNN', '79,89', '79.40', '79.63', '83.44'], ['TF.IDF &Feats &AttNN', '80.07', '79.49', '79.77', '83.57']] | Evaluating the final model, we set as a baseline the prediction of the majority class, i.e., the fake news class. This baseline has an F1 of 41.59% and accuracy of 71.22%. Another stable baseline, apart from just taking the majority class, is the TF.IDF bag-of-words approach, which sets a high bar for the general model... |
Controllable Length Control Neural Encoder-Decoder via Reinforcement Learning | 1909.09492 | Table 1: Example summaries of four LC models. (Note that “gunners” is a nickname of arsenal) | ['Source article Reference summary', 'arsenal chairman peter hill-wood revealed thursday that he fears french striker thierry henry will leave highbury at the end of the season . arsenal boss fears losing henry', 'arsenal chairman peter hill-wood revealed thursday that he fears french striker thierry henry will leave h... | [['model', 'desired length and sampled summaries (true length)', 'desired length and sampled summaries (true length)'], ['[EMPTY]', '25', 'arsenal chief quits to leave (24)'], ['LenLInit', '45', 'gunners chief fears french striker will leave the end (45)'], ['[EMPTY]', '65', 'gunners chief says he will leave as he fear... | It is also observed that LenLInit and LenMC perform better on short sentence summary in this case. |
Controllable Length Control Neural Encoder-Decoder via Reinforcement Learning | 1909.09492 | Table 2: Results of ML training on standard “test-1951” | ['model name', 'R-1', 'R-2', 'R-L', 'svar'] | [['Summarization models', 'Summarization models', 'Summarization models', 'Summarization models', 'Summarization models'], ['ABS', '29.55', '11.32', '26.42', '-'], ['ABS+', '29.76', '11.88', '26.96', '-'], ['Luong-NMT', '33.10', '14.45', '30.71', '-'], ['RAS-LSTM', '32.55', '14.70', '30.03', '-'], ['RAS-ELman', '33.78'... | Although the evaluation score is not the unique objective in this research, it is of interest that how exactly the score is deprived by LC capacity. After individually comparing two WLI models and two RLI models, we find the two proposed models, LenLInit and LenMC, slightly corrupt LC capacity while improve the scores ... |
Controllable Length Control Neural Encoder-Decoder via Reinforcement Learning | 1909.09492 | Table 3: Performance of length control RL in “test-4k” (ML results also included for comparison). Obviously highest scores (0.4 larger than the second best) are in bolded font, the scores in italic font are significantly worse score (2 lower than best socre). | ['model', 'parameter', '25 R-1', '25 R-2', '25 R-L', '45 R-1', '45 R-2', '45 R-L', '65 R-1', '65 R-2', '65 R-L', 'svar(±std)'] | [['ML', 'ML', 'ML', 'ML', 'ML', 'ML', 'ML', 'ML', 'ML', 'ML', 'ML', 'ML'], ['LenLInit', '[EMPTY]', '[BOLD] 39.03', '[BOLD] 17.68', '[BOLD] 37.46', '42.04', '20.47', '39.87', '[BOLD] 39.40', '[BOLD] 18.71', '[BOLD] 36.96', '3.96'], ['LenInit', '[EMPTY]', '37.36', '16.76', '35.92', '42.11', '20.55', '39.83', '38.67', '18... | We evaluate our models with sentence length of 25, 45 and 65, which represent short, median and long sentences separately. Results may vary after each training process since RL is usually unstable, so we repeat training for multiple times in each model and statistic the results on average. We make experiments of MTS-RL... |
Calculating the similarity between words and sentences using a lexical database and corpus statistics | 1802.05667 | TABLE III: Synsets and corresponding shortest path distances from WordNet | ['Synset Pair', 'Shortest Path Distance'] | [['Synset(‘river.n.01’) - Synset(‘bank.n.01’)', '8'], ['Synset(‘river.n.01’) - Synset(‘bank.n.09’)', '10'], ['Synset(‘river.n.01’) - Synset(‘bank.n.06’)', '11']] | When comparing two sentences, we have many such word pairs which have multiple synsets. Therefore, not considering the proper synset in context of the sentence, could introduce errors at the early stage of similarity calculation. Hence, sense of the word affects significantly on the overall similarity measure. Identify... |
Upcycle Your OCR: Reusing OCRs for Post-OCR Text Correction in Romanised Sanskrit | 1809.02147 | Table 4: Performance in terms of CRR, WRR for Google OCR | ['[BOLD] Model', '[BOLD] Bhagavad Gītā Ins', '[BOLD] Bhagavad Gītā Del', '[BOLD] Bhagavad Gītā Sub', '[BOLD] Sahaśranāma Ins', '[BOLD] Sahaśranāma Del', '[BOLD] Sahaśranāma Sub', '[BOLD] System errors Ins', '[BOLD] System errors Del', '[BOLD] System errors Sub'] | [['OCR', '23', '63', '1868', '73', '696', '1596', '–', '–', '–'], ['PCRF', '22', '57', '641', '72', '663', '932', '0', '73', '209'], ['[BOLD] CopyNet', '22', '[BOLD] 45', '629', '72', '[BOLD] 576', '561', '10', '5', '52']] | , we analyse the reduction in specific error types for PCRF and CopyNet after the alignment of the predicted string with that of the ground truth in terms of insertion, deletion and substitution. We also report the system induced errors, where a correct component at the input (OCR output) is mispredicted to a wrong out... |
