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A FOFE-based Local Detection Approach for Named Entity Recognition and Mention Detection | 1611.00801 | Table 5: Official entity discovery performance of our methods on KBP2016 trilingual EDL track. | ['LANG', 'NAME P', 'NAME R', 'NAME F1', 'NOMINAL P', 'NOMINAL R', 'NOMINAL F1', 'OVERALL P', 'OVERALL R', 'OVERALL F1'] | [['[EMPTY]', 'RUN1 (our official ED result in KBP2016 EDL2)', 'RUN1 (our official ED result in KBP2016 EDL2)', 'RUN1 (our official ED result in KBP2016 EDL2)', 'RUN1 (our official ED result in KBP2016 EDL2)', 'RUN1 (our official ED result in KBP2016 EDL2)', 'RUN1 (our official ED result in KBP2016 EDL2)', 'RUN1 (our of... | After fixing some system bugs, we have used both the KBP2015 data and iFLYTEK data to re-train our models for three languages and finally submitted three systems to the final KBP2016 EDL2 evaluation. In our systems, we treat all nominal mentions as special types of named entities and both named and nominal entities are... |
A FOFE-based Local Detection Approach for Named Entity Recognition and Mention Detection | 1611.00801 | Table 3: Entity Discovery Performance of our method on the KBP2015 EDL evaluation data, with comparison to the best system in KBP2015 official evaluation. | ['[EMPTY]', '2015 track best [ITALIC] P', '2015 track best [ITALIC] R', '2015 track best [ITALIC] F1', 'ours [ITALIC] P', 'ours [ITALIC] R', 'ours [ITALIC] F1'] | [['Trilingual', '75.9', '69.3', '72.4', '78.3', '69.9', '[BOLD] 73.9'], ['English', '79.2', '66.7', '[BOLD] 72.4', '77.1', '67.8', '72.2'], ['Chinese', '79.2', '74.8', '[BOLD] 76.9', '79.3', '71.7', '75.3'], ['Spanish', '78.4', '72.2', '75.2', '79.9', '71.8', '[BOLD] 75.6']] | The overall trilingual entity discovery performance is slightly better than the best system participated in the official KBP2015 evaluation, with 73.9 vs. 72.4 as measured by F1 scores. |
A FOFE-based Local Detection Approach for Named Entity Recognition and Mention Detection | 1611.00801 | Table 4: Entity discovery performance (English only) in KBP2016 EDL1 evaluation window is shown as a comparison of three models trained by different combinations of training data sets. | ['training data', 'P', 'R', '[ITALIC] F1'] | [['KBP2015', '0.818', '0.600', '0.693'], ['KBP2015 + WIKI', '0.859', '0.601', '0.707'], ['KBP2015 + iFLYTEK', '0.830', '0.652', '[BOLD] 0.731']] | In our first set of experiments, we investigate the effect of using different training data sets on the final entity discovery performance. Different training runs are conducted on different combinations of the aforementioned data sources. The first system, using only the KBP2015 data to train the model, has achieved 0... |
Learning from Explanations with Neural Execution Tree | 1911.01352 | Table 2: Experiment results on Relation Extraction and Sentiment Analysis. Average and standard deviation of F1 scores (%) over multiple runs are reported (5 runs for RE and 10 runs for SA). LF(E) denotes directly applying logical forms onto explanations. Bracket behind each method illustrates corresponding data used i... | ['[EMPTY]', 'TACRED', 'SemEval'] | [['LF (E)', '23.33', '33.86'], ['CBOW-GloVe (R+S)', '34.6±0.4', '48.8±1.1'], ['PCNN (S [ITALIC] a)', '34.8±0.9', '41.8±1.2'], ['PA-LSTM (S [ITALIC] a)', '41.3±0.8', '57.3±1.5'], ['BiLSTM+ATT (S [ITALIC] a)', '41.4±1.0', '58.0±1.6'], ['BiLSTM+ATT (S [ITALIC] l)', '30.4±1.4', '54.1±1.0'], ['Self Training (S [ITALIC] a+S ... | We observe that our proposed NExT consistently outperforms all baseline models in low-resource setting. Also, we found that, (1) directly applying logical forms to unlabeled data results in poor performance. We notice that this method achieves high precision but low recall. Based on our observation of the collected dat... |
Learning from Explanations with Neural Execution Tree | 1911.01352 | Table 2: Experiment results on Relation Extraction and Sentiment Analysis. Average and standard deviation of F1 scores (%) over multiple runs are reported (5 runs for RE and 10 runs for SA). LF(E) denotes directly applying logical forms onto explanations. Bracket behind each method illustrates corresponding data used i... | ['[EMPTY]', 'Restaurant', 'Laptop'] | [['LF (E)', '7.7', '13.1'], ['CBOW-GloVe (R+S)', '68.5±2.9', '61.5±1.3'], ['PCNN (S [ITALIC] a)', '72.6±1.2', '60.9±1.1'], ['ATAE-LSTM (S [ITALIC] a)', '71.1±0.4', '56.2±3.6'], ['ATAE-LSTM (S [ITALIC] l)', '71.4±0.5', '52.0±1.4'], ['Self Training (S [ITALIC] a+S [ITALIC] u)', '71.2±0.5', '57.6±2.1'], ['Pseudo Labeling ... | We observe that our proposed NExT consistently outperforms all baseline models in low-resource setting. Also, we found that, (1) directly applying logical forms to unlabeled data results in poor performance. We notice that this method achieves high precision but low recall. Based on our observation of the collected dat... |
Learning from Explanations with Neural Execution Tree | 1911.01352 | Table 4: Performance of NLProlog when extracted facts are used as input. Average accuracy over 3 runs is reported. NLProlog empowered by 21 natural language explanations and 5 hand-written rules achieves 1% gain in accuracy. | ['[EMPTY]', '|S [ITALIC] a|', '|S [ITALIC] u|', 'Accuracy'] | [['NLProlog (published code)', '0', '0', '74.57'], ['+ S [ITALIC] a', '103', '0', '74.40'], ['+ S [ITALIC] u (confidence >0.3)', '103', '340', '74.74'], ['+ S [ITALIC] u (confidence >0.2)', '103', '577', '75.26'], ['+ S [ITALIC] u (confidence >0.1)', '103', '832', '[BOLD] 75.60']] | From the result we observe that simply adding the 103 strictly-matched facts is not making notable improvement. However, with the help of NExT, a larger number of structured facts are recognized from support sentences, so that external knowledge from only 21 explanations and 5 rules improve the accuracy by 1 point. Thi... |
Learning from Explanations with Neural Execution Tree | 1911.01352 | Table 8: TACRED results on 130 explanations and 100 explanations | ['Metric', 'TACRED 130 Precision', 'TACRED 130 Recall', 'TACRED 130 F1', 'TACRED 100 Precision', 'TACRED 100 Recall', 'TACRED 100 F1'] | [['LF (E)', '[BOLD] 83.5', '12.8', '22.2', '[BOLD] 85.2', '11.8', '20.7'], ['CBOW-GloVe (R+S)', '26.0±2.3', '[BOLD] 39.9±5.0', '31.2±0.5', '24.4±1.3', '[BOLD] 41.7±3.7', '30.7±0.1'], ['PCNN (S [ITALIC] a)', '41.8±2.7', '28.8±1.8', '34.1±1.1', '28.2±3.4', '22.2±1.3', '24.8±1.9'], ['PA-LSTM (S [ITALIC] a)', '44.9±1.7', '... | As a supplement to Fig. Results show that our model achieves best performance compared with baseline methods. |
Learning from Explanations with Neural Execution Tree | 1911.01352 | Table 9: SemEval results on 150 explanations and 100 explanations | ['Metric', 'SemEval 150 Precision', 'SemEval 150 Recall', 'SemEval 150 F1', 'SemEval 100 Precision', 'SemEval 100 Recall', 'SemEval 100 F1'] | [['LF (E)', '[BOLD] 85.1', '17.2', '28.6', '[BOLD] 90.7', '9.0', '16.4'], ['CBOW-GloVe (R+S)', '44.8±1.9', '48.6±1.5', '46.6±1.1', '36.0±1.4', '40.2±2.0', '37.9±0.1'], ['PCNN (S [ITALIC] a)', '49.1±3.9', '36.1±2.4', '41.5±1.4', '43.3±1.4', '27.9±1.0', '33.9±0.3'], ['PA-LSTM (S [ITALIC] a)', '58.0±1.2', '52.5±0.4', '55.... | As a supplement to Fig. Results show that our model achieves best performance compared with baseline methods. |
Learning from Explanations with Neural Execution Tree | 1911.01352 | Table 10: Laptop results on 55 explanations and 70 explanations | ['Metric', 'Laptop 55 Precision', 'Laptop 55 Recall', 'Laptop 55 F1', 'Laptop 70 Precision', 'Laptop 70 Recall', 'Laptop 70 F1'] | [['LF (E)', '[BOLD] 90.8', '9.2', '16.8', '[BOLD] 89.4', '9.2', '16.8'], ['CBOW-GloVe (R+S)', '53.7±0.2', '72.9±0.2', '61.8±0.2', '53.6±0.3', '72.4±0.2', '61.6±0.2'], ['PCNN (S [ITALIC] a)', '53.5±3.3', '71.0±3.6', '61.0±3.2', '55.6±1.9', '74.1±1.9', '63.5±1.5'], ['ATAE-LSTM (S [ITALIC] a)', '53.5±0.4', '71.9±2.2', '61... | As a supplement to Fig. Results show that our model achieves best performance compared with baseline methods. |
Learning from Explanations with Neural Execution Tree | 1911.01352 | Table 11: Restaurant results on 60 explanations and 75 explanations | ['Metric', 'Restaurant 60 Precision', 'Restaurant 60 Recall', 'Restaurant 60 F1', 'Restaurant 75 Precision', 'Restaurant 75 Recall', 'Restaurant 75 F1'] | [['LF (E)', '[BOLD] 86.0', '3.8', '7.4', '[BOLD] 85.4', '6.8', '12.6'], ['CBOW-GloVe (R+S)', '63.7±2.3', '75.6±1.3', '69.1±1.9', '64.1±1.3', '76.6±0.1', '69.8±0.7'], ['PCNN (S [ITALIC] a)', '67.0±0.9', '81.0±1.0', '73.3±0.9', '68.4±0.1', '[BOLD] 82.8±0.3', '74.9±0.2'], ['ATAE-LSTM (S [ITALIC] a)', '65.2±0.6', '78.5±0.2... | As a supplement to Fig. Results show that our model achieves best performance compared with baseline methods. |
Learning from Explanations with Neural Execution Tree | 1911.01352 | Table 12: BERT experiments on Restaurant dataset using 45 and 75 explanations | ['[EMPTY]', '45', '75'] | [['ATAE-LSTM (S [ITALIC] a)', '79.9', '80.6'], ['Self Training (S [ITALIC] a+S [ITALIC] u)', '80.9', '81.1'], ['Pseudo Labeling (S [ITALIC] a+S [ITALIC] u)', '78.7', '81.0'], ['Mean Teacher (S [ITALIC] a+S [ITALIC] u)', '79.3', '79.8'], ['NExT (E+S)', '[BOLD] 81.4', '[BOLD] 82.0']] | Our framework is model-agnostic as it can be integrated with any downstream classifier. Results show that our model still outperforms baseline methods when BERT is incorporated. We observe the performance of NExT is approaching the upper bound 85% (by feeding all data to BERT), with only 75 explanations, which again de... |
Better Early than Late: Fusing Topics with Word Embeddingsfor Neural Question Paraphrase Identification | 2007.11314 | Table 3: Ablation study for our TAPA model reporting F1 scores on test sets. | ['[EMPTY]', 'PAWS', 'Quora', 'Sem-Eval'] | [['full TAPA (early fusion)', '42.2', '84.1', '46.4'], ['-topics', '40.6', '83.9', '45.1'], ['-ELMO', '26.9', '84.5', '45.0'], ['TAPA with late fusion', '39.8', '83.9', '40.1']] | Removing topics consistently reduces F1 scores on all datasets, while the effect of ELMo representations is dataset dependent. Deleting ELMo improves performance on Quora, but leads to a massive performance drop on PAWS. The large impact on PAWS can be explained by the fact that this dataset was automatically construct... |
