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Cross-lingual Word Analogies using Linear Transformations between Semantic Spaces | 1807.04175 | (b) state-currency | ['[EMPTY]', 'En', 'De', 'Es', 'It', 'Cs', 'Hr'] | [['En', '11.1', '7.4', '3.9', '4.4', '2.1', '5.3'], ['De', '5.8', '6.7', '1.5', '3.2', '1.5', '3.4'], ['Es', '6.5', '3.7', '2.8', '3.4', '1.8', '1.4'], ['It', '6.3', '4.9', '2.8', '3.7', '3.0', '3.1'], ['Cs', '3.4', '2.7', '1.7', '2.5', '1.0', '1.6'], ['Hr', '5.3', '5.7', '1.5', '1.6', '1.5', '4.3']] | Again, rows represent the source language a and columns the target language b. The results were achieved using B-CCA-cu transformation with dictionaries of size n=20,000. Each language seems to have strengths and weaknesses. |
Cross-lingual Word Analogies using Linear Transformations between Semantic Spaces | 1807.04175 | (c) capital-common-countries | ['[EMPTY]', 'En', 'De', 'Es', 'It', 'Cs', 'Hr'] | [['En', '95.3', '81.7', '86.7', '86.8', '48.8', '53.3'], ['De', '91.9', '82.6', '85.9', '89.2', '55.0', '49.3'], ['Es', '93.8', '82.8', '83.3', '84.5', '54.7', '47.8'], ['It', '93.6', '83.2', '85.7', '88.9', '54.3', '53.1'], ['Cs', '91.1', '77.9', '79.5', '80.0', '44.9', '43.9'], ['Hr', '71.1', '55.0', '64.4', '55.6', ... | Again, rows represent the source language a and columns the target language b. The results were achieved using B-CCA-cu transformation with dictionaries of size n=20,000. Each language seems to have strengths and weaknesses. |
Cross-lingual Word Analogies using Linear Transformations between Semantic Spaces | 1807.04175 | (d) state-adjective | ['[EMPTY]', 'En', 'De', 'Es', 'It', 'Cs', 'Hr'] | [['En', '91.2', '58.7', '90.2', '91.8', '86.3', '88.0'], ['De', '91.1', '75.9', '86.0', '92.8', '73.5', '80.2'], ['Es', '90.5', '71.5', '87.4', '94.1', '83.8', '83.6'], ['It', '90.5', '61.5', '89.8', '89.1', '90.1', '85.2'], ['Cs', '88.5', '44.6', '86.6', '90.4', '92.7', '80.2'], ['Hr', '86.6', '66.8', '82.0', '85.4', ... | Again, rows represent the source language a and columns the target language b. The results were achieved using B-CCA-cu transformation with dictionaries of size n=20,000. Each language seems to have strengths and weaknesses. |
Cross-lingual Word Analogies using Linear Transformations between Semantic Spaces | 1807.04175 | (e) adjective-comparative | ['[EMPTY]', 'En', 'De', 'Es', 'It', 'Cs', 'Hr'] | [['En', '78.5', '55.1', '1.7', '10.0', '34.5', '31.6'], ['De', '68.4', '59.1', '2.2', '7.3', '17.4', '16.8'], ['Es', '34.8', '29.2', '25.0', '12.0', '4.5', '9.6'], ['It', '41.3', '31.9', '8.0', '13.3', '4.3', '5.5'], ['Cs', '76.4', '49.7', '2.0', '15.3', '48.4', '33.1'], ['Hr', '67.6', '45.3', '8.3', '15.6', '32.6', '3... | Again, rows represent the source language a and columns the target language b. The results were achieved using B-CCA-cu transformation with dictionaries of size n=20,000. Each language seems to have strengths and weaknesses. |
Cross-lingual Word Analogies using Linear Transformations between Semantic Spaces | 1807.04175 | (f) adjective-superlative | ['[EMPTY]', 'En', 'De', 'Es', 'It', 'Cs', 'Hr'] | [['En', '68.9', '15.3', '11.5', '20.2', '12.0', '19.0'], ['De', '63.8', '32.9', '12.5', '20.1', '15.6', '19.8'], ['Es', '4.6', '0.4', '32.8', '37.3', '0.0', '0.2'], ['It', '5.9', '0.5', '24.9', '62.1', '0.1', '0.2'], ['Cs', '54.6', '21.8', '4.9', '24.3', '28.5', '17.3'], ['Hr', '57.2', '21.6', '7.5', '10.8', '21.6', '2... | Again, rows represent the source language a and columns the target language b. The results were achieved using B-CCA-cu transformation with dictionaries of size n=20,000. Each language seems to have strengths and weaknesses. |