Upcycle Your OCR: Reusing OCRs for Post-OCR Text Correction in Romanised Sanskrit | 1809.02147 | Table 1: OCR performances for different languages with overall CRR, total Insertion, Deletion and Substitution errors. | ['[BOLD] Language', '[BOLD] Bhagavad Gītā CRR', '[BOLD] Bhagavad Gītā Ins', '[BOLD] Bhagavad Gītā Del', '[BOLD] Bhagavad Gītā Sub', '[BOLD] Sahaśranāma CRR', '[BOLD] Sahaśranāma Ins', '[BOLD] Sahaśranāma Del', '[BOLD] Sahaśranāma Sub', '[BOLD] Combined CRR'] | [['English', '84.92', '23', '63', '1868', '64.06', '73', '696', '1596', '80.08'], ['French', '84.90', '21', '102', '1710', '63.91', '91', '702', '1670', '80.04'], ['Finnish', '82.61', '15', '141', '1902', '61.31', '80', '730', '1821', '78.81'], ['Italian', '83.45', '20', '73', '1821', '62.19', '84', '690', '1673', '79.... | French alphabet has the highest grapheme overlap with that of the Sanskrit alphabet (37 of 50), while all other languages have one less grapheme common with Sanskrit. Hence, we arbitrarily take 5 of the languages in addition to French and perform our analysis. The table also shows the count of error types made by the O... |
Word-based Domain Adaptation for Neural Machine Translation | 1906.03129 | Table 4: Study on the effect of different smoothing methods for word weights generation. Baseline is the same as before. w.w. without smoothing means the word weights (w.w.) are computed without smoothing in the log domain. w.w. (mean smooth.) indicates smoothing the word scores via using a mean average filter before t... | ['System', 'BLEU [%]', 'TER [%]'] | [['Baseline', '24.37', '61.66'], ['+w.w. without smooth.', '21.38', '66.25'], ['+w.w. (mean smooth.)', '25.99', '60.70'], ['+w.w. (gauss. smooth.)', '26.14', '60.34']] | The word weights generated without using smoothing methods, where ^st=st, lead to poor translation quality of 21.38% from 24.37% BLEU and 66.25% from 61.66% TER, respectively. We need to smooth the word scores before thresholding because the values of logPI(yt|yt−1t−n)−logPO(yt|yt−1t−n) are noisy. If there are selected... |
Word-based Domain Adaptation for Neural Machine Translation | 1906.03129 | Table 3: E-commerce English → Chinese BLEU results on test set. Baseline is trained on mixed in-domain and out-of-domain data. No. 2 is continuing training from baseline with objective defined as Eq. 1. No. 3 is continuing training from baseline with sentence-level weights and No. 4 is with word weights, as defined in ... | ['No.', 'System description', 'Item descriptions BLEU [%]', 'Item descriptions TER [%]'] | [['1', 'Baseline', '24.37', '61.66'], ['2', '1 + continue training without word weights', '24.31', '61.69'], ['3', '1 + continue training with sentence weights', '25.79', '60.82'], ['4', '1 + continue training with word weights', '26.14', '60.34'], ['5', '1 + continue training with chunk weights', '26.42', '60.10'], ['... | First, the baseline trained on mixed in-domain and out-of-domain datasets gives 24.37% BLEU and 61.66% TER, respectively. Directly fine-tuning on in-domain dataset already improves the model due to the bias of the model towards in-domain data. |
Evaluating historical text normalization systems: How well do they generalize? | 1804.02545 | Table 2: Tokens normalized correctly (%) for each dataset. Upper half: results on (A)ll tokens reported by Pettersson et al. (2014) for a hybrid model (apply memorization baseline to seen tokens and an edit-distance-based model to unseen tokens) and two SMT models (which align character unigrams and bigrams, respective... | ['[EMPTY]', 'English A', 'English S', 'English U', 'German A', 'German S', 'German U', 'Hungarian A', 'Hungarian S', 'Hungarian U', 'Icelandic A', 'Icelandic S', 'Icelandic U', 'Swedish A', 'Swedish S', 'Swedish U'] | [['Hybrid', '92.9', '[EMPTY]', '[EMPTY]', '95.1', '[EMPTY]', '[EMPTY]', '76.4', '[EMPTY]', '[EMPTY]', '[BOLD] 84.6', '[EMPTY]', '[EMPTY]', '90.8', '[EMPTY]', '[EMPTY]'], ['GIZA++ un', '[BOLD] 94.3', '[EMPTY]', '[EMPTY]', '[BOLD] 96.6', '[EMPTY]', '[EMPTY]', '79.9', '[EMPTY]', '[EMPTY]', '71.8', '[EMPTY]', '[EMPTY]', '[... | The split into seen/unseen highlights the fact that neither of the neural models does as well on seen items as the baseline; indeed the soft attention model is considerably worse in English and Hungarian, the two largest datasets. The result is that this model actually underperforms the baseline when applied to all tok... |