Sample Efficient Text Summarization Using a Single Pre-Trained Transformer | 1905.08836 | Table 1: Summarization results when using the full training set. Our scores are averaged over three models trained with different random seeds. *Other abstractive summarization model scores are provided to contextualize performance on this task but are not directly comparable to our models. | ['[BOLD] Model', '[BOLD] R1', '[BOLD] R2', '[BOLD] RL'] | [['Other Abs. Sum. models*', '[EMPTY]', '[EMPTY]', '[EMPTY]'], ['Celikyilmaz et\xa0al. ( 2018 )', '41.69', '19.47', '37.92'], ['CopyTransformer (4-layer)', '39.25', '17.54', '36.45'], ['Gehrmann et\xa0al. ( 2018 )', '[EMPTY]', '[EMPTY]', '[EMPTY]'], ['GPT-2 (48-layer, zero-shot)', '29.34', '08.27', '26.58'], ['Radford ... | We find that pre-training improves performance by about 2 ROUGE points, on average. Surprisingly, when only the decoder is pre-trained, ROUGE gets substantially worse. We speculate this is because the model starting out with a well-trained decoder and poor encoder learns to overly rely on its language modeling abilitie... |
1 Introduction | 2002.01535 | Table 5: Document classification results, reporting number of parameters and accuracy. | ['Representation', 'Parameters', 'Accuracy'] | [['Recurrent', '509.6 K', '[BOLD] 87.4'], ['Convolutional', '2.5 M', '85.7'], ['+ non-linearity', '1.3 M', '87.3'], ['+ separability', '234.4 K', '86.7'], ['+ bottlenecks', '[BOLD] 92.5 K', '86.4']] | Our optimized representation is significantly smaller than the baselines, using 5x and 27x less parameters than the recurrent and convolutional baselines respectively. Even with far less capacity, the optimized model’s accuracy is comparable to the baselines. Specifically, it only drops by 1% in comparison to the recur... |
DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset | 1710.03957 | Table 4: Experiments Results of generation-based approaches. | ['[EMPTY]', 'Epoch', 'Test Loss', 'PPL', 'BLEU-1', 'BLEU-2', 'BLEU-3', 'BLEU-4'] | [['Seq2Seq', '30', '4.024', '55.94', '0.352', '0.146', '0.017', '0.006'], ['Attn-Seq2Seq', '60', '4.036', '56.59', '0.335', '0.134', '0.013', '0.006'], ['HRED', '44', '4.082', '59.24', '0.396', '0.174', '0.019', '0.009'], ['L+Seq2Seq', '21', '3.911', '49.96', '0.379', '0.156', '0.018', '0.006'], ['L+Attn-Seq2Seq', '37'... | Intention and Emotion-enhanced To utilize the intention and emotion labels, we follow Zhou et al. The intention and emotion labels are characterized as one-hot vectors. (last four columns), we can see that in general attention-based approaches are better than vanilla Seq2Seq model. Among the three compared approaches, ... |
DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset | 1710.03957 | Table 5: BLEU scores of retrieval-based approaches. | ['[EMPTY]', 'BLEU-2', 'BLEU-3', 'BLEU-4'] | [['Embedding', '0.207', '0.162', '0.150'], ['[BOLD] Feature', '[BOLD] 0.258', '[BOLD] 0.204', '[BOLD] 0.194'], ['+ I-Rerank', '0.204', '0.189', '0.181'], ['+ I-E-Rerank', '0.190', '0.174', '0.164']] | Because the groundtruth responses in the test set are not seen in the training set, we can not evaluate the performance using ranking-like metrics such as Recall-k. |
DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset | 1710.03957 | Table 6: “Equivalence” percentage (%) of retrieval-based approaches. | ['[EMPTY]', 'Feature', '+I-Rerank', '+I-E-Rerank'] | [['Intention', '46.3', '[BOLD] 47.3', '46.7'], ['Emotion', '73.7', '72.3', '[BOLD] 74.3']] | We also evaluate them by calculating the “Equivalence” percentage between the labels (i.e., intention, emotion) of the retrieved responses and those of the groundtruth responses. Though subtle improvements can be seen when using labels, we speculate it as not a very strong evaluation metric. It is unsafe to conclude th... |
Mono vs Multilingual Transformer-based Models: a Comparison across Several Language Tasks | 2007.09757 | Table 9: Comparison of the 3 models across all tasks | ['Model', 'BertPT >', 'BertPT =', 'BertPT ', 'AlbertPT >', 'AlbertPT =', 'AlbertPT ', 'Multilingual >', 'Multilingual =', 'Multilingual '] | [['Baseline', '3', '12', '8', '1', '8', '14', '1', '13', '9'], ['BertPT', '[EMPTY]', '[EMPTY]', '[EMPTY]', '4', '28', '6', '2', '24', '12'], ['AlbertPT', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]', '1', '30', '7']] | The different tasks and settings under which BertPT and AlbertPT were compared to Multilingual BERT and published baselines yield 38 possible pairwise comparisons. We analyzed in how many times the performance of each model was better than, worse than or equivalent to the performance of the others. If the proportional ... |
Mono vs Multilingual Transformer-based Models: a Comparison across Several Language Tasks | 2007.09757 | Table 2: RTE and STS evaluated on Test Sets. | ['Data', 'Model', 'RTE Acc', 'RTE F1-M', 'STS Pearson', 'STS MSE'] | [['EP', 'FialhoMarquesMartinsCoheurQuaresma2016', '0.84', '0.73', '0.74', '0.60'], ['EP', 'BertPT', '0.84', '0.69', '0.79', '0.54'], ['EP', 'AlbertPT', '0.88', '0.78', '0.80', '0.47'], ['EP', 'Multilingual', '[BOLD] 0.89', '[BOLD] 0.81', '[BOLD] 0.84', '[BOLD] 0.43'], ['BP', 'FialhoMarquesMartinsCoheurQuaresma2016', '0... | Next, we applied our fine-tuned model to the entire training set and ran the evaluation over the test set. The first observation is that the BP setting is difficult for all models. The EP+BP * * in the last rows indicates that the model was trained using both EP and BP training sets, but they were evaluated only using ... |
Mono vs Multilingual Transformer-based Models: a Comparison across Several Language Tasks | 2007.09757 | Table 5: Sentiment Polarity Classification | ['Model', 'Acc', 'F1-W'] | [['araujo2016@sac', '-', '0.71'], ['BertPT', '0.77', '0.76'], ['AlbertPT', '[BOLD] 0.79', '[BOLD] 0.78'], ['Multilingual', '0.71', '0.70']] | In order keep the results comparable, we use only the positive and negative samples. The fact that this corpus has several emoticons and out-of-vocabulary expressions makes it hard for the models that were not trained using a similar vocabulary. |
Mono vs Multilingual Transformer-based Models: a Comparison across Several Language Tasks | 2007.09757 | Table 8: Emotion Classification | ['Model', '6 classes Acc', '6 classes F1-W', 'Binary Acc', 'Binary F1-W'] | [['Becker:2017:MEC:3063600.3063706', '-', '-', '-', '[BOLD] 0.84'], ['BertPT', '[BOLD] 0.51', '[BOLD] 0.47', '[BOLD] 0.84', '0.83'], ['AlbertPT', '0.41', '0.28', '[BOLD] 0.84', '0.81'], ['Multilingual', '0.49', '0.46', '[BOLD] 0.84', '0.80']] | The baseline for the binary version reaches up to 0.84 of F-measure. In this experiment, BertPT has a result similar to the baseline, but it was not able to overcome it. |
Yin and Yang: Balancing and Answering Binary Visual Questions | 1511.05099 | Table 2: Evaluation on balanced test set. All accuracies are calculated using the VQA [2] evaluation metric. | ['[EMPTY]', 'Training set Unbalanced', 'Training set Balanced'] | [['Prior (“no”)', '63.85', '63.85'], ['Blind-Q+Tuple', '65.98', '63.33'], ['[ITALIC] SOTA Q+Tuple+H-IMG', '65.89', '71.03'], ['[ITALIC] Ours Q+Tuple+A-IMG', '[BOLD] 68.08', '[BOLD] 74.65']] | We also evaluate all models trained on the train splits of both the unbalanced and balanced datasets, by testing on the balanced test set. Training on balanced is better. both language+vision models trained on balanced data perform better than the models trained on unbalanced data. This may be because the models traine... |
Yin and Yang: Balancing and Answering Binary Visual Questions | 1511.05099 | Table 3: Classifying a pair of complementary scenes. All accuracies are percentage of test pairs that have been predicted correctly. | ['[EMPTY]', 'Training set Unbalanced', 'Training set Balanced'] | [['Blind-Q+Tuple', '0', '0'], ['Q+Tuple+H-IMG', '03.20', '23.13'], ['Q+Tuple+A-IMG', '[BOLD] 09.84', '[BOLD] 34.73']] | We observe that our model trained on the balanced dataset performs the best. (Q+Tuple+H-IMG) that does not model attention. |
Structural-Aware Sentence Similarity with Recursive Optimal Transport | 2002.00745 | Table 3: Ablation Study for 4 Aspects of ROTS. Bold values indicates the best in the row | ['Comments', 'Dataset', 'd=1', 'd=2', 'd=3', 'd=4', 'd=5', 'ROTS', 'uSIF'] | [['ROTS + ParaNMT Vectors + Dependency Tree', 'STSB dev', '84.5', '84.6', '84.6', '84.5', '84.4', '[BOLD] 84.6', '84.2'], ['ROTS + ParaNMT Vectors + Dependency Tree', 'STSB test', '80.5', '80.6', '80.6', '80.5', '80.4', '[BOLD] 80.6', '79.5'], ['ROTS + ParaMNT Vectors + Binary Tree', 'STSB dev', '84.2', '84.3', '84.3',... | The dependency tree’s score is higher than that from binary tree, while it slightly decrease after 3-th level. In the first four cases that we could see the results by changing the word vectors and tree structures. We conclude that almost at every case our approach consistently outperformed uSIF and ParaMNT and Depende... |
Structural-Aware Sentence Similarity with Recursive Optimal Transport | 2002.00745 | Table 1: Weakly Supervised Model Results on STS-Benchmark Dataset. | ['Weakly Supervised Model', 'Dev', 'Test'] | [['InferSent (bi-LSTM trained on SNLI)\xa0', '80.1', '75.8'], ['Sent2vec\xa0', '78.7', '75.5'], ['Conversation response prediction + SNLI\xa0', '81.4', '78.2'], ['SIF on Glove vectors\xa0', '80.1', '72.0'], ['GRAN (uses SimpWiki)\xa0', '81.8', '76.4'], ['Unsupervised SIF + ParaNMT vectors\xa0', '84.2', '79.5'], ['GEM\x... | The benchmark comparisons are focused on weakly supervised models with no parameters to train but only hyper-parameters to select. The results that we compared are gathered from either the leaderboard in STS-Benchmark website or from directly the best reported models in the paper The ROTS model gets much more improveme... |
Structural-Aware Sentence Similarity with Recursive Optimal Transport | 2002.00745 | Table 2: Detailed Comparisons with Similar Unsupervised Approaches on 20 STS Datasets | ['Model Type', 'Senrence Similarity', 'STS12', 'STS13', 'STS14', 'STS15', 'AVE'] | [['OT based', 'WMDDBLP:conf/icml/KusnerSKW15', '60.6', '54.5', '65.5', '61.8', '60.6'], ['OT based', 'WMEDBLP:conf/emnlp/WuYXXBCRW18', '62.8', '65.3', '68', '64.2', '65.1'], ['OT based', 'CoMBDBLP:conf/iclr/SinghHDJ19', '57.9', '64.2', '70.3', '73.1', '66.4'], ['Weighted average', 'SIFDBLP:conf/iclr/AroraLM17', '59.5',... | While our HOT approaches almost steadily outperformed uSIF and has best average scores. Notebly, best DynaMax model also employs the same uSIF weights as well as ParaMNT word vectors. So the improvement of ROTS over uSIF and DynaMax is fair and clear. |