Cross-lingual Word Analogies using Linear Transformations between Semantic Spaces | 1807.04175 | (g) adjective-opposite | ['[EMPTY]', 'En', 'De', 'Es', 'It', 'Cs', 'Hr'] | [['En', '51.4', '39.4', '40.7', '38.2', '79.8', '49.8'], ['De', '49.5', '33.5', '42.9', '37.9', '78.7', '47.4'], ['Es', '46.2', '37.2', '40.3', '37.3', '76.7', '47.2'], ['It', '49.6', '38.9', '43.3', '38.9', '79.2', '47.4'], ['Cs', '49.6', '35.6', '33.9', '34.3', '78.9', '41.0'], ['Hr', '46.6', '40.2', '41.4', '36.4', ... | Again, rows represent the source language a and columns the target language b. The results were achieved using B-CCA-cu transformation with dictionaries of size n=20,000. Each language seems to have strengths and weaknesses. |
Cross-lingual Word Analogies using Linear Transformations between Semantic Spaces | 1807.04175 | (h) noun-plural | ['[EMPTY]', 'En', 'De', 'Es', 'It', 'Cs', 'Hr'] | [['En', '66.8', '48.2', '67.6', '45.0', '32.6', '40.7'], ['De', '66.1', '49.0', '65.2', '41.8', '33.2', '40.4'], ['Es', '68.9', '48.6', '71.7', '55.4', '32.1', '45.0'], ['It', '68.6', '48.4', '72.5', '52.6', '33.3', '42.8'], ['Cs', '62.2', '43.8', '61.7', '36.2', '39.4', '31.3'], ['Hr', '66.8', '47.6', '63.2', '41.8', ... | Again, rows represent the source language a and columns the target language b. The results were achieved using B-CCA-cu transformation with dictionaries of size n=20,000. Each language seems to have strengths and weaknesses. |
Interactive Variance Attention based Online Spoiler Detection for Time-Sync Comments | 1908.03451 | Table 4. F1-score, Precision, and Recall with different IVA parameters | ['Parameters Metrics', 'R=1 Precision', 'R=1 Recall', 'R=1 F1-score', 'R=2 Precision', 'R=2 Recall', 'R=2 F1-score', 'R=3 Precision', 'R=3 Recall', 'R=3 F1-score', 'R=4 Precision', 'R=4 Recall', 'R=4 F1-score'] | [['P=1', '0.489', '0.472', '0.480', '0.525', '0.566', '0.525', '0.537', '0.589', '0.540', '0.545', '0.607', '0.555'], ['P=2', '0.583', '0.563', '0.561', '0.638', '0.662', '0.649', '0.642', '0.682', '0.652', '0.628', '0.695', '0.673'], ['P=3', '0.688', '0.733', '0.712', '0.759', '0.762', '0.740', '0.817', '0.797', '0.80... | Finally, we change the number of former neighbors R and keyframe P to see their influence on the experiment result in the validation set. We can find the F1-score increase with the number of former neighbors R and keyframe P. This result proves that our application of the interactive and real-time properties of TSCs is... |
Interactive Variance Attention based Online Spoiler Detection for Time-Sync Comments | 1908.03451 | Table 3. F1-score, Precision, and Recall of each method | ['[BOLD] (a) TV-series Baselines', '[BOLD] (a) TV-series Precision', '[BOLD] (a) TV-series Recall', '[BOLD] (a) TV-series F1-score'] | [['KM', '0.443', '[BOLD] 0.892', '0.577'], ['LDA', '0.563', '0.656', '0.606'], ['LI-NPP', '0.677', '0.713', '0.695'], ['DN-GAA', '0.730', '0.798', '0.747'], ['SBN', '0.782', '0.791', '0.779'], ['SBN-WT', '0.762', '0.743', '0.754'], ['SBN-IVA', '[BOLD] 0.843', '0.856', '[BOLD] 0.850'], ['[BOLD] (b) Movies', '[BOLD] (b) ... | The experiments of all the models are repeated for 10 times, and we use the average values as the final results. Compared to the-state-of-art DN-GAA method proposed by Chang et al In other baselines, keyword-matching (KM) method achieves high Recall and low Precision, since it treats many non-spoiler TSCs as spoilers. ... |
Relation Discovery with Out-of-Relation Knowledge Base as Supervision | 1905.01959 | Table 4: Comparison of prediction results based on encoder using NYT122, NYT71, and NYT27 datasets with different KB regularization strategies. | ['[BOLD] Model', '[BOLD] NYT122 F1', '[BOLD] NYT122 F1', '[BOLD] NYT122 NMI', '[BOLD] NYT122 NMI', '[BOLD] NYT71 F1', '[BOLD] NYT71 F1', '[BOLD] NYT71 NMI', '[BOLD] NYT71 NMI', '[BOLD] NYT27 F1', '[BOLD] NYT27 F1', '[BOLD] NYT27 NMI', '[BOLD] NYT27 NMI'] | [['[EMPTY]', 'Mean', 'Std', 'Mean', 'Std', 'Mean', 'Std', 'Mean', 'Std', 'Mean', 'Std', 'Mean', 'Std'], ['Majority', '0.355', '-', '0', '-', '0.121', '-', '0', '-', '0.549', '-', '0', '-'], ['DVAE', '0.417', '0.011', '0.339', '0.009', '0.325', '0.011', '0.375', '0.023', '0.433', '0.018', '0.384', '0.021'], ['DVAE+E', '... | S4SS3SSS0Px2 Comparison on Different Datasets. We also compare our algorithm on the three datasets with different baseline settings. In order to evaluate our model rigorously, besides the original DVAE model, we compare two additional augmented baseline models with the same hyper-parameter setting: DVAE with TransE emb... |