A Simple Approach to Case-Based Reasoning in Knowledge Bases | 2006.14198 | Table 3: Link prediction results on WN18RR dataset. | ['[BOLD] Metric', '[BOLD] TransE', '[BOLD] DistMult', '[BOLD] ComplEx', '[BOLD] ConvE', '[BOLD] RotatE', '[BOLD] GNTP', '[BOLD] CBR'] | [['hits@1', '-', '0.39', '0.41', '0.40', '[BOLD] 0.43', '0.41', '0.38'], ['hits@3', '-', '0.44', '0.46', '0.44', '[BOLD] 0.49', '0.44', '0.46'], ['hits@10', '0.50', '0.49', '0.51', '0.52', '[BOLD] 0.57', '0.48', '0.51'], ['MRR', '0.23', '0.43', '0.44', '0.43', '[BOLD] 0.48', '0.43', '0.43']] | WN18RR: CBR performs competitively with GNTPs and most embedding based methods except RotatE sun2019rotate. Upon further analysis, we find that for 210 triples in the test set, the entity was not present in the graph and hence no answers were returned for those query entities. |
A Simple Approach to Case-Based Reasoning in Knowledge Bases | 2006.14198 | Table 1: Link prediction results on the FB122 dataset. | ['[EMPTY]', '[BOLD] Model', 'hits@3', 'hits@5', 'hits@10', 'MRR'] | [['With Rules', 'KALE-Pre guo2016jointly', '0.358', '0.419', '0.498', '0.291'], ['With Rules', 'KALE-Joint guo2016jointly', '0.384', '[BOLD] 0.447', '[BOLD] 0.522', '0.325'], ['With Rules', '[ITALIC] ASR-DistMult minervini2017adversarial', '0.363', '0.403', '0.449', '0.330'], ['With Rules', '[ITALIC] ASR-ComplEx minerv... | FB122: Next we consider the FB122 dataset by guo2016jointly. Comparing results on FB122 is attractive for a couple of reasons — (a) Firstly, this dataset comes with a set of logical rules hand coded by the authors that can be used for logical inference. It would be interesting to see if our CBR approach is able to auto... |
A Simple Approach to Case-Based Reasoning in Knowledge Bases | 2006.14198 | Table 4: Link prediction results on NELL-995 for few shot relations. | ['[BOLD] Model', 'hits@1', 'hits@10', 'MRR'] | [['NeuralLP yang2017differentiable', '0.048', '0.351', '0.179'], ['NTP- [ITALIC] λ ntp', '0.102', '0.334', '0.155'], ['MINERVA das2018go', '0.162', '0.283', '0.201'], ['MultiHop(DistMult) LinRX2018:MultiHopKG', '0.145', '0.306', '0.200'], ['MultiHop(ConvE) LinRX2018:MultiHopKG', '0.178', '0.329', '0.231'], ['Meta-KGR(D... | As mentioned before, our CBR based approach needs no training and gathers reasoning patterns from few similar entities. Therefore, it should ideally perform well for query relations for which we do not have a lot of data. Recently, lv2019adapting studied this problem and propose a meta-learning finn2017model based solu... |
Slim Embedding Layers for Recurrent Neural Language Models | 1711.09873 | Table 4: Time Usage Comparison | ['Model', 'CPU(seconds)', 'GPU (milliseconds)'] | [['Uncompressed', '2.7', '38'], ['HashNet', '80.6', '-'], ['SE', '0.7', '25']] | We report the time used on both CPU and GPU. All the computations use 32 bit floating point numbers. On CPU, HashNet is slower than the normal uncompressed model, mainly because of two reasons: 1) The uncompressed model uses optimized matrix multiplication subroutines, 2) The hash function used in HashNet is cheap, but... |
Slim Embedding Layers for Recurrent Neural Language Models | 1711.09873 | Table 2: Perplexity results for single models on BillionW. Bold number denotes results on a single GPU. | ['Model', 'Perplexity', '#P[Billions]'] | [['Interpolated Kneser-Ney 5-gram ', '67.6', '1.76'], ['4-layer IRNN-512 ', '69.4', '[EMPTY]'], ['RNN-2048 + BlackOut sampling ', '[BOLD] 68.3', '[EMPTY]'], ['RNN-1024 + MaxEnt 9-gram ', '51.3', '20'], ['LSTM-2048-512 ', '43.7', '0.83'], ['LightRNN ', '[BOLD] 66.0', '0.041'], ['LSTM-2048-512 ', '43.7', '0.83'], ['2-lay... | In the one billion word experiments, the total memory during training used on the GPU is about 7GB, and is smaller if a larger compression rate is used. We use a fixed smoothed unigram distribution (unigram distribution raised to 0.75) as the noise distribution. For the two layer model, the compression rate for the inp... |
Neural Baby Talk | 1803.09845 | Table 2: Performance on the test portion of Karpathy et al. [20]’s splits on COCO dataset. ∗ directly optimizes the CIDEr Metric, † uses better image features, and are thus not directly comparable. | ['Method', 'BLEU1', 'BLEU4', 'METEOR', 'CIDEr', 'SPICE'] | [['Adaptive ', '74.2', '32.5', '26.6', '[BOLD] 108.5', '19.5'], ['Att2in ', '-', '31.3', '26.0', '101.3', '-'], ['Up-Down ', '74.5', '33.4', '26.1', '105.4', '19.2'], ['Att2in∗ ', '-', '33.3', '26.3', '111.4', '-'], ['Up-Down† ', '79.8', '36.3', '27.7', '120.1', '21.4'], ['NBT', '[BOLD] 75.5', '[BOLD] 34.7', '[BOLD] 27... | Our method outperforms 4 out of 5 automatic evaluation metrics compared to the state of the art Interestingly, the NBToracle has little improvement over NBT. We suspect the reason is that explicit ground truth annotation is absent for visual words. Our model can be further improved with explicit co-reference supervisio... |