Automated Chess Commentator Powered by Neural Chess Engine | 1909.10413 | Table 2: Human evaluation results. Models marked with * are evaluated only for the Description, Quality, and Comparison categories. The underlined results are significantly worse than those of SCC-mult(*) in a two-tail T-test (p<0.01). | ['[BOLD] Models', '[BOLD] Fluency', '[BOLD] Accuracy', '[BOLD] Insights', '[BOLD] Overall'] | [['[BOLD] Ground Truth', '[BOLD] 4.02', '[BOLD] 3.88', '[BOLD] 3.58', '[BOLD] 3.84'], ['[BOLD] Temp', '[BOLD] 4.05', '[BOLD] 4.03', '3.02', '3.56'], ['[BOLD] Re', '3.71', '3.00', '2.80', '2.85'], ['[BOLD] KWG', '3.51', '3.24', '2.93', '3.00'], ['[BOLD] SCC-weak', '3.63', '3.62', '3.32', '3.30'], ['[BOLD] SCC-strong', '... | The human annotators are required to be good at playing chess. That is to say, they are the true audiences of the commentator researches and applications. By introducing human evaluations, we further reveal the performances in the perspective of the audiences. We further demonstrate the efficacy of our models with sign... |
Automated Chess Commentator Powered by Neural Chess Engine | 1909.10413 | Table 1: Automatic evaluation results. | ['[BOLD] BLEU-4 (%)', '[BOLD] Temp', '[BOLD] Re', '[BOLD] KWG', '[BOLD] GAC', '[BOLD] SCC-weak', '[BOLD] SCC-strong', '[BOLD] SCC-mult'] | [['[BOLD] Description', '0.82', '1.24', '1.22', '[BOLD] 1.42', '1.23', '1.31', '1.34'], ['[BOLD] Quality', '13.71', '4.91', '13.62', '16.90', '16.83', '18.87', '[BOLD] 20.06'], ['[BOLD] Comparison', '0.11', '1.03', '1.07', '1.37', '2.33', '[BOLD] 3.05', '2.53'], ['[BOLD] Planning', '0.05', '0.57', '0.84', '[EMPTY]', '[... | Our SCC models outperform all of the baselines and previous state-of-the-art models. Temp is limited by the variety of templates. It is competitive with the neural models on Description and Quality due to limited expressions in these tasks. But when coming to Comparison, Planning and Contexts, Temp shows really bad per... |
SentiCite \subtitleAn Approach for Publication Sentiment Analysis | 1910.03498 | Table 3: Distribution of positive and negative references in different sections of publications. | ['[BOLD] Section', '[BOLD] Positive', '[BOLD] Negative'] | [['Introduction / Motivation', '0.22', '0.28'], ['Information / Background', '0.2', '0.06'], ['Related Work', '0.11', '0.06'], ['Approach / Method', '[BOLD] 0.3', '0.1'], ['Evaluation / Experiments', '0.17', '[BOLD] 0.5']] | This analysis presents a general behavior for this research area based on the dataset. This shows that the negative references are often in the evaluation section of a paper because the methods, in general, get outperformed by the proposed systems. In contrast to this, the positive references occur frequently in the pr... |
SentiCite \subtitleAn Approach for Publication Sentiment Analysis | 1910.03498 | Table 2: SentiCiteDB. | ['[EMPTY]', '[BOLD] Total', '[BOLD] Train set', '[BOLD] Test set'] | [['Positive', '210', '50', '160'], ['Neutral', '1805', '50', '1755'], ['Negative', '85', '50', '35'], ['Overall', '2100', '150', '1983']] | SentiCiteDB is a dataset of publication sentiment analysis which is created using Scientific publications from International Conference on Document Analysis and Recognition (ICDAR) 2013. Sentences with citation are manually extracted from the publication and their manual ground truth is created. In total, SentiCiteDB c... |
SentiCite \subtitleAn Approach for Publication Sentiment Analysis | 1910.03498 | Table 5: Evaluation of different features for SentiCite (SC). | ['[BOLD] Label', '[BOLD] SC-SVM', '[BOLD] SC-Paum'] | [['Only POS', '0.7241', '0.7336'], ['Combination', '[BOLD] 0.7260', '[BOLD] 0.8154']] | The purpose of the feature extraction step is to get a meaningful set of features to train the classifiers. Besides the string of the tokens, three additional feature extraction modules were used. The POS tagger assigns different features e.g., type of the token, length or capitalization. The type of the token helps to... |
SentiCite \subtitleAn Approach for Publication Sentiment Analysis | 1910.03498 | Table 6: Evaluation of different test corpus size for SentiCite (SC). | ['[BOLD] Approach', '[BOLD] 5 docs', '[BOLD] 10 docs', '[BOLD] 20 docs', '[BOLD] 30 docs'] | [['SC-SVM', '0.7111', '0.7091', '0.7141', '0.7203'], ['SC-Paum', '0.5727', '0.7795', '0.8218', '0.8221']] | In the next experiment, the impact of the corpus size was tested. The results show that for the SVM the f-score is almost the same for each run and that the corpus size has no impact. In addition, it shows that the perceptron performance increased in the beginning. |
SentiCite \subtitleAn Approach for Publication Sentiment Analysis | 1910.03498 | Table 9: Ten run k-fold cross-fold for SentiCite (SC). | ['[BOLD] Algorithm', '[BOLD] F1', '[BOLD] F2', '[BOLD] F3', '[BOLD] F4', '[BOLD] F5', '[BOLD] F6', '[BOLD] F7', '[BOLD] F8', '[BOLD] F9', '[BOLD] F10', '[BOLD] Overall'] | [['SC-SVM', '0.47', '0.6', '0.53', '0.67', '0.6', '0.47', '0.47', '0.6', '0.4', '0.53', '0.53'], ['SC-Paum', '0.4', '0.53', '0.47', '0.6', '0.6', '0.67', '0.53', '0.53', '0.4', '0.6', '0.53']] | Finally, to validate that the results of the approach are correct a cross validation was done. The cross validation was done with the training corpus to have a balanced number of all classes. Therefore, the classifier was trained with a subset of the training corpus and evaluated on the remaining documents. This proced... |
SentiCite \subtitleAn Approach for Publication Sentiment Analysis | 1910.03498 | Table 10: Different nature of citation classes. | ['[BOLD] Label', '[BOLD] SC-SVM', '[BOLD] SC-Paum', '[BOLD] #refs'] | [['Usage', '0.5294', '[BOLD] 0.8325', '17'], ['Reference', '[BOLD] 1.0', '0.9455', '110'], ['Reading', '0.7143', '[BOLD] 0.8571', '7'], ['Rest', '0.4533', '[BOLD] 0.4602', '112'], ['Dataset', '[BOLD] 0.667', '[BOLD] 0.667', '15'], ['Overall', '0.7075', '[BOLD] 0.7099', '261']] | As the numbers indicate, the classification of the different nature of references is not equally hard for the classifiers. The important labels have a good performance with scores around 0.667 for the datasets and up to 1.0 for the references. |
Detecting Mismatch between Text Script and Voice-over Using Utterance Verification Based on Phoneme Recognition Ranking | 2003.09180 | Table 5: Performance improvement of the two-stage APR-based UV. | ['Test Set', 'APR', 'APR2-stage', 'Δ'] | [['BNS-1', '0.9675', '0.9677', '+0.0002'], ['BNS-2', '0.9592', '0.9598', '+0.0006']] | We finally investigate the effect of the proposed two-stage APR-based UV. Although the improvement is minimal, the two-stage APR-based UV compensates for a few errors of the pure APR-based UV. |
Detecting Mismatch between Text Script and Voice-over Using Utterance Verification Based on Phoneme Recognition Ranking | 2003.09180 | Table 1: Excerpted test sets from a speech database and an MMORPG | ['Test Set', 'Description', 'Correct', 'Incorrect'] | [['DICT01', 'Read-style', '1,600', '1,600'], ['BNS-1', 'Exaggerated-style', '1,600', '1,600'], ['BNS-2', '+ various tones & effects', '483', '483']] | The second type of test set is used to detect a mismatch between text script and voice-over. We create three test sets (DICT01, BNS-1, and BNS-2). DICT01 is the 1,600 read-style utterances excerpted from a Korean speech DB (DICT01) BNS-2 comprises 483 voice-overs with various tones and sound effects. To create the mism... |
Detecting Mismatch between Text Script and Voice-over Using Utterance Verification Based on Phoneme Recognition Ranking | 2003.09180 | Table 3: Comparison between the proposed APR-based UV and the conventional LRT-based UV with the optimized thresholds. | ['Test Set', 'LRT ACC', 'LRT [ITALIC] τ', 'APR ACC', 'APR [ITALIC] θ', 'APR Δ'] | [['DICT01', '0.992', '1.5', '0.998', '4.0', '+0.006 (0.6%)'], ['BNS-1', '0.930', '1.2', '0.968', '5.0', '+0.038 (4.1%)'], ['BNS-2', '0.901', '1.1', '0.959', '6.0', '+0.058 (6.4%)']] | In the test sets of text scripts and voice-overs, we perform an experiment for comparing the performances of the proposed APR-based UV and the conventional LRT-based UV. APR is an alternative to the log-likelihood ratio (LLR) of the LRT-based UV. For the evaluation, we apply the optimized thresholds (i.e., τ and θ) tha... |
Detecting Mismatch between Text Script and Voice-over Using Utterance Verification Based on Phoneme Recognition Ranking | 2003.09180 | Table 4: Performance degradation in the exaggerated voice-overs, when applying the optimized thresholds of the read-speech utterances. | ['Test Set', 'LRT ACC', 'LRT Δ', 'APR ACC', 'APR Δ'] | [['BNS-1', '0.813', '-0.117', '0.952', '-0.016'], ['BNS-1', '0.813', '(-14.4%)', '0.952', '(-1.7%)'], ['BNS-2', '0.674', '-0.228', '0.900', '-0.059'], ['BNS-2', '0.674', '(-33.8%)', '0.900', '(-6.6%)']] | (-14.4%) and -0.228 (-33.8%), respectively. However, those in the case of the APR-based UV are -0.016 (-1.7%) and -0.059 (-6.6%), respectively, which are remarkably lower than that of the LRT-based UV. |
From News to Medical: Cross-domain Discourse Segmentation | 1904.06682 | Table 5: Average inter-annotator agreement per section, ordered from highest to lowest, the corresponding average F1 of the neural segmenter, and number of tokens (there are 2 documents per section, except 1 for Summary). | ['Section', 'Kappa', 'F1', '#tokens'] | [['Summary', '1.00', '100', '35'], ['Introduction', '0.96', '86.58', '258'], ['Results', '0.93', '91.74', '354'], ['Abstract', '0.89', '95.08', '266'], ['Methods', '0.86', '92.99', '417'], ['Discussion', '0.84', '89.03', '365']] | Here we compare the level of annotator agreement with the performance of the neural segmenter. However, high agreement does not always translate to good performance. The Introduction section is straightforward for the annotators to segment, but this is also where most citations occur, causing the segmenter to perform m... |
From News to Medical: Cross-domain Discourse Segmentation | 1904.06682 | Table 3: F1, precision (P) and recall (R) of RST discourse segmenters on two domains (best numbers for News are underlined, for Medical are bolded). | ['RST Seg', 'Domain', 'F1', 'P', 'R'] | [['DPLP', '[ITALIC] News', '82.56', '81.75', '83.37'], ['DPLP', '[ITALIC] Medical', '75.29', '78.69', '72.18'], ['two-pass', '[ITALIC] News', '95.72', '97.19', '94.29'], ['two-pass', '[ITALIC] Medical', '84.69', '86.23', '83.21'], ['Neural', '[ITALIC] News', '97.32', '95.68', '99.01'], ['Neural', '[ITALIC] Medical', '[... | As expected, the News domain outperforms the Medical domain, regardless of which segmenter is used. In the case of the DPLP segmenter, the gap between the two domains is about 7.4 F1 points. Note that the performance of DPLP on News lags considerably behind the state of the art (-14.76 F1 points). When switching to the... |