Relation Discovery with Out-of-Relation Knowledge Base as Supervision | 1905.01959 | Table 3: Comparison results on NYT122 with different prediction and regularization strategies (using encoder or decoder). | ['[BOLD] Model', '[BOLD] Metrics Prediction based on encoder', '[BOLD] Metrics Prediction based on encoder', '[BOLD] Metrics Prediction based on encoder', '[BOLD] Metrics Prediction based on encoder', '[BOLD] Metrics Prediction based on decoder', '[BOLD] Metrics Prediction based on decoder', '[BOLD] Metrics Prediction ... | [['[EMPTY]', 'F1', 'F1', 'NMI', 'NMI', 'F1', 'F1', 'NMI', 'NMI'], ['[EMPTY]', 'Mean', 'Std', 'Mean', 'Std', 'Mean', 'Std', 'Mean', 'Std'], ['DVAE', '0.417', '0.011', '0.339', '0.009', '0.419', '0.011', '0.337', '0.014'], ['RegDVAE (Euclidean at encoder)', '[BOLD] 0.469', '0.014', '[BOLD] 0.430', '0.020', '[BOLD] 0.448'... | From the table, we can see that regularization with Euclidean distance performs the best compared to KL and JS. Moreover, the regularization over encoder is better than the regularization over decoder. This may be because the way that we put constraints only over sampled sentences in a batch may hurt the regularization... |
Hierarchical Contextualized Representation for Named Entity Recognition | 1911.02257 | Table 2: F1 scores on CoNLL-2003. † refers to models trained on both training and development datasets. | ['[BOLD] Models', '[ITALIC] F1'] | [['', '90.94'], ['', '91.21'], ['', '91.62'], ['', '91.24 ± 0.12'], ['', '91.35'], ['', '91.57'], ['', '91.64 ± 0.17'], ['', '91.10'], ['', '91.44 ± 0.10'], ['', '91.74'], ['', '91.54'], ['Ours', '[BOLD] 91.96 ± 0.03'], ['[BOLD] + Language Models / External knowledge', '[BOLD] + Language Models / External knowledge'], ... | Our model surpasses previous state-of-the-art approaches on all the three datasets. On CoNLL-2002 Spanish dataset, our model achieves 87.08 F1 score without external knowledge, which surpasses previous best score by 0.4. Considering that the above two datasets are relatively small, we further conduct experiment on a mu... |
Hierarchical Contextualized Representation for Named Entity Recognition | 1911.02257 | Table 3: F1 scores on CoNLL-2002. | ['[BOLD] Models', '[ITALIC] F1'] | [['', '82.95'], ['', '85.75'], ['', '85.77'], ['', '86.68 ± 0.35'], ['Ours', '[BOLD] 87.08 ± 0.16']] | Our model surpasses previous state-of-the-art approaches on all the three datasets. On CoNLL-2002 Spanish dataset, our model achieves 87.08 F1 score without external knowledge, which surpasses previous best score by 0.4. Considering that the above two datasets are relatively small, we further conduct experiment on a mu... |
Hierarchical Contextualized Representation for Named Entity Recognition | 1911.02257 | Table 4: F1 scores on OntoNotes 5.0. | ['[BOLD] Models', '[ITALIC] F1'] | [['', '84.04'], ['', '86.28 ± 0.26'], ['', '86.63 ± 0.49'], ['', '86.84 ± 0.19'], ['', '87.44'], ['', '87.67 ± 0.17'], ['Ours', '[BOLD] 87.98 ± 0.05'], ['[BOLD] + Language Models / External knowledge', '[BOLD] + Language Models / External knowledge'], ['', '87.95'], ['', '88.88'], ['', '89.71'], ['Ours + BERT', '[BOLD]... | Our model surpasses previous state-of-the-art approaches on all the three datasets. On CoNLL-2002 Spanish dataset, our model achieves 87.08 F1 score without external knowledge, which surpasses previous best score by 0.4. Considering that the above two datasets are relatively small, we further conduct experiment on a mu... |