Neural Baby Talk | 1803.09845 | Table 1: Performance on the test portion of Karpathy et al. [20]’s splits on Flickr30k Entities dataset. | ['Method', 'BLEU1', 'BLEU4', 'METEOR', 'CIDEr', 'SPICE'] | [['Hard-Attention ', '66.9', '19.9', '18.5', '-', '-'], ['ATT-FCN ', '64.7', '23.0', '18.9', '-', '-'], ['Adaptive ', '67.7', '25.1', '20.4', '53.1', '14.5'], ['NBT', '[BOLD] 69.0', '[BOLD] 27.1', '[BOLD] 21.7', '[BOLD] 57.5', '[BOLD] 15.6'], ['NBToracle', '72.0', '28.5', '23.1', '64.8', '19.6']] | When using ground truth proposals, NBToracle significantly outperforms previous methods, improving 5.1 on SPICE, which implies that our method could further benefit from improved object detectors. |
Neural Baby Talk | 1803.09845 | Table 3: Performance on the test portion of the robust image captioning split on COCO dataset. | ['Method', 'BLEU4', 'METEOR', 'CIDEr', 'SPICE', 'Accuracy'] | [['Att2in ', '31.5', '24.6', '90.6', '17.7', '39.0'], ['Up-Down ', '31.6', '25.0', '92.0', '18.1', '39.7'], ['NBT', '[BOLD] 31.7', '[BOLD] 25.2', '[BOLD] 94.1', '[BOLD] 18.3', '[BOLD] 42.4'], ['NBToracle', '31.9', '25.5', '95.5', '18.7', '45.7']] | Results and analysis. As we can see, all models perform worse on the robust-COCO split than the Karpathy’s split by 2∼3 points in general. The oracle setting (NBToracle) has consistent improvements on all metrics, improving 3.3 on the proposed metric. |
Neural Baby Talk | 1803.09845 | Table 4: Evaluation of captions generated using the proposed method. G means greedy decoding, and T1−2 means using constrained beam search [2] with 1−2 top detected concepts. ∗ is the result using VGG-16 [41] and † is the result using ResNet-101. | ['Method', 'Out-of-Domain Test Data bottle', 'Out-of-Domain Test Data bus', 'Out-of-Domain Test Data couch', 'Out-of-Domain Test Data microwave', 'Out-of-Domain Test Data pizza', 'Out-of-Domain Test Data racket', 'Out-of-Domain Test Data suitcase', 'Out-of-Domain Test Data zebra', 'Out-of-Domain Test Data Avg', 'Out-of... | [['DCC ', '4.6', '29.8', '45.9', '28.1', '64.6', '52.2', '13.2', '79.9', '39.8', '13.4', '21.0', '59.1', '15.9', '23.0', '77.2'], ['NOC ', '17.8', '68.8', '25.6', '24.7', '69.3', '68.1', '39.9', '89.0', '49.1', '-', '21.4', '-', '-', '-', '-'], ['C-LSTM ', '29.7', '74.4', '38.8', '27.8', '68.2', '70.3', '44.8', '91.4',... | Specifically, NBT†+T2 outperforms the previous state-of-art model C-LSTM by 14.6% on average F1 scores. From the category F1 scores, we can see that our model is less likely to select small objects, e.g. “bottle”, “racket” when only using the greedy decoding. Fig. Also see rightmost example in Fig. |
FusionNet: Fusing via Fully-Aware Attention with Application to Machine Comprehension | 1711.07341 | Table 10: The performance (accuracy) of ESIM with our proposed attention enhancement on MultiNLI (Williams et al., 2017) development set. (d is the output hidden size of BiLSTM) | ['[EMPTY]', '[BOLD] Cross-Domain', '[BOLD] In-Domain'] | [['Our ESIM without CoVe ( [ITALIC] d=300)', '73.4', '73.3'], ['Our ESIM without CoVe + fully-aware ( [ITALIC] d=250)', '76.9', '76.2'], ['Our ESIM without CoVe + fully-aware + multi-level ( [ITALIC] d=250)', '78.2', '77.9'], ['Our ESIM ( [ITALIC] d=300)', '73.9', '73.7'], ['Our ESIM + fully-aware ( [ITALIC] d=250)', '... | Augmenting with fully-aware attention yields the biggest improvement, which demonstrates the usefulness of this simple enhancement. Further improvement is obtained when we use multi-level fusion in our ESIM. Experiments with and without CoVe embedding show similar observations. |
Morphological Inflection Generationwith Hard Monotonic Attention | 1611.01487 | Table 2: Results on the Wiktionary datasets | ['[EMPTY]', 'DE-N', 'DE-V', 'ES-V', 'FI-NA', 'FI-V', 'FR-V', 'NL-V', 'Avg.'] | [['durrettdenero2013', '88.31', '94.76', '99.61', '92.14', '97.23', '98.80', '90.50', '94.47'], ['nicolai-cherry-kondrak:2015:NAACL-HLT', '88.6', '97.50', '99.80', '93.00', '[BOLD] 98.10', '[BOLD] 99.20', '96.10', '96.04'], ['faruquiTND15', '88.12', '[BOLD] 97.72', '[BOLD] 99.81', '95.44', '97.81', '98.82', '96.71', '9... | This shows the robustness of our model also with large amounts of training examples, and the advantage the hard attention mechanism provides over the encoder-decoder approach of \newcitefaruquiTND15 which does not employ an attention mechanism. Our model is also significantly more accurate than the model of \newciteyu2... |