Domain-Specific Sentiment Word Extraction by Seed Expansion and Pattern Generation | 1309.6722 | (b) Negative PageRank | ['postag', 'P@50', 'P@100', 'P@500', 'P@1000'] | [['[BOLD] i', '[BOLD] 0.980', '[BOLD] 0.960', '[BOLD] 0.808', '[BOLD] 0.649'], ['a', '0.260', '0.200', '0.240', '0.231'], ['v', '0.020', '0.040', '0.032', '0.048']] | In negative pagerank result, idioms gets the best result. After checking the final ranking result, we find that idioms have more synonymies with other idioms and they have higher probability to act as sentiment word. In addition, the performance in positive PageRank is poor. |
Domain-Specific Sentiment Word Extraction by Seed Expansion and Pattern Generation | 1309.6722 | (a) Positive PageRank | ['postag', 'P@50', 'P@100', 'P@500', 'P@1000'] | [['i', '0.000', '0.000', '0.016', '0.018'], ['[BOLD] a', '[BOLD] 0.240', '[BOLD] 0.280', '[BOLD] 0.370', '[BOLD] 0.385'], ['v', '0.020', '0.010', '0.028', '0.044']] | In negative pagerank result, idioms gets the best result. After checking the final ranking result, we find that idioms have more synonymies with other idioms and they have higher probability to act as sentiment word. In addition, the performance in positive PageRank is poor. |
Domain-Specific Sentiment Word Extraction by Seed Expansion and Pattern Generation | 1309.6722 | Table 6: Experimental results on DSSW extraction | ['finance', 'P', 'Hu04 0.5423', 'Qiu11 0.5404', 'Our 0.6347'] | [['finance', 'R', '0.2956', '0.3118', '0.3411'], ['finance', 'F1', '0.3826', '0.3955', '[BOLD] 0.4437'], ['entertainment', 'P', '0.5626', '0.5878', '0.6449'], ['entertainment', 'R', '0.2769', '0.3022', '0.3256'], ['entertainment', 'F1', '0.3711', '0.3992', '[BOLD] 0.4328'], ['digital', 'P', '0.5534', '0.5649', '0.5923'... | Our precision(P) improves significantly, especially in finance domain with 9.4% improvement. Our recall(R) improves slightly because there are still some sentiment words don’t co-occur with target words. Problem with hidden target words will be studied in future work. |
The Microsoft 2017 Conversational Speech Recognition System | 1708.06073 | Table 2: Acoustic model performance by senone set, model architecture, and for various frame-level combinations, using an N-gram LM. The “puhpum” senone sets use an alternate dictionary with special phones for filled pauses. | ['Senone set', 'Architecture', 'devset WER', 'test WER'] | [['[BOLD] 9k', 'BLSTM', '11.5', '8.3'], ['[EMPTY]', 'ResNet', '10.0', '8.2'], ['[EMPTY]', 'LACE', '11.2', '8.1'], ['[EMPTY]', 'CNN-BLSTM', '11.3', '8.4'], ['[EMPTY]', 'BLSTM+ResNet+LACE', '9.8', '7.2'], ['[EMPTY]', 'BLSTM+ResNet+LACE+CNN-BLSTM', '9.6', '7.2'], ['[BOLD] 9k puhpum', 'BLSTM', '11.3', '8.1'], ['[EMPTY]', '... | The results are based on N-gram language models, and all combinations are equal-weighted. In the past we had used a relatively small vocabulary of 30,500 words drawn only from in-domain (Switchboard and Fisher corpus) training data. While this yields an out-of-vocabulary (OOV) rate well below 1%, our error rates have r... |
The Microsoft 2017 Conversational Speech Recognition System | 1708.06073 | Table 4: Perplexities and word errors with session-based LSTM-LMs (forward direction only). The last line reflects the use of 1-best recognition output for words in preceding utterances. | ['Model inputs', 'PPL devset', 'PPL test', 'WER devset', 'WER test'] | [['Utterance words, letter-3grams', '50.76', '44.55', '9.5', '6.8'], ['+ session history words', '39.69', '36.95', '[EMPTY]', '[EMPTY]'], ['+ speaker change', '38.20', '35.48', '[EMPTY]', '[EMPTY]'], ['+ speaker overlap', '37.86', '35.02', '[EMPTY]', '[EMPTY]'], ['(with 1-best history)', '40.60', '37.90', '9.3', '6.7']... | There is a large perplexity reduction of 21% by conditioning on the previous word context, with smaller incremental reductions from adding speaker change and overlap information. The table also compares the word error rate with the full session-based model to the baseline, within-utterance LSTM-LM. As shown in the last... |
The Microsoft 2017 Conversational Speech Recognition System | 1708.06073 | Table 5: Results for LSTM-LM rescoring on systems selected for combination, the combined system, and confusion network rescoring | ['Senone set', 'Model/combination step', 'WER devset', 'WER test', 'WER devset', 'WER test'] | [['[EMPTY]', '[EMPTY]', 'ngram-LM', 'ngram-LM', 'LSTM-LMs', 'LSTM-LMs'], ['9k', 'BLSTM', '11.5', '8.3', '9.2', '6.3'], ['27k', 'BLSTM', '11.4', '8.0', '9.3', '6.3'], ['27k-puhpum', 'BLSTM', '11.3', '8.0', '9.2', '6.3'], ['9k', 'BLSTM+ResNet+LACE+CNN-BLSTM', '9.6', '7.2', '7.7', '5.4'], ['9k-puhpum', 'BLSTM+ResNet+LACE'... | The collection of LSTM-LMs (which includes the session-based LMs) gives a very consistent 22 to 25% relative error reduction on individual systems, compared to the N-gram LM. The system combination reduces error by 4% relative over the best individual systems, and the CN rescoring improves another 2-3% relative. |
A Nested Attention Neural Hybrid Model for Grammatical Error Correction | 1707.02026 | Table 5: F0.5 results on the CoNLL-13 set of main model architectures, on different segments of the set according to whether the input contains OOVs. | ['[BOLD] Model', '[BOLD] NonOOV', '[BOLD] OOV', '[BOLD] Overall'] | [['Word NMT + UNK replacement', '27.61', '21.57', '26.17'], ['Hybrid model', '[BOLD] 29.36', '25.92', '28.49'], ['Nested Attention Hybrid Model', '29.00', '[BOLD] 27.39', '[BOLD] 28.61']] | We present a comparative performance analysis of models on the CoNLL-13 development set. First, we divide the set into two segments: OOV and NonOOV, based on whether there is at least one OOV word in the given source input. The additional nested character-level attention of our hybrid model brings a sizable improvement... |
A Nested Attention Neural Hybrid Model for Grammatical Error Correction | 1707.02026 | Table 1: Overview of the datasets used. | ['[EMPTY]', 'Training', 'Validation', 'Development', 'Test'] | [['#Sent pairs', '2,608,679', '4,771', '1,381', '1,312']] | We evaluate the performance of the models on the standard sets from the CoNLL-14 shared task Ng et al. We report final performance on the CoNLL-14 test set without alternatives, and analyze model performance on the CoNLL-13 development set Dahlmeier et al. We use the development and validation sets for model selection.... |
A Nested Attention Neural Hybrid Model for Grammatical Error Correction | 1707.02026 | Table 3: F0.5 results on the CoNLL-13 and CoNLL-14 test sets of main model architectures. | ['[BOLD] Model', '[BOLD] Performance Dev', '[BOLD] Performance Test'] | [['Word NMT + UNK replacement', '26.17', '38.77'], ['Hybrid model', '28.49', '40.44'], ['Nested Attention Hybrid Model', '[BOLD] 28.61', '[BOLD] 41.53']] | In addition to the word-level baseline, we include the performance of a hybrid model with a single level of attention, which follows the work of \newciteluong-manning:2016:P16-1 for machine translation, and is the first application of a hybrid word/character-level model to grammatical error correction. Based on hyper-p... |
A Nested Attention Neural Hybrid Model for Grammatical Error Correction | 1707.02026 | Table 7: Precision, Recall and F0.5 results on CoNLL-13,on the ”small changes” and “large changes” portions of the OOV segment. | ['[BOLD] Model', '[BOLD] Performance P', '[BOLD] Performance R', '[BOLD] Performance [ITALIC] F0.5'] | [['[BOLD] Small Changes Portion', '[BOLD] Small Changes Portion', '[BOLD] Small Changes Portion', '[BOLD] Small Changes Portion'], ['Hybrid model', '43.86', '16.29', '32.77'], ['Nested Attention Hybrid Model', '48.25', '17.92', '36.04'], ['[BOLD] Large Changes Portion', '[BOLD] Large Changes Portion', '[BOLD] Large Cha... | Our hypothesis is that the additional character-level attention layer is particularly useful to model edits among orthographically similar words. We can see that the gains in the “small changes” portion are indeed quite large, indicating that the fine-grained character-level attention empowers the model to more accurat... |
Lattice Rescoring Strategies for Long Short Term Memory Language Models in Speech Recognition | 1711.05448 | Table 3: WER of LSTMLM lattice rescoring algorithms | ['Algorithm', 'WER (%)', '[EMPTY]'] | [['[EMPTY]', 'LM used in', 'LM used in'], ['[EMPTY]', 'Lattice generation', 'Lattice generation'], ['[EMPTY]', '2-gram', '5-gram'], ['[ITALIC] Push-forward (Algorithm\xa0 1 )', '[ITALIC] Push-forward (Algorithm\xa0 1 )', '[EMPTY]'], ['[ITALIC] k=1', '12.8', '12.4'], ['[ITALIC] k=10', '12.7', '12.4'], ['[ITALIC] k=50', ... | While the WER reduced by 2% relative for the 2-gram lattices, it showed no variation for the 5-gram lattices. The push-forward algorithm with k=1 results in a more severe approximation for the 2-gram lattices. Hence, increasing the LSTM hypotheses per lattice node (k) results in a better WER. We next expanded the latti... |
Contextual Phonetic Pretraining for End-to-endUtterance-level Language and Speaker Recognition | 1907.00457 | Table 2: Fisher speaker recognition task results | ['[BOLD] System', '[BOLD] EER', '[BOLD] minDCF08', '[BOLD] minDCF10'] | [['i-vector ', '2.10', '0.093', '0.3347'], ['x-vector + stat.\xa0pooling ', '1.73', '0.086', '0.3627'], ['phn.\xa0vec.\xa0+ finetune ', '1.60', '0.076', '0.3413'], ['+ multi-tasking ', '1.39', '0.073', '0.3087'], ['x-vector + SAP', '1.50', '0.074', '0.2973'], ['pretrain + CNN + SAP', '[BOLD] 1.07', '[BOLD] 0.052', '[BO... | The results of i-vector and x-vector from the original work are first presented. To ablate the merit of the SAP layer, we train a similar x-vector architecture with self-attentive instead of regular statistics pooling to get a 13.2% relative EER reduction. Training on contextual frame representations induced from the A... |