Hierarchical Contextualized Representation for Named Entity Recognition | 1911.02257 | Table 5: Ablation study on the three benchmark datasets. | ['[EMPTY]', 'CoNLL03', 'CoNLL02', 'OntoNotes'] | [['base model', '91.60', '86.65', '87.58'], ['+ sentence-level', '91.80', '86.95', '87.86'], ['+ document-level', '91.79', '86.76', '87.81'], ['+ ALL', '[BOLD] 91.96', '[BOLD] 87.08', '[BOLD] 87.98']] | In this experiment, we individually adopt two hierarchical contextualized representations to enhance the representation of tokens: sentence-level representation for assigning the sentence state to each token and document-level representation for inference. We discover that both sentence-level and document-level represe... |
Hierarchical Contextualized Representation for Named Entity Recognition | 1911.02257 | Table 6: Comparison of different strategies on CoNLL-2003 dataset. ERR is the relative error rate reduction of our model compared to the baseline. | ['[EMPTY]', 'Strategy', '[ITALIC] F1', 'ERR'] | [['base model', '-', '91.60', '-'], ['sentence-level', 'mean-pooling', '91.65', '0.60'], ['sentence-level', 'label-embedding', '91.80', '2.23'], ['document-level', 'dot-product', '91.63', '1.55'], ['document-level', 'scaled dot-product', '91.75', '1.79'], ['document-level', 'cosine similarity', '91.79', '2.38'], ['ALL'... | We further analyze the two hierarchical representations by adopting different strategies. We further conduct experiments to investigate the three compatibility functions used to employ memorized information. Among the three compatibility functions to compute the weight of query word and memorized slots, cosine similari... |
Hierarchical Contextualized Representation for Named Entity Recognition | 1911.02257 | Table 7: Detailed results on the CoNLL-2003 dataset for IV, OOTV, OOEV, OOBV. | ['[EMPTY]', '[BOLD] Baseline [ITALIC] P', '[BOLD] Baseline [ITALIC] R', '[BOLD] Baseline [ITALIC] F1', '[BOLD] Ours [ITALIC] P', '[BOLD] Ours [ITALIC] R', '[BOLD] Ours [ITALIC] F1'] | [['IV', '94.58', '93.16', '93.87', '94.96', '93.58', '[BOLD] 94.26'], ['OOTV', '93.46', '91.57', '92.51', '94.07', '91.85', '[BOLD] 92.95'], ['OOEV', '94.12', '94.12', '94.12', '94.12', '94.12', '94.12'], ['OOBV', '88.42', '84.81', '86.58', '88.51', '85.56', '[BOLD] 87.01']] | out-of-training-vocabulary words (OOTV), out-of-embedding-vocabulary words (OOEV), and out-of-both-vocabulary words (OOBV) on CoNLL-2003 datset. According to our statistic, 63.40% / 52.43% / 84.68% of the NEs in the test set of CoNLL-2003, CoNLL-2002, and OntoNotes datasets are located in the IV part, respectively. The... |
Graph Pattern Entity Ranking Model for Knowledge Graph Completion | 1904.02856 | Table 2: Mean Reciprocal Rank (MRR) and HITS@n scores obtained for the link prediction tasks on the WN18, FB15k, WN18RR, and FB15k-237 datasets. The highest result for each column is shown in bold. The results of TransE and TorusE were reported by Ebisu and Ichise Ebisu and Ichise (2018), the results of RESCAL were rep... | ['[EMPTY]', 'WN18 MRR', 'WN18 HITS@', 'WN18 HITS@', 'WN18 HITS@', 'FB15k MRR', 'FB15k HITS@', 'FB15k HITS@', 'FB15k HITS@', 'WN18RR MRR', 'WN18RR HITS@', 'WN18RR HITS@', 'WN18RR HITS@', 'FB15k-237 MRR', 'FB15k-237 HITS@', 'FB15k-237 HITS@', 'FB15k-237 HITS@'] | [['Model', '[EMPTY]', '1', '3', '10', '[EMPTY]', '1', '3', '10', '[EMPTY]', '1', '3', '10', '[EMPTY]', '1', '3', '10'], ['TransE', '0.397', '0.040', '0.745', '0.923', '0.414', '0.247', '0.534', '0.688', '0.182', '0.027', '0.295', '0.444', '0.257', '0.174', '0.284', '0.420'], ['TorusE', '0.947', '0.943', '0.950', '0.954... | The Node+LinkFeat model performed well on WN18 and FB15k because these datasets often contain the reverse relations of other relations. In other words, it shows that knowledge graph embedding models failed to capture this redundancy. On the other hand, our proposed models, GPro and GRank, generally yield better results... |