Morphological Inflection Generationwith Hard Monotonic Attention | 1611.01487 | Table 1: Results on the CELEX dataset | ['[EMPTY]', '13SIA', '2PIE', '2PKE', 'rP', 'Avg.'] | [['med Kann and Schütze ( 2016a )', '83.9', '95', '87.6', '84', '87.62'], ['nwfst Rastogi et\xa0al. ( 2016 )', '86.8', '94.8', '87.9', '81.1', '87.65'], ['lat Dreyer et\xa0al. ( 2008 )', '[BOLD] 87.5', '93.4', '87.4', '84.9', '88.3'], ['Soft', '83.1', '93.8', '88', '83.2', '87'], ['Hard', '85.8', '[BOLD] 95.1', '[BOLD]... | In addition, it significantly outperforms our implementation of the soft attention model (Soft). It is also, to our knowledge, the first model that surpassed in overall accuracy the latent variable model on this dataset. We attribute our advantage over the soft attention models to the ability of the hard attention cont... |
Morphological Inflection Generationwith Hard Monotonic Attention | 1611.01487 | Table 3: Results on the SIGMORPHON 2016 morphological inflection dataset. The text above each language lists the morphological phenomena it includes: circ.=circumfixing, agg.=agglutinative, v.h.=vowel harmony, c.h.=consonant harmony | ['[EMPTY]', 'suffixing+stem changes RU', 'suffixing+stem changes DE', 'suffixing+stem changes ES', 'circ. GE', 'suffixing+agg.+v.h. FI', 'suffixing+agg.+v.h. TU', 'suffixing+agg.+v.h. HU', 'c.h.', 'templatic AR', 'templatic MA', 'Avg.'] | [['med', '91.46', '95.8', '98.84', '98.5', '95.47', '98.93', '96.8', '91.48', '[BOLD] 99.3', '[BOLD] 88.99', '95.56'], ['Soft', '92.18', '96.51', '98.88', '[BOLD] 98.88', '[BOLD] 96.99', '[BOLD] 99.37', '[BOLD] 97.01', '[BOLD] 95.41', '[BOLD] 99.3', '88.86', '[BOLD] 96.34'], ['Hard', '[BOLD] 92.21', '[BOLD] 96.58', '[B... | As different languages show different morphological phenomena, we also experiment with how our model copes with these various phenomena using the morphological inflection dataset from the SIGMORPHON2016 shared task Cotterell et al. We compare our model to two soft attention baselines on this dataset: med Kann and Schüt... |
Bilateral Multi-Perspective Matching for Natural Language Sentences | 1702.03814 | Table 1: Ablation studies on the dev set. | ['Models', 'Accuracy'] | [['Only [ITALIC] P→ [ITALIC] Q', '87.74'], ['Only [ITALIC] P← [ITALIC] Q', '87.47'], ['w/o Full-Matching', '87.86'], ['w/o Maxpooling-Matching', '87.64'], ['w/o Attentive-Matching', '87.87'], ['w/o MaxAttentive-Matching', '87.98'], ['Full Model', '[BOLD] 88.69']] | Second, to check the effectiveness of bilateral matching, we build two ablation models to matching sentences in only a single direction: 1) “Only P→Q” which only matches P against Q; 2) Comparing the two ablation models with the “Full Model”, we can observe that single direction matching hurts the performance for about... |
Bilateral Multi-Perspective Matching for Natural Language Sentences | 1702.03814 | Table 2: Performance for paraphrase identification on the Quora dataset. | ['Models', 'Accuracy'] | [['Siamese-CNN', '79.60'], ['Multi-Perspective-CNN', '81.38'], ['Siamese-LSTM', '82.58'], ['Multi-Perspective-LSTM', '83.21'], ['L.D.C.', '85.55'], ['BiMPM', '[BOLD] 88.17']] | Our “BiMPM” model outperforms the “L.D.C.” model by more than two percent. Therefore, our model is very effective for the paraphrase identification task. |
Bilateral Multi-Perspective Matching for Natural Language Sentences | 1702.03814 | Table 3: Performance for natural language inference on the SNLI dataset. | ['Models', 'Accuracy'] | [['', '77.6'], ['', '81.4'], ['', '82.1'], ['', '83.5'], ['', '85.0'], ['', '85.1'], ['', '86.1'], ['', '86.3'], ['', '86.8'], ['', '87.3'], ['', '87.5'], [' (Single)', '87.7'], [' (Ensemble)', '88.3'], ['Only [ITALIC] P→ [ITALIC] Q', '85.6'], ['Only [ITALIC] P← [ITALIC] Q', '86.3'], ['BiMPM', '86.9'], ['BiMPM (Ensem... | First, we can see that “Only P←Q” works significantly better than “Only P→Q”, which tells us that, for natural language inference, matching the hypothesis against the premise is more effective than the other way around. Second, our “BiMPM” model works much better than “Only P←Q”, which reveals that matching premise aga... |