A Comparison of Modeling Units in Sequence-to-Sequence Speech Recognition with the Transformer on Mandarin Chinese | 1805.06239 | Table 6: CER (%) on HKUST datasets compared to previous works. | ['model', 'CER'] | [['LSTMP-9×800P512-F444 ', '30.79'], ['CTC-attention+joint dec. (speed perturb., one-pass) +VGG net +RNN-LM (separate) ', '28.9 [BOLD] 28.0'], ['CI-phonemes-D1024-H16 ', '30.65'], ['Syllables-D1024-H16 (speed perturb) ', '28.77'], ['Words-D1024-H16 (speed perturb)', '27.42'], ['Sub-words-D1024-H16 (speed perturb)', '2... | We can observe that the best CER 26.64% of character based model with the Transformer on HKUST datasets achieves a 13.4% relative reduction compared to the best CER of 30.79% by the deep multidimensional residual learning with 9 LSTM layers. It shows the superiority of the sequence-to-sequence attention-based model com... |
Global Thread-Level Inference for Comment Classificationin Community Question Answering | 1911.08755 | Table 2: Same-vs-Different classification. P, R, and F1 are calculated with respect to Same. | ['Classifier', 'P', 'R', 'F1', 'Acc'] | [['baseline: [ITALIC] Same', '[EMPTY]', '[EMPTY]', '[EMPTY]', '69.26'], ['MaxEnt-2C', '73.95', '90.99', '81.59', '71.56'], ['MaxEnt-3C', '77.15', '80.42', '78.75', '69.94']] | We can see that the two-class MaxEnt-2C classifier works better than the three-class MaxEnt-3C. MaxEnt-3C has more balanced P and R, but loses in both F1 and accuracy. MaxEnt-2C is very skewed towards the majority class, but performs better due to the class imbalance. Overall, it seems very difficult to learn with the ... |
Global Thread-Level Inference for Comment Classificationin Community Question Answering | 1911.08755 | Table 3: Good-vs-Bad classification. ‡ and † mark statistically significant differences in accuracy compared to the baseline MaxEnt classifier with confidence levels of 99% and 95%, respectively (randomized test). | ['System', 'P', 'R', 'F1', 'Acc'] | [['[BOLD] Top-3 at SemEval-2015 Task 3', '[BOLD] Top-3 at SemEval-2015 Task 3', '[BOLD] Top-3 at SemEval-2015 Task 3', '[EMPTY]', '[EMPTY]'], ['JAIST', '80.23', '77.73', '78.96', '79.10'], ['HITSZ-ICRC', '75.91', '77.13', '76.52', '76.11'], ['QCRI', '74.33', '83.05', '78.45', '76.97'], ['[BOLD] Instance Classifiers', '... | On the top are the best systems at SemEval-2015 Task 3. We can see that our MaxEnt classifier is competitive: it shows higher accuracy than two of them, and the highest F1 overall. |
Analyzing the Language of Food on Social Media | 1409.2195 | TABLE VIII: Effects of varying the fraction of tweets used for training and testing on classification accuracy in the state-prediction task, using All Words and LDA topics. | ['training fraction', 'testing fraction 0.2', 'testing fraction 0.4', 'testing fraction 0.6', 'testing fraction 0.8', 'testing fraction 1.0'] | [['0.2', '11.76', '11.76', '5.88', '9.80', '15.68'], ['0.4', '19.60', '17.64', '17.64', '17.64', '25.49'], ['0.6', '25.49', '29.41', '35.29', '41.17', '47.05'], ['0.8', '39.21', '41.17', '43.13', '50.98', '52.94'], ['1.0', '43.13', '58.82', '54.90', '62.74', '64.70']] | Performance varies from 11.76% when using 20% of tweets in the training set and 20% in the testing set, to 64.7% when using all available tweets. Increasing the number of tweets in the training set has a larger positive effect on accuracy than increasing the number of tweets in the testing set. As in the city predictio... |
Analyzing the Language of Food on Social Media | 1409.2195 | TABLE V: Effects of varying the fraction of tweets used for training and testing on classification accuracy in the city-prediction task, using All Words and LDA topics. | ['training fraction', 'testing fraction 0.2', 'testing fraction 0.4', 'testing fraction 0.6', 'testing fraction 0.8', 'testing fraction 1.0'] | [['0.2', '6.66', '6.66', '6.66', '6.66', '6.66'], ['0.4', '13.33', '13.33', '13.33', '13.33', '20.00'], ['0.6', '20.00', '26.66', '26.66', '26.66', '40.00'], ['0.8', '33.33', '46.66', '33.33', '53.33', '53.33'], ['1.0', '46.66', '53.33', '60.00', '66.66', '80.00']] | Indeed, when only 20% of the training set is used, the models achieve the same score as the baseline classifier (6.67%). Performance continues to increase as we add more data, suggesting that we have not reached a performance ceiling yet. |
Construction of the Literature Graph in Semantic Scholar | 1805.02262 | Table 2: Document-level evaluation of three approaches in two scientific areas: computer science (CS) and biomedical (Bio). | ['Approach', 'CS prec.', 'CS yield', 'Bio prec.', 'Bio yield'] | [['Statistical', '98.4', '712', '94.4', '928'], ['Hybrid', '91.5', '1990', '92.1', '3126'], ['Off-the-shelf', '97.4', '873', '77.5', '1206']] | In both domains, the statistical approach gives the highest precision and the lowest yield. The hybrid approach consistently gives the highest yield, but sacrifices precision. The TagMe off-the-shelf library used for the CS domain gives surprisingly good results, with precision within 1 point from the statistical model... |
Construction of the Literature Graph in Semantic Scholar | 1805.02262 | Table 3: Results of the entity extraction model on the development set of SemEval-2017 task 10. | ['Description', 'F1'] | [['Without LM', '49.9'], ['With LM', '54.1'], ['Avg. of 15 models with LM', '55.2']] | We use the standard data splits of the SemEval-2017 Task 10 on entity (and relation) extraction from scientific papers Augenstein et al. The first line omits the LM embeddings lmk, while the second line is the full model (including LM embeddings) showing a large improvement of 4.2 F1 points. The third line shows that c... |
Construction of the Literature Graph in Semantic Scholar | 1805.02262 | Table 4: The Bag of Concepts F1 score of the baseline and neural model on the two curated datasets. | ['[EMPTY]', 'CS', 'Bio'] | [['Baseline', '84.2', '54.2'], ['Neural', '84.6', '85.8']] | Candidate selection. In a preprocessing step, we build an index which maps any token used in a labeled mention or an entity name in the KB to associated entity IDs, along with the frequency this token is associated with that entity. This is similar to the index used in previous entity linking systems At train and test ... |
A Discriminative Neural Model for Cross-Lingual Word Alignment | 1909.00444 | Table 2: Precision, Recall, and F1 on Chinese GALE test data. BPE indicates “with BPE”, and NER denotes restriction to NER spans. | ['Method', 'P', 'R', 'F1'] | [['Avg. attention.', '36.30', '46.17', '40.65'], ['Avg. attention (BPE)', '37.89', '49.82', '43.05'], ['Avg. attention (NER)', '16.57', '35.85', '22.66'], ['FastAlign', '80.46', '50.46', '62.02'], ['FastAlign\xa0(BPE)', '70.41', '55.43', '62.03'], ['FastAlign (NER)', '83.70', '49.54', '62.24'], ['DiscAlign', '72.92', '... | Our model outperforms both baselines for all languages and experimental conditions. By increasing the threshold α above which p(ai,j|s,t) is considered an alignment we can obtain high-precision alignments, exceeding FastAlign’s precision and recall. The best threshold values on the development set were α=0.13 and α=0.1... |
A Discriminative Neural Model for Cross-Lingual Word Alignment | 1909.00444 | Table 3: Precision, Recall, and F1 on Arabic GALE test data. | ['Method', 'P', 'R', 'F1'] | [['Avg. attention.', '8.46', '32.50', '13.42'], ['Avg. attention (BPE)', '10.11', '17.27', '12.75'], ['FastAlign', '62.26', '51.06', '56.11'], ['FastAlign\xa0(BPE)', '62.74', '51.25', '56.42'], ['DiscAlign', '91.30', '75.66', '[BOLD] 82.74'], ['DiscAlign (BPE)', '87.05', '76.98', '[BOLD] 81.71']] | Furthermore, we see that average attention performs abysmally in Arabic. The best thresholds for Arabic differed substantially from those for Chinese: for average attention (with and without BPE) the values were α=0.05 and α=0.1, while for the discriminative aligner they were α=0.94 and α=0.99 (compared to 0.15 for Chi... |
A Discriminative Neural Model for Cross-Lingual Word Alignment | 1909.00444 | Table 4: F1 results on OntoNotes test for systems trained on data projected via FastAlign and DiscAlign. | ['Method', '# train', 'P', 'R', 'F1'] | [['Zh Gold', '36K', '75.46', '80.55', '77.81'], ['FastAlign', '36K', '38.99', '36.61', '37.55'], ['FastAlign', '53K', '39.46', '36.65', '37.77'], ['DiscAlign', '36K', '51.94', '52.37', '51.76'], ['DiscAlign', '53K', '51.92', '51.93', '51.57']] | Based on our findings that: [topsep=0.0pt,itemsep=-1ex,partopsep=1ex,parsep=1ex] discriminative alignments outperform common unsupervised baselines in two typologically divergent languages this performance boost leads to major downstream improvements on NER only a small amount of labelled data is needed to realize thes... |
A Discriminative Neural Model for Cross-Lingual Word Alignment | 1909.00444 | Table 7: Sentences per minute and average scores against gold-labelled data for sentences annotated for alignment by human annotators (Hu.), compared to DiscAlign (DA) on the same sentences. | ['Model', 'sents/m', 'P', 'R', 'F1'] | [['Human', '4.4', '90.09', '62.85', '73.92'], ['DiscAlign', '-', '74.54', '72.15', '73.31'], ['Hu. (NER)', '-', '87.73', '71.02', '78.24'], ['DA (NER)', '-', '77.37', '67.69', '71.94']] | The annotators achieve high overall precision for alignment, but fall short on recall. When only alignments of NER spans are considered, their F1 score improves considerably. Additionally, human annotators outperform DiscAlign when evaluated on the same sentences. |
Structure-Infused Copy Mechanisms for Abstractive Summarization | 1806.05658 | Table 11: Effects of applying the coverage regularizer and the reference beam search to structural models, evaluated on test-1951. Combining both yields the highest scores. | ['| [ITALIC] V|', '[BOLD] R-2', '[BOLD] Train Speed', '[BOLD] InVcb', '[BOLD] InVcb+Src'] | [['1K', '13.99', '2.5h/epoch', '60.57', '76.04'], ['2K', '15.35', '2.7h/epoch', '69.71', '80.72'], ['5K', '17.25', '3.2h/epoch', '79.98', '86.51'], ['10K', '17.62', '3.8h/epoch', '88.26', '92.18']] | Coverage and reference beam. The coverage regularizer is applied in a second training stage, where the system is trained for an extra 5 epochs with coverage and the model yielding the lowest validation loss is selected. Both coverage and ref_beam can improve the system performance. Our observation suggests that ref_bea... |
Structure-Infused Copy Mechanisms for Abstractive Summarization | 1806.05658 | Table 9: Informativeness, fluency, and faithfulness scores of summaries. They are rated by Amazon turkers on a Likert scale of 1 (worst) to 5 (best). We choose to evaluate Struct+2Way+Relation (as oppose to 2Way+Word) because it focuses on preserving source relations in the summaries. | ['[BOLD] System', '[BOLD] Info.', '[BOLD] Fluency', '[BOLD] Faithful.'] | [['Struct+Input', '2.9', '3.3', '3.0'], ['Struct+2Way+Relation', '3.0', '3.4', '3.1'], ['Ground-truth Summ.', '3.2', '3.5', '3.1']] | Linguistic quality. fluency (is the summary grammatical and well-formed?), informativeness (to what extent is the meaning of the original sentence preserved in the summary?), and faithfulness (is the summary accurate and faithful to the original?). We found that “Struct+2Way+Relation” outperforms “Struct+Input” on all ... |