Massively Multilingual Neural Grapheme-to-Phoneme Conversion | 1708.01464 | Table 5: High Resource Results | ['Model', 'WER', 'WER 100', 'PER'] | [['wFST', '[BOLD] 44.17', '21.97', '[BOLD] 14.70'], ['LangID-High', '47.88', '[BOLD] 15.50', '16.89'], ['LangID-All', '48.76', '15.78', '17.35'], ['NoLangID-High', '69.72', '29.24', '35.16'], ['NoLangID-All', '69.82', '29.27', '35.47']] | Having shown that our model exceeds the performance of the wFST-adaptation approach, we next compare it to the baseline models for just high resource languages. The wFST models here are purely monolingual – they do not use data adaptation because there is sufficient training data for each of them. We omit models traine... |
Massively Multilingual Neural Grapheme-to-Phoneme Conversion | 1708.01464 | Table 4: Adapted Results | ['Model', 'WER', 'WER 100', 'PER'] | [['wFST', '88.04', '69.80', '48.01'], ['LangID-High', '74.99', '46.18', '42.64'], ['LangID-Adapted', '75.06', '46.39', '41.77'], ['LangID-All', '[BOLD] 74.10', '[BOLD] 43.23', '[BOLD] 37.85'], ['NoLangID-High', '82.14', '50.17', '54.05'], ['NoLangID-Adapted', '85.11', '48.24', '55.93'], ['NoLangID-All', '83.65', '47.13... | On the 229 languages for which Deri and Knight The best performance came with the version of our model that was trained on data in all available languages, not just the languages it was tested on. Using a language ID token improves results considerably, but even NoLangID beats the baseline in WER and WER 100. |
Massively Multilingual Neural Grapheme-to-Phoneme Conversion | 1708.01464 | Table 6: Results on languages not in the training corpus | ['Model', 'WER', 'WER 100', 'PER'] | [['LangID-High', '[BOLD] 85.94', '58.10', '[BOLD] 53.06'], ['LangID-Adapted', '87.78', '68.40', '65.62'], ['LangID-All', '86.27', '62.31', '54.33'], ['NoLangID-High', '88.52', '58.21', '62.02'], ['NoLangID-Adapted', '91.27', '57.61', '74.07'], ['NoLangID-All', '89.96', '[BOLD] 56.29', '62.79']] | The unseen languages are any that are present in the test corpus but absent from the training data. Deri and Knight did not report results specifically on these languages. Although the NoLangID models sometimes do better on WER 100, even here the LangID models have a slight advantage in WER and PER. This is somewhat su... |
A Read-Write Memory Network for Movie Story Understanding | 1709.09345 | Table 2: Performance comparison for the video+subtitle task on MovieQA public validation/test dataset. (–) means that the method does not participate on the task. Baselines include DEMM (Deep embedded memory network), OVQAP (Only video question answer pairs) and VCFSM (Video clip features with simple MLP). | ['Methods', 'Video+Subtitle val', 'Video+Subtitle test'] | [['OVQAP', '–', '23.61'], ['Simple MLP', '–', '24.09'], ['LSTM + CNN', '–', '23.45'], ['LSTM + Discriminative CNN', '–', '24.32'], ['VCFSM', '–', '24.09'], ['DEMN\xa0', '–', '29.97'], ['MEMN2N\xa0', '34.20', '–'], ['RWMN-noRW', '34.20', '–'], ['RWMN-noR', '36.50', '–'], ['RWMN-noQ', '38.17', '–'], ['RWMN-noVid', '37.20... | Results of VQA task. We observe that RWMN achieves the best performance on both validation and test sets. For example, in the test set, RWMN attains 36.25%, which is significantly better than the runner-up DEMN of 29.97%. |
A Read-Write Memory Network for Movie Story Understanding | 1709.09345 | Table 4: Performance of the RWMN on the video+subtitle task, according to the structure parameters of write/read networks. νw/r: the number of layers for write/read networks, (fw/rvi,sw/rvi,fw/rci): the height and the stride of convolution filters, and the number of output channels. | ['# Layers [ITALIC] νw', '# Layers [ITALIC] νr', 'Write network ( [ITALIC] fwvi, [ITALIC] swvi, [ITALIC] fwci)', 'Read network ( [ITALIC] frvi, [ITALIC] srvi, [ITALIC] fwri)', 'Acc.'] | [['0', '0', '–', '–', '34.2'], ['1', '0', '(40,7,1)', '–', '33.9'], ['1', '0', '(40,30,3)', '–', '36.5'], ['1', '1', '(40,30,3)', '(3,1,1)', '[BOLD] 38.6'], ['1', '1', '(40,60,3)', '(3,1,1)', '33.6'], ['2', '1', '(40,10,3), (10,5,3)', '(3,1,1)', '37.2'], ['2', '1', '(5,3,1), (5,3,1)', '(3,1,1)', '37.3'], ['2', '2', '(4... | We make several observations from the results. First, as the number of CNN layers in read /write network increases, the capacity of memory interaction may increase as well; yet the performance becomes worsen. Presumably, the main reason may be overfitting due to a relative small dataset size of MovieQA as discussed. It... |