Bilateral Multi-Perspective Matching for Natural Language Sentences | 1702.03814 | Table 4: Performance for answer sentence selection on TREC-QA and WikiQA datasets. | ['Models', 'TREC-QA MAP', 'TREC-QA MRR', 'WikiQA MAP', 'WikiQA MRR'] | [['', '0.695', '0.763', '0.652', '0.665'], ['', '0.728', '0.832', '–', '–'], ['Wang and Itty. wang2015faq', '0.746', '0.820', '–', '–'], ['', '0.753', '0.851', '0.689', '0.696'], ['', '–', '–', '0.692', '0.711'], ['', '–', '–', '0.689', '0.707'], ['', '0.771', '0.845', '0.706', '0.723'], ['', '0.777', '0.836', '0.709',... | In this Sub-section, we study the effectiveness of our model for answer sentence selection tasks. The answer sentence selection task is to rank a list of candidate answer sentences based on their similarities to the question, and the performance is measured by the mean average precision (MAP) and mean reciprocal rank (... |
TextNAS: A Neural Architecture Search Space tailored for Text Representation | 1912.10729 | Table 4: Test accuracy on the text classification datasets. For each dataset, we conduct significance test against the best reproducible model, and * means that the improvement is significant at 0.05 significance level. | ['Model', 'AG', 'Sogou', 'DBP', 'Yelp-B', 'Yelp', 'Yahoo', 'Amz', 'Amz-B'] | [['Zhang ET AL., 2015', '92.36', '[BOLD] 97.19', '98.69', '95.64', '62.05', '71.20', '59.57', '95.07'], ['Joulin ET AL., 2016', '[BOLD] 92.50', '96.80', '98.60', '95.70', '63.90', '72.30', '60.20', '94.60'], ['Conneau ET AL., 2016', '91.33', '96.82', '98.71', '[BOLD] 95.72', '[BOLD] 64.72', '[BOLD] 73.43', '[BOLD] 63.0... | The results demonstrate that the TextNAS model outperforms state-of-the-art methods on all text classification datasets except Sogou. One potential reason is that Sogou is a dataset in Chinese language, while the Glove embedding vectors are trained by English corpus. One can improve the performance by adding Chinese-la... |
TextNAS: A Neural Architecture Search Space tailored for Text Representation | 1912.10729 | Table 3: Results on SST dataset. For each dataset, we conduct significance test against the best reproducible model, and * means that the improvement is significant at 0.05 significance level. | ['Model', 'SST', 'SST-B'] | [['Lai ET AL., 2015', '47.21', '-'], ['Zhou ET AL., 2015', '49.20', '87.80'], ['Liu ET AL., 2016', '49.60', '87.90'], ['Tai ET AL., 2016', '51.00', '88.00'], ['Kumar ET AL., 2016', '[BOLD] 52.10', '[BOLD] 88.60'], ['24-layers Transformer', '49.37', '86.66'], ['ENAS-macro', '51.55', '[BOLD] 88.90'], ['ENAS-micro', '47.0... | We can see that the neural architecture discovered by TextNAS achieves competitive performances compared with state-of-the-art manual architectures, including the 24-layers Transformer adopted by BERT. At the same time, it outperforms other network architectures discovered automatically by other search spaces and algor... |
TextNAS: A Neural Architecture Search Space tailored for Text Representation | 1912.10729 | Table 6: Detailed settings for experiments of text classification. | ['Exp', 'batch size', 'max length', '[ITALIC] l2', 'lr', 'sliding window', 'hidden size'] | [['AG', '128', '256', '1×10−6', '0.02', 'no', '256'], ['Sogou', '64', '1024', '1×10−6', '0.02', 'yes', '32'], ['DBP', '128', '256', '1×10−6', '0.02', 'no', '64'], ['Yelp-B', '128', '512', '1×10−6', '0.02', 'no', '64'], ['Yelp', '128', '512', '1×10−6', '0.02', 'no', '64'], ['Yahoo', '64', '1024', '1×10−6', '0.02', 'yes'... | In all the experiments, we apply dropout (ratio=0.5) to the embedding layers, final output layers and self-attention layers. In addition, in the bidirectional GRU layers, we apply dropout (ratio=0.5) on the input and output tensors. Besides, for several time-consuming experiments, we employ sliding window trick to acce... |
TextNAS: A Neural Architecture Search Space tailored for Text Representation | 1912.10729 | Table 7: Detailed settings for experiments of natural language inference. | ['Exp', 'lr', 'training epoch', '[ITALIC] l2', 'dropout rate', 'penalization'] | [['SNLI', '2×10−4', '8', '2×10−2', '0.2', '0'], ['MNLI', '1×10−4', '20', '1×10−2', '0.2', '0']] | In NLI experiments, we evaluate the result model of TextNAS by training it from scratch. We set the dimension of hidden units as 512 for all layers in the sentence encoder and 2400 for the three fully-connected layers before softmax output. All 24 layers in the sentence encoder are linearly combined to produce the ulti... |
Polyglot Semantic Role Labeling | 1805.11598 | Table 3: Per-label breakdown of F1 scores for Catalan and Spanish. These numbers reflect labels for each argument; the combination is different from the overall semantic F1, which includes predicate sense disambiguation. | ['[EMPTY]', '[BOLD] arg0', '[BOLD] arg1', '[BOLD] arg2', '[BOLD] arg3', '[BOLD] arg4', '[BOLD] arg [ITALIC] L', '[BOLD] arg [ITALIC] M'] | [['Gold label count (cat)', '2117', '4296', '1713', '61', '71', '49', '2968'], ['Monolingual cat\xa0 [ITALIC] F1', '82.06', '79.06', '68.95', '28.89', '42.42', '39.51', '60.85'], ['+ eng\xa0improvement', '+2.75', '+2.58', '+4.53', '+18.17', '+9.81', '+1.35', '+1.10'], ['Gold label count (spa)', '2438', '4295', '1677', ... | Label-wise results. In both languages, we find a small but consistent improvement in the most common label categories (e.g., arg1 and argM). Less common label categories are sensitive to small changes in performance; they have the largest changes in F1 in absolute value, but without a consistent direction. This could b... |