Structure-Infused Copy Mechanisms for Abstractive Summarization | 1806.05658 | Table 10: Percentages of source dependency relations (of various types) preserved in the system summaries. | ['[BOLD] System', '[BOLD] nsubj', '[BOLD] dobj', '[BOLD] amod', '[BOLD] nmod', '[BOLD] nmod:poss', '[BOLD] mark', '[BOLD] case', '[BOLD] conj', '[BOLD] cc', '[BOLD] det'] | [['Baseline', '7.23', '12.07', '20.45', '8.73', '12.46', '15.83', '14.84', '9.72', '5.03', '2.22'], ['Struct+Input', '7.03', '11.72', '19.72', '[BOLD] 9.17↑', '12.46', '15.35', '14.69', '9.55', '4.67', '1.97'], ['Struct+Hidden', '[BOLD] 7.78↑', '[BOLD] 12.34↑', '[BOLD] 21.11↑', '[BOLD] 9.18↑', '[BOLD] 14.86↑', '14.93',... | Dependency relations. A source relation is considered preserved if both its words appear in the summary. We observe that the models implementing structure-infused copy mechanisms (e.g., “Struct+2Way+Word”) are more likely to preserve important dependency relations in the summaries, including nsubj, dobj, amod, nmod, an... |
When to Talk: Chatbot Controls the Timing of Talking during Multi-turn Open-domain Dialogue Generation | 1912.09879 | Table 4: Automatic evaluation results of all the models on Ubuntu and modified dailydialog test dataset (The table above is the results of ubuntu dataset, and the below is dailydialog). Baseline HRED cannot control the timing of talking and the accuracy and macro-F1 of it is empty. All the best results are shown in bol... | ['[BOLD] Models', '[BOLD] PPL', '[BOLD] BLEU1', '[BOLD] BLEU2', '[BOLD] BLEU3', '[BOLD] BLEU4', '[BOLD] BERTScore', '[BOLD] Dist-1', '[BOLD] Dist-2', '[BOLD] Acc', '[BOLD] Macro-F1'] | [['[BOLD] HRED', '63.06', '0.0774', '0.0486', '0.0427', '0.0399', '0.8308', '0.0427', '0.1805', '-', '-'], ['[BOLD] HRED-CF', '[BOLD] 60.28', '[BOLD] 0.0877', '[BOLD] 0.0605', '[BOLD] 0.0547', '[BOLD] 0.0521', '0.8343', '0.0327', '0.1445', '0.6720', '0.6713'], ['[BOLD] W2T-GCN', '62.18', '0.0844', '0.0579', '0.0521', '... | For all the models, we adopt the early stopping to avoid the overfitting. Generation Quality: The strong baseline HRED-CF which explicity models the timing of talking is much better than HRED on almost all the metrics such as PPL and BLEU. Especially in terms of BLEU, HRED-CF exceeds HRED by up to 1.5% on Embedding Ave... |
When to Talk: Chatbot Controls the Timing of Talking during Multi-turn Open-domain Dialogue Generation | 1912.09879 | Table 4: Automatic evaluation results of all the models on Ubuntu and modified dailydialog test dataset (The table above is the results of ubuntu dataset, and the below is dailydialog). Baseline HRED cannot control the timing of talking and the accuracy and macro-F1 of it is empty. All the best results are shown in bol... | ['[BOLD] Models', '[BOLD] PPL', '[BOLD] BLEU1', '[BOLD] BLEU2', '[BOLD] BLEU3', '[BOLD] BLEU4', '[BOLD] BERTScore', '[BOLD] Dist-1', '[BOLD] Dist-2', '[BOLD] Acc', '[BOLD] Macro-F1'] | [['[BOLD] HRED', '21.91', '0.1922', '0.1375', '0.1266', '0.1235', '[BOLD] 0.8735', '0.0573', '0.2236', '-', '-'], ['[BOLD] HRED-CF', '[BOLD] 20.01', '[BOLD] 0.1950', '0.1397', '[BOLD] 0.1300', '[BOLD] 0.1270', '0.8718', '0.0508', '0.2165', '0.8321', '0.8162'], ['[BOLD] W2T-GCN', '23.21', '0.1807', '0.1262', '0.1179', '... | For all the models, we adopt the early stopping to avoid the overfitting. Generation Quality: The strong baseline HRED-CF which explicity models the timing of talking is much better than HRED on almost all the metrics such as PPL and BLEU. Especially in terms of BLEU, HRED-CF exceeds HRED by up to 1.5% on Embedding Ave... |
Medical Exam Question Answering with Large-scale Reading Comprehension | 1802.10279 | Table 3: Results (accuracy) of SeaReader and other approaches on MedQA task | ['[EMPTY]', 'Valid set', 'Test set'] | [['Iterative Attention', '60.7', '59.3'], ['Neural Reasoner', '54.8', '53.0'], ['R-NET', '65.2', '64.5'], ['SeaReader', '[BOLD] 73.9', '[BOLD] 73.6'], ['SeaReader (ensemble)', '[BOLD] 75.8', '[BOLD] 75.3'], ['Human passing score', '60.0 (360/600)', '60.0 (360/600)']] | Quantitative Results We evaluated model performance by accuracy of choosing the best candidate. Our SeaReader clearly outperforms baseline models by a large margin. We also include the human passing score as a reference. As MedQA is not a commonsense question answering task, human performance relies heavily on individu... |
Medical Exam Question Answering with Large-scale Reading Comprehension | 1802.10279 | Table 4: Test performance with different number of documents given per candidate answer | ['Number of documents', 'top-1', 'top-5', 'top-10', 'top-20'] | [['SeaReader accuracy', '57.8', '71.7', '73.6', '74.4'], ['Relevant document ratio', '0.90', '0.54', '0.46', '0.29']] | To discover the general relevancy of documents returned by our retrieval system, we hand-labeled the relevancy of 1000 retrieved documents for 100 problems. We notice that there is still performance gain using as many as 20 documents per candidate answer, while the ratio of relevant documents is already low. This illus... |
Bridging the Gap for Tokenizer-Free Language Models | 1908.10322 | Table 2: Comparing recent language model results on lm1b. | ['[EMPTY]', 'Segmentation', 'Context Length', '# of params', 'Perplexity', 'Bits/Byte'] | [['shazeer2017outrageously', 'Word', 'Fixed', '6.0B', '28.0', '0.929'], ['shazeer2018mesh', 'Word-Piece', 'Fixed', '4.9B', '24.0', '0.886'], ['baevski2018adaptive', 'Word', 'Fixed', '1.0B', '23.0', '0.874'], ['transformerxl', 'Word', 'Arbitrary', '0.8B', '21.8', '0.859'], ['bytelmaaai', 'Byte', 'Fixed', '0.2B', '40.6',... | We observe that tokenizer-free LM performance improves significantly (40.6 to 23.0) when the model capacity is increased from 0.2B to 0.8B parameters. With sufficient capacity our byte-level LM is competitive with word based models (ranging from 21.8 to 28.0). Note, our model is able to achieve comparable performance w... |
Classical Structured Prediction Losses for Sequence to Sequence Learning | 1711.04956 | Table 5: Comparison to Beam Search Optimization. We report the best likelihood (MLE) and BSO results from Wiseman and Rush (2016), as well as results from our MLE reimplementation and training with Risk. Results based on unnormalized beam search (k=5). | ['[EMPTY]', '[BOLD] BLEU', 'Δ'] | [['MLE', '24.03', '[EMPTY]'], ['+ BSO', '26.36', '+2.33'], ['MLE Reimplementation', '23.93', '[EMPTY]'], ['+ Risk', '26.68', '+2.75']] | Risk significantly improves BLEU compared to our baseline at +2.75 BLEU, which is slightly better than the +2.33 BLEU improvement reported for Beam Search Optimization (cf. This shows that classical objectives for structured prediction are still very competitive. |
Classical Structured Prediction Losses for Sequence to Sequence Learning | 1711.04956 | Table 1: Test accuracy in terms of BLEU on IWSLT’14 German-English translation with various loss functions cf. Figure 1. W & R (2016) refers to Wiseman and Rush (2016), B (2016) to Bahdanau et al. (2016), [S] indicates sequence level-training and [T] token-level training. We report averages and standard deviations over... | ['[EMPTY]', '[BOLD] test', '[BOLD] std'] | [['MLE (W & R, 2016) [T]', '24.03', '[EMPTY]'], ['BSO (W & R, 2016) [S]', '26.36', '[EMPTY]'], ['Actor-critic (B, 2016) [S]', '28.53', '[EMPTY]'], ['Huang et\xa0al. ( 2017 ) [T]', '28.96', '[EMPTY]'], ['Huang et\xa0al. ( 2017 ) (+LM) [T]', '29.16', '[EMPTY]'], ['TokNLL\xa0[T]', '31.78', '0.07'], ['TokLS\xa0[T]', '32.23... | Our baseline token-level results are several points above other figures in the literature and we further improve these results by up to 0.61 BLEU with Risk training. |
Classical Structured Prediction Losses for Sequence to Sequence Learning | 1711.04956 | Table 2: Validation and test BLEU for loss combination strategies. We either use token-level TokLS and sequence-level Riskindividually or combine them as a weighted combination, a constrained combination, a random choice for each sample, cf. §3.3. | ['[EMPTY]', '[BOLD] valid', '[BOLD] test'] | [['TokLS', '33.11', '32.21'], ['Risk\xa0only', '33.55', '32.45'], ['Weighted', '33.91', '32.85'], ['Constrained', '33.77', '32.79'], ['Random', '33.70', '32.61']] | Next, we compare various strategies to combine sequence-level and token-level objectives (cf. For these experiments we use 5 candidate sequences per training example for faster experimental turnaround. We consider Risk as sequence-level loss and label smoothing as token-level loss. We also compare to randomly choosing ... |
Classical Structured Prediction Losses for Sequence to Sequence Learning | 1711.04956 | Table 3: Effect of initializing sequence-level training (Risk) with parameters from token-level likelihood (TokNLL) or label smoothing (TokLS). | ['[EMPTY]', '[BOLD] valid', '[BOLD] test'] | [['TokNLL', '32.96', '31.74'], ['Risk\xa0init with TokNLL', '33.27', '32.07'], ['Δ', '+0.31', '+0.33'], ['TokLS', '33.11', '32.21'], ['Risk\xa0init with TokLS', '33.91', '32.85'], ['Δ', '+0.8', '+0.64']] | So far we initialized sequence-level models with parameters from a token-level model trained with label smoothing. The improvement of initializing with TokNLL is only 0.3 BLEU with respect to the TokNLL baseline, whereas, the improvement from initializing with TokLS is 0.6-0.8 BLEU. We believe that the regularization p... |
Classical Structured Prediction Losses for Sequence to Sequence Learning | 1711.04956 | Table 4: Generating candidates online or offline. | ['[EMPTY]', '[BOLD] valid', '[BOLD] test'] | [['Online generation', '33.91', '32.85'], ['Offline generation', '33.52', '32.44']] | Next, we consider the question if refreshing the candidate subset at every training step (online) results in better accuracy compared to generating candidates before training and keeping the set static throughout training (offline). However the online setting is much slower, since regenerating the candidate set require... |