Exploring Gap Filling as a Cheaper Alternative to Reading Comprehension Questionnaires when Evaluating Machine Translation for Gisting | 1809.00315 | Table 1: A comparison of BLEU and NIST scores, RCQ marks in the three possible weightings, and GF success rates at different densities. | ['[EMPTY]', 'BLEU', 'NIST', 'RCQ scores Simple', 'RCQ scores Weighted', 'RCQ scores Literal', 'GF scores Overall', 'GF scores 10%', 'GF scores 20%'] | [['Google', '[BOLD] 0.306', '[BOLD] 4.66', '[BOLD] 0.753', '[BOLD] 0.748', '0.776', '0.592', '0.565', '0.619'], ['Bing', '0.281', '4.40', '0.709', '0.695', '0.734', '[BOLD] 0.618', '[BOLD] 0.595', '[BOLD] 0.640'], ['Homebrew', '0.241', '4.51', '0.594', '0.577', '0.608', '0.550', '0.547', '0.553'], ['Systran', '0.203', ... | In view that score distributions are actually very far from normality, the usual significance tests (such as Welch’s t-test) are not applicable; therefore, statistical significances of differences between RCQ and GF scores will be reported throughout using the distribution-agnostic Kolmogorov–Smirnov test. . Jordan-Núñ... |
Exploring Gap Filling as a Cheaper Alternative to Reading Comprehension Questionnaires when Evaluating Machine Translation for Gisting | 1809.00315 | Table 2: Effect in success rates of allowing for synonyms in GF | ['System', 'GF scores with synonyms Overall', 'GF scores with synonyms 10%', 'GF scores with synonyms 20%', 'GF scores without synonyms Overall', 'GF scores without synonyms 10%', 'GF scores without synonyms 20%'] | [['Google', '0.757', '0.711', '0.776', '0.592', '0.565', '0.619'], ['Bing', '[BOLD] 0.795', '[BOLD] 0.785', '[BOLD] 0.804', '[BOLD] 0.618', '[BOLD] 0.595', '[BOLD] 0.640'], ['Homebrew', '0.704', '0.711', '0.697', '0.550', '0.547', '0.553'], ['Systran', '0.765', '0.750', '0.781', '0.569', '0.544', '0.595'], ['MT Average... | Allowing for synonyms: The GF success scores reported thus far have been computed by giving credit only to exact matches. We have studied giving credit to synonyms observed in informant work, namely to those appearing at least twice (in the work of all informants) that, according to one of the authors, preserved the me... |
Natural Language Generation enhances human decision-making with uncertain information | 1606.03254 | Table 3: Average Monetary gains and Confidence scores (Females). | ['[EMPTY]', '[BOLD] Monetary gains', '[BOLD] Confidence'] | [['Graphs only', '60.83', '74.6%'], ['Multi-modal', '118.41', '81.3%'], ['NLG only', '113.86', '65.8%']] | We found that females score significantly higher at the decision task when exposed to either of the NLG output presentations, when compared to the graphics-only presentation (p<0.05, effect = +53.03). In addition, the same group of users scores significantly higher when presented with the multi-modal output as compared... |
Natural Language Generation enhances human decision-making with uncertain information | 1606.03254 | Table 2: Average Monetary gains and Confidence scores (All Adults). | ['[EMPTY]', '[BOLD] Monetary gains', '[BOLD] Confidence'] | [['Graphs only', '81.15', '78.5%'], ['Multi-modal', '117.51', '83.7%'], ['NLG only', '101.33', '66%']] | Multi-modal vs. Graphics-only : We found that use of multi-modal representations leads to gaining significantly higher game scores (i.e. better decision-making) than the Graphics-only representation (p=0.03, effect = +36.36). This is a 44% average increase in game score. Multi-modal vs. NLG-only: However, there is no s... |