Polyglot Semantic Role Labeling | 1805.11598 | Table 2: Semantic F1 scores (including predicate sense disambiguation) on the CoNLL 2009 dataset. State of the art for Catalan and Japanese is from Zhao2009, for German and Spanish from Roth2016-fn, for English and Chinese from marcheggiani2017gcn. Italics indicate use of syntax. | ['[BOLD] Model', 'cat', 'ces', 'deu', 'eng', 'jpn', 'spa', 'zho'] | [['marcheggiani2017lstm', '-', '86.00', '-', '87.60', '-', '80.30', '81.20'], ['Best previously reported', '[ITALIC] 80.32', '86.00', '[ITALIC] 80.10', '[ITALIC] 89.10', '[ITALIC] 78.15', '[ITALIC] 80.50', '[ITALIC] 81.20'], ['Monolingual', '77.31', '84.87', '66.71', '86.54', '74.99', '75.98', '81.26'], ['+ eng(simple ... | We observe that simple polyglot training improves over monolingual training, with the exception of Czech, where we observe no change in performance. |
Polyglot Semantic Role Labeling | 1805.11598 | Table 4: Semantic F1 scores on the English test set for each language pair. | ['eng-only', '+cat', '+ces', '+deu', '+jpn', '+spa', '+zho'] | [['86.54', '86.79', '87.07', '87.07', '87.11', '87.24', '87.10']] | English SRL consistently benefits from polyglot training, with an increase of 0.25–0.7 absolute F1 points, depending on the language. Surprisingly, Czech provides the smallest improvement, despite the large amount of data added; the absence of crosslingual transfer in both directions for the English-Czech case, breakin... |
Polyglot Semantic Role Labeling | 1805.11598 | Table 5: Unlabeled semantic F1 scores on the CoNLL 2009 dataset. | ['[BOLD] Model', 'cat', 'ces', 'deu', 'eng', 'jpn', 'spa', 'zho'] | [['Monolingual', '93.92', '91.92', '87.95', '92.87', '85.55', '93.61', '87.93'], ['+ eng', '94.09', '91.97', '89.01', '–', '86.17', '93.65', '87.90']] | S4SS0SSS0Px3 Labeled vs. unlabeled F1. As can be seen here, the unlabeled F1 improvements are generally positive but small, indicating that polyglot training can help both in structure prediction and labeling of arguments. The pattern of seeing the largest improvements on the languages with the smallest datasets genera... |
Strongly Incremental Repair Detection | 1408.6788 | Table 3: Comparison of performance of systems with different stack capacities | ['[EMPTY]', 'F [ITALIC] rm', 'F [ITALIC] s', 'DA', 'EO', 'PO', 'TD [ITALIC] rp', 'TD [ITALIC] rm'] | [['1-best [ITALIC] rmstart', '0.745', '0.707', '0.699', '3.780', '1.650', '1.0', '2.6'], ['2-best [ITALIC] rmstart', '0.758', '0.721', '0.701', '4.319', '1.665', '1.1', '2.7']] | Our experiments showed that different system settings perform better in different metrics, and no individual setting achieved the best result in all of them. ’s measure and STIR achieves 0.736 on the previously unevaluated Fs. The fastest average time to detection is 1 word for TDrp and 2.6 words for TDrm We make a pre... |
Procedural Reasoning Networks for Understanding Multimodal Procedures | 1909.08859 | Table 1: Quantitative comparison of the proposed PRN model against the baselines. | ['Model', 'Single-task Training Cloze', 'Single-task Training Coherence', 'Single-task Training Ordering', 'Single-task Training Average', 'Multi-task Training Cloze', 'Multi-task Training Coherence', 'Multi-task Training Ordering', 'Multi-task Training All'] | [['Human∗', '77.60', '81.60', '64.00', '74.40', '–', '–', '–', '–'], ['Hasty Student', '27.35', '[BOLD] 65.80', '40.88', '44.68', '–', '–', '–', '–'], ['Impatient Reader', '27.36', '28.08', '26.74', '27.39', '–', '–', '–', '–'], ['BIDAF', '53.95', '48.82', '62.42', '55.06', '44.62', '36.00', '[BOLD] 63.93', '48.67'], [... | In single-task training setting, PRN gives state-of-the-art results compared to other neural models. Moreover, it achieves the best performance on average. These results demonstrate the importance of having a dynamic memory and keeping track of entities extracted from the recipe. In multi-task training setting where a ... |