Classical Structured Prediction Losses for Sequence to Sequence Learning | 1711.04956 | Table 6: Accuracy on Gigaword abstractive summarization in terms of F-measure Rouge-1 (RG-1), Rouge-2 (RG-2), and Rouge-L (RG-L) for token-level label smoothing, and Risk optimization of all three ROUGE F1 metrics. [T] indicates a token-level objective and [S] indicates a sequence level objectives. ABS+ refers to Rush ... | ['[EMPTY]', '[BOLD] RG-1', '[BOLD] RG-2', '[BOLD] RG-L'] | [['ABS+ [T]', '29.78', '11.89', '26.97'], ['RNN MLE [T]', '32.67', '15.23', '30.56'], ['RNN MRT [S]', '36.54', '16.59', '33.44'], ['WFE [T]', '36.30', '17.31', '33.88'], ['SEASS [T]', '36.15', '17.54', '33.63'], ['DRGD [T]', '36.27', '17.57', '33.62'], ['TokLS', '36.53', '18.10', '33.93'], ['+ Risk\xa0RG-1', '36.96', '... | We optimize all three ROUGE metrics separately and find that Risk can further improve our strong baseline. but accuracy was generally lower on the validation set: RG-1 (36.59 Risk only vs. 36.67 Weighted), RG-2 (17.34 vs. 18.05), and RG-L (33.66 vs. 33.98). |
Automatic Severity Classification of Coronary Artery Disease via Recurrent Capsule Network | 1807.06718 | TABLE IV: Class-wise Performance (in Terms of F{}_{1}-score) of Our RCN Model and Baseline Methods | ['Method', 'Input', 'r:modifier (e1,e2)', 'r:negative (e2,e1)', 'r:position (e1,e2)', 'r:percentage (e1,e2)', 'r:percentage (e2,e1)'] | [['CNN + MaxPooling\xa0[nguyen2015relation]', 'Word + Position', '96.33', '[BOLD] 99.54', '95.45', '80.60', '97.53'], ['CNN + MaxPooling\xa0[nguyen2015relation]', 'Word + Position + Entity Type*', '96.28', '[BOLD] 99.54', '[BOLD] 96.21', '80.00', '98.77'], ['BiLSTM + MaxPooling\xa0[zhang2015relation]', 'Word Only', '95... | Furthermore, we compare class-wise performance of our RCN model with baseline methods. Firstly, we clearly observe that our model achieves No.1 in three relations (i.e. r:modifier[e1,e2], r:negative[e2,e1] and r:percentage[e2,e1]) and No.2 in one relation (i.e. r:percentage[e1,e2]) among all the five relations. The F{}... |
Automatic Severity Classification of Coronary Artery Disease via Recurrent Capsule Network | 1807.06718 | TABLE III: Comparative Results of Our RCN Model and Baseline Methods | ['Method', 'Input', 'P', 'R', 'F{}_{1}'] | [['CNN + MaxPooling\xa0[nguyen2015relation]', 'Word + Position', '95.54', '96.40', '95.97'], ['CNN + MaxPooling\xa0[nguyen2015relation]', 'Word + Position + Entity Type*', '[BOLD] 96.53', '95.95', '96.24'], ['BiLSTM + MaxPooling\xa0[zhang2015relation]', 'Word Only', '94.45', '94.30', '94.87'], ['BiLSTM + MaxPooling\xa0... | First of all, we can observe that our model with entity type features outperforms these reference algorithms, with 95.59% in Precision, 97.45% in Recall and 96.51% in F{}_{1}-score. The improvements compared with the original baselines without entity type features are 1.5, 1.5, 1.05, 0.45 and 1.2 points in Recall, 0.54... |
Automatic Severity Classification of Coronary Artery Disease via Recurrent Capsule Network | 1807.06718 | TABLE V: Performance of Our Automatic Severity Classification Method | ['[EMPTY]', 'P', 'R', 'F{}_{1}'] | [['Mild Stenosis', '100.00', '98.62', '99.31'], ['Moderate Stenosis', '93.33', '93.33', '93.33'], ['Severe Stenosis', '75.00', '90.00', '81.82'], ['Overall Accuracy', '97.00', '97.00', '97.00']] | To evaluate the effectiveness of our proposed severity classification method, we randomly select 200 coronary arteriography texts for evaluation. First of all, our method obtains an overall Accuracy of 97.00%. Only six texts are classified into the wrong level. Secondly, most of the coronary arteriography texts (72.5%)... |
Automatic Severity Classification of Coronary Artery Disease via Recurrent Capsule Network | 1807.06718 | TABLE VI: Comparisons between Different Input Features with Different Routing Iterations | ['r', 'Word Only P', 'Word Only R', 'Word Only F{}_{1}', 'Word + Entity Type P', 'Word + Entity Type R', 'Word + Entity Type F{}_{1}'] | [['1', '94.53', '95.80', '95.16', '94.97', '96.25', '95.61'], ['2', '[BOLD] 95.14', '96.85', '[BOLD] 95.99', '94.74', '97.30', '96.01'], ['3', '94.69', '96.25', '95.46', '95.15', '97.15', '96.14'], ['4', '94.95', '95.80', '95.37', '[BOLD] 95.59', '[BOLD] 97.45', '[BOLD] 96.51'], ['5', '93.25', '[BOLD] 97.30', '95.23', ... | To study the effect of the input features and the routing iterations in our model, we experimentally compare the performance between different input features with different routing iterations. The Precision, Recall and F{}_{1}-score are 95.59%, 97.45% and 96.51%, respectively. Comparing the model inputs, we can observe... |
Automatic Severity Classification of Coronary Artery Disease via Recurrent Capsule Network | 1807.06718 | TABLE VII: Comparison of the Interest of Uni/Bi-LSTMs and Capsules | ['[EMPTY]', 'P', 'R', 'F{}_{1}'] | [['All Bi-LSTMs', '94.76', '[BOLD] 97.60', '96.16'], ['Softmax layer', '95.16', '97.30', '96.22'], ['[BOLD] Uni/Bi-LSTMs & Capsule layer', '[BOLD] 95.59', '97.45', '[BOLD] 96.51']] | To analyze the interest of Uni/Bi-LSTMs and capsules, we compare our model with that all using Bi-LSTMs or replacing the capsule layer by a fully-connected layer with a softmax function. From the table, we can observe that the F{}_{1}-score of our model is higher than that all using Bi-LSTMs by 0.29\%, and higher than ... |
Select-Additive Learning: Improving Generalization in Multimodal Sentiment Analysis | 1609.05244 | Table 2: Across data set experiments | ['[EMPTY]', 'Youtube [BOLD] CNN', 'Youtube [BOLD] SAL-CNN', 'MOUD [BOLD] CNN', 'MOUD [BOLD] SAL-CNN'] | [['Verbal', '0.605', '[BOLD] 0.657', '0.522', '[BOLD] 0.569'], ['Acoustic', '0.441', '[BOLD] 0.564', '0.455', '[BOLD] 0.549'], ['Visual', '0.492', '[BOLD] 0.549', '[BOLD] 0.555', '0.548'], ['Ver+Acou', '0.642', '[BOLD] 0.652', '0.515', '[BOLD] 0.574'], ['Ver+Vis', '0.642', '[BOLD] 0.667', '0.542', '[BOLD] 0.574'], ['Ac... | First, it is noteworthy that in some cases the performance of the CNN is worse than mere chance. This inferior performance substantiates the existence of the non-generalization problems we are targeting. |
FRAGE: Frequency-Agnostic Word Representation | 1809.06858 | Table 1: Results on three word similarity datasets. | ['[BOLD] RG65 [BOLD] Orig.', '[BOLD] RG65 [BOLD] with FRAGE', '[BOLD] WS [BOLD] Orig.', '[BOLD] WS [BOLD] with FRAGE', '[BOLD] RW [BOLD] Orig.', '[BOLD] RW [BOLD] with FRAGE'] | [['75.63', '[BOLD] 78.78', '66.74', '[BOLD] 69.35', '52.67', '[BOLD] 58.12']] | Word Similarity From the table, we can see that our method consistently outperforms the baseline on all datasets. In particular, we outperform the baseline for about 5.4 points on the rare word dataset RW. This result shows that our method improves the representation of words, especially the rare words. |
FRAGE: Frequency-Agnostic Word Representation | 1809.06858 | Table 2: Perplexity on validation and test sets on Penn Treebank and WikiText2. Smaller the perplexity, better the result. Baseline results are obtained from DBLP:journals/corr/abs-1708-02182 ; DBLP:journals/corr/abs-1711-03953 . “Paras” denotes the number of model parameters. | ['[EMPTY]', '[EMPTY]', '[BOLD] Paras', '[BOLD] Orig. Validation', '[BOLD] Orig. Test', '[BOLD] with FRAGE Validation', '[BOLD] with FRAGE Test'] | [['[BOLD] PTB', 'AWD-LSTM w/o finetune DBLP:journals/corr/abs-1708-02182 ', '24M', '60.7', '58.8', '60.2', '58.0'], ['[BOLD] PTB', 'AWD-LSTM DBLP:journals/corr/abs-1708-02182 ', '24M', '60.0', '57.3', '58.1', '56.1'], ['[BOLD] PTB', 'AWD-LSTM + continuous cache pointer DBLP:journals/corr/abs-1708-02182 ', '24M', '53.9'... | Language Modeling In all these settings, our method outperforms the two baselines. On PTB dataset, our method improves the AWD-LSTM and AWD-LSTM-MoS baseline by 0.8/1.2/1.0 and 0.76/1.13/1.15 points in test set at different checkpoints. On WT2 dataset, which contains more rare words, our method achieves larger improvem... |
FRAGE: Frequency-Agnostic Word Representation | 1809.06858 | Table 3: BLEU scores on test set on WMT2014 English-German and IWSLT German-English tasks. | ['[BOLD] WMT En→De [BOLD] Method', '[BOLD] WMT En→De [BOLD] BLEU', '[BOLD] IWSLT De→En [BOLD] Method', '[BOLD] IWSLT De→En [BOLD] BLEU'] | [['ByteNet kalchbrenner2016neural ', '23.75', 'DeepConv gehring2016convolutional ', '30.04'], ['ConvS2S gehring2017convolutional ', '25.16', 'Dual transfer learning Wang2018Dual ', '32.35'], ['Transformer Base vaswani2017attention ', '27.30', 'ConvS2S+SeqNLL edunov2017classical ', '32.68'], ['Transformer Base with FR... | Machine Translation We outperform the baselines for 1.06/0.71 in the term of BLEU in transformer_base and transformer_big settings in WMT14 English-German task, respectively. The model learned from adversarial training also outperforms original one in IWSLT14 German-English task by 0.85. These results show improving wo... |
FRAGE: Frequency-Agnostic Word Representation | 1809.06858 | Table 10: BLEU scores on test set of the WMT14 English-German task and IWSLT14 German-English task. Our method is denoted as “FRAGE”, “Reweighting” denotes reweighting the loss of each word by reciprocal of its frequency, and “Weight Decay” denotes putting weight decay rate (0.2) on embeddings. | ['[BOLD] WMT En→De [BOLD] Method', '[BOLD] WMT En→De [BOLD] BLEU', '[BOLD] IWSLT De→En [BOLD] Method', '[BOLD] IWSLT De→En [BOLD] BLEU'] | [['Transformer Base ', '27.30', 'Transformer', '33.12'], ['Transformer Base + Reweighting', '26.04', 'Transformer + Reweighting', '31.04'], ['Transformer Base + Weight Decay', '26.76', 'Transformer + Weight Decay', '32.52'], ['Transformer Base with FRAGE', '[BOLD] 28.36', 'Transformer with FRAGE', '[BOLD] 33.97']] | We compare some other simple methods with ours on machine translation tasks, which include reweighting method and l2 regularization (weight decay). We notice that those simple methods do not work for the tasks, even have negative effects. |
Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Autoencoders | 1904.02142 | Table 6: F1 for different model variants on the binary WSJ validation set with included punctuation. The binary trees are as-is (∅) or modified according to the post-processing heuristic (+PP). The mean F1 is shown across three random seeds. | ['[BOLD] Composition', '[BOLD] Loss', 'F1 [ITALIC] μ ∅', 'F1 [ITALIC] μ +PP'] | [['TreeLSTM', 'Margin', '49.9', '53.1'], ['TreeLSTM', 'Softmax', '52.0', '52.9'], ['MLP', 'Margin', '49.7', '54.4'], ['MLP', 'Softmax', '52.6', '55.5'], ['MLPKernel', 'Softmax', '51.8', '54.8'], ['MLPShared', 'Softmax', '50.8', '56.7']] | We see that MLP composition consistently performs better than with TreeLSTM, that MLP benefits from the Softmax loss, and that the best performance comes from sharing parameters. All other experimental results use this highly performant setting unless otherwise specified. |
Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Autoencoders | 1904.02142 | Table 1: Full WSJ (test set) unsupervised unlabeled binary constituency parsing including punctuation. † indicates trained to optimize NLI task. Mean and max are calculated over five random restarts. PRPN F1 was calculated using the parse trees and results provided by Htut et al. (2018). The depth (δ) is the average tr... | ['[BOLD] Model', 'F1 [ITALIC] μ', 'F1 [ITALIC] max', '[ITALIC] δ'] | [['LB', '13.1', '13.1', '12.4'], ['RB', '16.5', '16.5', '12.4'], ['Random', '21.4', '21.4', '5.3'], ['Balanced', '21.3', '21.3', '4.6'], ['RL-SPINN†', '13.2', '13.2', '-'], ['ST-Gumbel - GRU†', '22.8 ±1.6', '25.0', '-'], ['PRPN-UP', '38.3 ±0.5', '39.8', '5.9'], ['PRPN-LM', '35.0 ±5.4', '42.8', '6.2'], ['ON-LSTM', '47.7... | This model achieves a mean F1 7 points higher than ON-LSTM and an increase of over 6.5 max F1 points. We also see that DIORA exhibits much less variance between random seeds than ON-LSTM. Additionally, we find that PRPN-UP and DIORA benefit much more from the +PP heuristic than PRPN-LM. This is consistent with qualitat... |
Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Autoencoders | 1904.02142 | Table 2: NLI unsupervised unlabeled binary constituency parsing comparing to CoreNLP predicted parses. PRPN F1 was calculated using the parse trees and results provided by Htut et al. (2018). F1 median and max are calculated over five random seeds and the top F1 value in each column is bolded. Note that we use median r... | ['[BOLD] Model', 'F1 [ITALIC] median', 'F1 [ITALIC] max', '[ITALIC] δ'] | [['Random', '27.0', '27.0', '4.4'], ['Balanced', '21.3', '21.3', '3.9'], ['PRPN-UP', '48.6', '-', '4.9'], ['PRPN-LM', '50.4', '-', '5.1'], ['DIORA', '51.2', '53.3', '6.4'], ['PRPN-UP+PP', '-', '54.8', '5.2'], ['PRPN-LM+PP', '-', '50.4', '5.1'], ['DIORA+PP', '59.0', '[BOLD] 59.1', '6.7']] | Using the heuristic, DIORA greatly surpasses both variants of PRPN. A second caveat is that SNLI Bowman et al. Syntactic parsers often suffer significant performance drops when predicting outside of the newswire domain that the models were trained on. |
Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Autoencoders | 1904.02142 | Table 3: WSJ-10 and WSJ-40 unsupervised non-binary unlabeled constituency parsing with punctuation removed. † indicates that the model predicts a full, non-binary parse with additional resources. ‡ indicates model was trained on WSJ data and PRPNNLI was trained on MultiNLI data. CCM uses predicted POS tags while CCMgol... | ['[BOLD] Model', '[BOLD] WSJ-10 F1 [ITALIC] μ', '[BOLD] WSJ-10 F1 [ITALIC] max', '[BOLD] WSJ-40 F1 [ITALIC] μ', '[BOLD] WSJ-40 F1 [ITALIC] max'] | [['UB', '87.8', '87.8', '85.7', '85.7'], ['LB', '28.7', '28.7', '12.0', '12.0'], ['RB', '61.7', '61.7', '40.7', '40.7'], ['CCM†', '-', '63.2', '-', '-'], ['CCM [ITALIC] gold†', '-', '71.9', '-', '33.7'], ['PRLG †', '-', '[BOLD] 72.1', '-', '54.6'], ['PRPN [ITALIC] NLI', '66.3 ±0.8', '68.5', '-', '-'], ['PRPN‡', '70.5 ±... | We also compare our models to two subsets of the WSJ dataset that were used in previous unsupervised parsing evaluations. WSJ-10 and WSJ-40 contain sentences up to length 10 and 40 respectively after punctuation removal. The WSJ-10 split has been difficult for latent tree parsers such as DIORA, PRPN, and ON-LSTM, none ... |
Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Autoencoders | 1904.02142 | Table 4: Segment recall from WSJ separated by phrase type. The 10 most frequent phrase types are shown above, and the highest value in each row is bolded. P-UP=PRNP-UP, P-LM=PRPN-LM | ['Label', 'Count', 'DIORA', 'P-UP', 'P-LM'] | [['NP', '297,872', '[BOLD] 0.767', '0.687', '0.598'], ['VP', '168,605', '[BOLD] 0.628', '0.393', '0.316'], ['PP', '116,338', '0.595', '0.497', '[BOLD] 0.602'], ['S', '87,714', '[BOLD] 0.798', '0.639', '0.657'], ['SBAR', '24,743', '[BOLD] 0.613', '0.403', '0.554'], ['ADJP', '12,263', '[BOLD] 0.604', '0.342', '0.360'], [... | In many scenarios, one is only concerned with extracting particular constituent phrases rather than a full parse. Common use cases would be identifying entities, noun phrases, or verb phrases for downstream analysis. To get an idea of how well our model can perform on phrase segmentation, we consider the maximum recall... |
Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Autoencoders | 1904.02142 | Table 5: P@1, P@10, and P@100 for labeled chunks from CoNLL-2000 and CoNLL 2012 datasets. For all metrics, higher is better. The top value in each column is bolded. Diora uses the concatenation of the inside and outside vector at each cell which performed better than either in isolation. | ['[BOLD] Model', 'Dim', '[BOLD] CoNLL 2000 P@1', '[BOLD] CoNLL 2000 P@10', '[BOLD] CoNLL 2000 P@100', '[BOLD] CoNLL 2012 P@1', '[BOLD] CoNLL 2012 P@10', '[BOLD] CoNLL 2012 P@100'] | [['Random', '800', '0.684', '0.683', '0.680', '0.137', '0.133', '0.135'], ['ELMo [ITALIC] CI', '1024', '0.962', '0.955', '0.957', '0.708', '0.643', '0.544'], ['ELMo [ITALIC] SI', '4096', '0.970', '0.964', '0.955', '0.660', '0.624', '0.533'], ['ELMo', '4096', '0.987', '0.983', '0.974', '[BOLD] 0.896', '[BOLD] 0.847', '[... | For each of the labeled spans with length greater than one, we first generate its phrase representation. We then calculate its cosine similarity to all other labeled spans. We then calculate if the label for that query span matches the labels for each of the K most similar other spans in the dataset. |
FQuAD: French Question Answering Dataset | 2002.06071 | Table 3: The number of articles, paragraphs and questions for FQuAD1.1 | ['Dataset', 'Articles', 'Paragraphs', 'Questions'] | [['Train', '271', '12123', '50741'], ['Development', '30', '1387', '5668'], ['Test', '25', '1398', '5594']] | The guidelines for writing question and answer pairs for each paragraph are the same as for SQuAD1.1 [rajpurkar-etal-2016-squad]. First, the paragraph is presented to the student on the platform and the student reads it. Second, the student thinks of a question whose answer is a span of text within the context. Third, ... |
FQuAD: French Question Answering Dataset | 2002.06071 | Table 7: Human Performance on FQuAD | ['Dataset', 'F1 [%]', 'EM [%]'] | [['FQuAD1.0-test.', '92.1', '78.4'], ['FQuAD1.1-test', '91.2', '75.9'], ['"FQuAD1.1-test new samples"', '90.5', '74.1'], ['FQuAD1.0-dev', '92.6', '79.5'], ['FQuAD1.1-dev', '92.1', '78.3'], ['"FQuAD1.1-dev new samples"', '91.4', '76.7']] | The human score on FQuAD1.0 reaches 92.1% F1 and 78.4% EM on the test set and 92.6% and 79.5% on the development set. On FQuAD1.1, it reaches 91.2% F1 and 75.9% EM on the test set and 92.1% and 78.3% on the development set. We observe that there is a noticeable gap between the human performance on FQuAD1.0 test dataset... |
FQuAD: French Question Answering Dataset | 2002.06071 | Table 8: Answer type comparison for the development sets of FQuAD1.1 and SQuAD1.1 | ['Answer type', 'FQuAD1.1 [%]', 'SQuAD1.1 [%]'] | [['Common noun', '26.6', '31.8'], ['Person', '14.6', '12.9'], ['Other proper nouns', '13.8', '15.3'], ['Location', '14.1', '4.4'], ['Date', '7.3', '8.9'], ['Other numeric', '13.6', '10.9'], ['Verb', '6.6', '5.5'], ['Adjective', '2.6', '3.9'], ['Other', '0.9', '2.7']] | S5SS3SSS0Px1 Answer type distribution For both datasets, the most represented answer type is Common Noun with FQuAD1.1 scoring 26.6% and SQuAD1.1 scoring 31.8%. The less represented ones are Adjective and Other which have a noticeable higher proportion for SQuAD1.1 than FQuAD1.1 Compared to SQuAD1.1, a significant diff... |
Backpropagating through Structured Argmax using a spigot | 1805.04658 | Table 2: Test accuracy of sentiment classification on Stanford Sentiment Treebank. Bold font indicates the best performance. | ['[BOLD] Model', '[BOLD] Accuracy (%)'] | [['BiLSTM', '84.8'], ['pipeline', '85.7'], ['ste', '85.4'], ['spigot', '[BOLD] 86.3']] | Pipelined semantic dependency predictions brings 0.9% absolute improvement in classification accuracy, and spigot outperforms all baselines. In this task ste achieves slightly worse performance than a fixed pre-trained pipeline. |
Backpropagating through Structured Argmax using a spigot | 1805.04658 | Table 3: Syntactic parsing performance (in unlabeled attachment score, UAS) and DM semantic parsing performance (in labeled F1) on different groups of the development data. Both systems predict the same syntactic parses for instances from Same, and they disagree on instances from Diff (§5). | ['[BOLD] Split', '[BOLD] # Sent.', '[BOLD] Model', '[BOLD] UAS', '[BOLD] DM'] | [['Same', '1011', 'pipeline', '97.4', '94.0'], ['Same', '1011', 'spigot', '97.4', '94.3'], ['Diff', '681', 'pipeline', '91.3', '88.1'], ['Diff', '681', 'spigot', '89.6', '89.2']] | We consider the development set instances where both syntactic and semantic annotations are available, and partition them based on whether the two systems’ syntactic predictions agree (Same), or not (Diff). The second group includes sentences with much lower syntactic parsing accuracy (91.3 vs. 97.4 UAS), and spigot fu... |
DAWT: Densely Annotated Wikipedia Texts across multiple languages | 1703.00948 | Table 7: Accuracy of Semantic Analogy | ['[BOLD] Relation', '[BOLD] GloVe Word dimensionality 50', '[BOLD] GloVe Word dimensionality 100', '[BOLD] GloVe Word dimensionality 200', '[BOLD] GloVe Word dimensionality 300', '[BOLD] DAWT Entity dimensionality 50', '[BOLD] DAWT Entity dimensionality 300', '[BOLD] DAWT Entity dimensionality 1000'] | [['Capital-World', '74.43', '92.77', '97.05', '97.94', '93.24', '93.95', '91.81'], ['City-in-State', '23.22', '40.10', '63.90', '72.59', '68.39', '88.98', '87.90'], ['Capital-Common-Countries', '80.04', '95.06', '96.64', '97.23', '78.66', '79.64', '71.54'], ['Currency', '17.29', '30.05', '37.77', '35.90', '43.88', '13.... | In comparison, it also shows the accuracy from GloVe word embeddings with vector sizes of 50, 100, 200, and 300. Entity embeddings have better performance with vector size of 50. As we increase vector size, word embeddings perform significantly better and outperform entity embeddings when the vector size is 200 or high... |
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