Learning Visual Question Answering by Bootstrapping Hard Attention | 1808.00300 | Table 4: Results, in %, on CLEVR. SAN denotes the SAN [9] implementation of [34]. SAN* denotes the SAN implementation of [25]. Object RN** [55] and Stack-NMNs** [52] report the results only on the validation set, whereas others report on the test set. Overall performance of Stack-NMNs** [52] is measured with the “exper... | ['Model', '[BOLD] Overall', 'Count', 'Exist', 'Compare Numbers', 'Query Attribute', 'Compare Attribute'] | [['Human\xa0', '92.6', '86.7', '96.6', '86.5', '95.0', '96.0'], ['Q-type baseline\xa0', '41.8', '34.6', '50.2', '51.0', '36.0', '51.3'], ['LSTM-only\xa0', '46.8', '41.7', '61.1', '69.8', '36.8', '51.8'], ['CNN+LSTM\xa0', '52.3', '43.7', '65.2', '67.1', '49.3', '53.0'], ['SAN\xa0', '68.5', '52.2', '71.1', '73.5', '85.3'... | (TbD+hres) have noted increasing the spatial resolution definitely helps in achieving better performance. Finally, through a visual inspection, we have observed that the fraction of input cells that we have experimented with (k=16 for 8x8 spatial tensor, and k=64 for 14x14 spatial tensor) is sufficient to cover all the... |
Learning Visual Question Answering by Bootstrapping Hard Attention | 1808.00300 | Table 1: Comparison between different number of attended cells (percentage of the whole input), and aggregation operation. We consider a simple summation, and non-local pairwise computations as the aggregation tool. | ['[EMPTY]', 'Percentage', 'Overall', 'Yes/No', 'Number', 'Other'] | [['[EMPTY]', 'of cells', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]'], ['HAN+sum', '16%', '26.99', '40.53', '11.38', '24.15'], ['HAN+sum', '32%', '27.43', '41.05', '11.38', '24.68'], ['HAN+sum', '48%', '27.94', '41.35', '11.93', '25.27'], ['HAN+sum', '64%', '27.80', '40.74', '11.29', '25.52'], ['sum', '100%', '27.96', '... | We begin with the most basic hard attention architecture, which applies hard attention and then does sum pooling over the attended cells, followed by a small MLP. For each experiment, we take the top k cells, out of 100, according to our L2-norm criterion, where k ranges from 16 to 100 (with 100, there is no attention,... |
Learning Visual Question Answering by Bootstrapping Hard Attention | 1808.00300 | Table 2: Comparison between different adaptive hard-attention techniques with average number of attended parts, and aggregation operation. We consider a simple summation, and the non-local pairwise aggregation. Since AdaHAN adaptively selects relevant features, based on the fixed threshold 1w∗h, we report here the aver... | ['[EMPTY]', 'Percentage', 'Overall', 'Yes/No', 'Number', 'Other'] | [['[EMPTY]', 'of cells', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]'], ['AdaHAN+sum', '25.66%', '27.40', '40.70', '11.13', '24.86'], ['AdaHAN+pairwise', '32.63%', '28.65', '52.25', '13.79', '20.33'], ['HAN+sum', '32%', '27.43', '41.05', '11.38', '24.68'], ['HAN+sum', '48%', '27.94', '41.35', '11.93', '25.27'], ['HAN+pai... | Thus far, our experiments have dealt with networks that have a fixed threshold for all images. However, some images and questions may require reasoning about more entities than others. Therefore, we explore a simple adaptive method, where the network chooses how many cells to attend to for each image. We can see that o... |
Learning Visual Question Answering by Bootstrapping Hard Attention | 1808.00300 | Table 3: Comparison between different number of the attended cells as the percentage of the whole input. The results are reported on VQA-CP v2. The second column denotes the percentage of the attended input. The third column denotes number of layers of the MLP (Equations 2 and 3). | ['[EMPTY]', 'Percentage of cells', 'Number of layers', 'Overall', 'Yes/No', 'Number', 'Other'] | [['HAN+sum', '25%', '0', '26.38', '43.21', '13.12', '21.17'], ['HAN+sum', '50%', '0', '26.75', '41.42', '10.94', '23.38'], ['HAN+sum', '75%', '0', '26.82', '41.30', '11.48', '23.42'], ['HAN+sum', '25%', '2', '26.99', '40.53', '11.38', '24.15'], ['HAN+sum', '50%', '2', '27.43', '41.05', '11.38', '24.68'], ['HAN+sum', '7... | In this section, we briefly analyze an important architectural choice: the number of layers used on top of the pretrained embeddings. That is, before the question and image representations are combined, we perform a small amount of processing to “align” the information, so that the embedding can easily tell the relevan... |