A Unified Linear-Time Framework for Sentence-Level Discourse Parsing | 1905.05682 | Table 4: Speed comparison of our systems with other open-sourced systems. | ['[BOLD] System', '[BOLD] Speed (Sents/s)', '[BOLD] Speedup'] | [['[BOLD] Only Segmenter', '[EMPTY]', '[EMPTY]'], ['CODRA Joty et\xa0al. ( 2015 )', '3.06', '1.0x'], ['WLY Wang et\xa0al. ( 2018 )', '4.30', '1.4x'], ['SPADE Soricut and Marcu ( 2003 )', '5.24', '1.7x'], ['Our (CPU)', '12.05', '3.9x'], ['Our (GPU)', '35.54', '11.6x'], ['[BOLD] Only Parser', '[EMPTY]', '[EMPTY]'], ['SPA... | As noted earlier, both our segmenter and parser operate in linear time with respect to the number of input units. We test all the systems with the same 100 sentences randomly selected from our test set on our machine (CPU: Intel Xeon W-2133, GPU: NVIDIA GTX 1080Ti). We include the model loading time for all the systems... |
A Unified Linear-Time Framework for Sentence-Level Discourse Parsing | 1905.05682 | Table 1: Discourse segmentation results. Superscript ⋆ indicates the model is significantly superior to the WLYELMo model with a p-value <0.01. | ['[BOLD] Approach', '[BOLD] Precision', '[BOLD] Recall', '[BOLD] F1'] | [['[BOLD] Human Agreement', '98.5', '98.2', '98.3'], ['[BOLD] Baselines', '[EMPTY]', '[EMPTY]', '[EMPTY]'], ['SPADE Soricut and Marcu ( 2003 )', '83.8', '86.8', '85.2'], ['F&R Fisher and Roark ( 2007 )', '91.3', '89.7', '90.5'], ['JCN Joty et\xa0al. ( 2012 )', '88.0', '92.3', '90.1'], ['SegBotglove Li et\xa0al. ( 2018 ... | Using encoder hidden states as decoder inputs and adopting dot product as the attention score function together gives 0.40%-7.29% relative improvement in F1 over the first four baselines. Using ELMo, our segmenter outperforms all the baselines in all three measures. We achieve 2.3%-11.9%, 2.4%-11.3% and 2.3%-12.3% rela... |
Towards Generating Long and Coherent Text with Multi-Level Latent Variable Models | 1902.00154 | Table 7: Human evaluations on Yelp Reviews dataset. Each block is a head-to-head comparison of two models on grammatically, consistency, and non-redundancy. | ['[BOLD] Model', '[BOLD] Grammar.', '[BOLD] Cons.', '[BOLD] Non-Red.', '[BOLD] Overall'] | [['[ITALIC] ml-VAE', '52.0', '55.0', '53.7', '60.0'], ['[ITALIC] flat-VAE', '30.0', '33.0', '27.7', '32.3'], ['[ITALIC] ml-VAE', '75.3', '86.0', '76.7', '86.0'], ['AAE', '13.3', '10.3', '15.0', '12.0'], ['[ITALIC] flat-VAE', '19.7', '18.7', '14.3', '19.0'], ['Real data', '61.7', '74.7', '74.3', '77.7'], ['[ITALIC] ml-V... | As shown in Even though both models underperform when compared against the ground-truth real reviews, ml-VAE was rated higher in comparison to flat-VAE (raters find ml-VAE closer to human-generated than the flat-VAE) in all the criteria evaluation criteria. When compared against AAE baseline models using the same data ... |
Towards Generating Long and Coherent Text with Multi-Level Latent Variable Models | 1902.00154 | Table 2: Language modeling results on Yelp and arXiv data. Upper block are baselines, and lower are our models. | ['[BOLD] Model', '[BOLD] Yelp [BOLD] NLL', '[BOLD] Yelp [BOLD] KL', '[BOLD] Yelp [BOLD] PPL', '[BOLD] arXiv [BOLD] NLL', '[BOLD] arXiv [BOLD] KL', '[BOLD] arXiv [BOLD] PPL'] | [['[ITALIC] flat-LM', '162.6', '-', '48.0', '218.7', '-', '57.6'], ['[ITALIC] flat-VAE', '≤ 163.1', '0.01', '≤ 49.2', '≤ 219.5', '0.01', '≤ 58.4'], ['[ITALIC] ml-LM', '162.4', '-', '47.9', '219.3', '-', '58.1'], ['[ITALIC] ml-VAE-S', '≤ 160.8', '3.6', '≤ 46.6', '≤ 216.8', '5.3', '≤ 55.6'], ['[ITALIC] ml-VAE-D', '≤ [BO... | The flat-VAE model obtains slightly worse NNL and PPL relative to a flat LSTM-based language model. With multi-level LSTM decoder, our ml-VAE-S yields increased KL divergence, demonstrating that the VAE model tends to leverage more information from the latent variable in the decoding stage. The PPL of ml-VAE-S is also ... |
Towards Generating Long and Coherent Text with Multi-Level Latent Variable Models | 1902.00154 | Table 3: Evaluation results for generated sequences by our models and baselines on corpus-level BLEU scores (B-n denotes the corpus-level BLEU-n score.) | ['[BOLD] Model', '[BOLD] Yelp [BOLD] B-2', '[BOLD] Yelp [BOLD] B-3', '[BOLD] Yelp [BOLD] B-4', '[BOLD] arXiv [BOLD] B-2', '[BOLD] arXiv [BOLD] B-3', '[BOLD] arXiv [BOLD] B-4'] | [['[ITALIC] ARAE', '0.684', '0.524', '0.350', '0.624', '0.475', '0.305'], ['[ITALIC] AAE', '0.735', '0.623', '0.383', '0.729', '0.564', '0.342'], ['[ITALIC] flat-VAE', '0.855', '0.705', '0.515', '0.784', '0.625', '0.421'], ['[ITALIC] ml-VAE-S', '0.901', '0.744', '0.531', '0.821', '[BOLD] 0.663', '0.447'], ['[ITALIC] ml... | VAE tends to be a stronger baseline for paragraph generation, exhibiting higher corpus-level BLEU scores than both AAE and ARAE. The VAE with multi-level decoder demonstrates better BLEU scores than the one with a flat decoder, indicating that the plan-ahead mechanism associated with the hierarchical decoding process i... |
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