An Empirical Study of Discriminative Sequence Labeling Models for Vietnamese Text Processing | 1708.09163 | TABLE V: Performance of the CRF model for PoS tagging with two feature sets | ['[BOLD] Feature set', '[BOLD] Test Acc. [ITALIC] n≤20', '[BOLD] Test Acc. [ITALIC] n≤25', '[BOLD] Training Acc. [ITALIC] n≤20', '[BOLD] Training Acc. [ITALIC] n≤25'] | [['{Word identities, word shapes}', '87.62', '88.93', '90.75', '91.56'], ['{Word identities, word shapes, word embeddings}', '[BOLD] 88.97', '[BOLD] 90.26', '91.64', '92.29']] | The table indicates that incorporating word embedding features helps to improves the accuracy of the CRF model 1.35% from 87.62% to 88.97%. The CRF model outperformed LSTMs when we do not use word embedding features. However, its accuracy is lower than that of LSTMs when word embedding features are incorporated. |
An Empirical Study of Discriminative Sequence Labeling Models for Vietnamese Text Processing | 1708.09163 | TABLE III: Performance of LSTMs for PoS tagging using word identities and word shapes | ['[BOLD] Hidden Units', '[BOLD] Test Acc. [ITALIC] n≤20', '[BOLD] Test Acc. [ITALIC] n≤25', '[BOLD] Training Acc. [ITALIC] n≤20', '[BOLD] Training Acc. [ITALIC] n≤25'] | [['16', '84.85', '86.45', '94.91', '96.40'], ['32', '83.41', '83.89', '94.43', '93.18'], ['64', '83.82', '85.93', '96.04', '96.23'], ['100', '83.49', '85.53', '93.93', '95.74'], ['128', '84.67', '86.75', '97.49', '97.52'], ['150', '85.84', '86.33', '97.49', '97.27'], ['200', '[BOLD] 85.98', '[BOLD] 87.46', '97.27', '97... | We train different LSTMs with varying number hidden units in the range from 32 to 200. We see that the larger number of hidden units is, the better result the tagger can achieve on the test set. The LSTMs tagger achieves 85.98% of accuracy on the test set when the network has 200 hidden units. |
An Empirical Study of Discriminative Sequence Labeling Models for Vietnamese Text Processing | 1708.09163 | TABLE X: Performances of NER systems at VLSP 2016 | ['[BOLD] Team', '[BOLD] Model', '[BOLD] Performance'] | [['Le-Hong\xa0', 'ME', '88.78'], ['[Anonymous]', 'CRF', '86.62'], ['Nguyen et al.\xa0', 'ME', '84.08'], ['Nguyen et al.\xa0', 'LSTM', '83.80'], ['Le et al.\xa0', 'CRF', '78.40']] | In VLSP 2016 workshop, several different systems have been proposed for Vietnamese NER. That system used many hand-crafted features to improve the performance of MEMM. Most approaches in VLSP 2016 used the CRF and maximum entropy models, whose performance is heavily dependent on feature engineering. We observe that alt... |
MS-UEdin Submission to the WMT2018 APE Shared Task: Dual-Source Transformer for Automatic Post-Editing | 1809.00188 | Table 1: Experiments with WMT 2017 data, correcting a phrase-base system. | ['Model', 'dev 2016 [BOLD] TER↓', 'dev 2016 BLEU↑', 'test 2016 [BOLD] TER↓', 'test 2016 BLEU↑', 'test 2017 [BOLD] TER↓', 'test 2017 BLEU↑'] | [['Uncorrected', '24.81', '62.92', '24.76', '62.11', '24.48', '62.49'], ['WMT17: FBK Primary', '19.22', '71.89', '19.32', '70.88', '19.60', '70.07'], ['WMT17: AMU Primary', '—', '—', '19.21', '70.51', '19.77', '69.50'], ['Baseline (single model)', '19.77', '70.54', '20.10', '69.25', '20.43', '68.48'], ['+Tied embedding... | During the WMT2017 APE shared task we submitted a dual-source model with soft and hard attention which placed second right after a very similar dual-source model by the FBK team. FBK and WMT17:AMU (ours). An ensemble of four identical models trained with different random initializations strongly improves over last year... |
MS-UEdin Submission to the WMT2018 APE Shared Task: Dual-Source Transformer for Automatic Post-Editing | 1809.00188 | Table 2: Experiments with WMT 2017+eSCAPE data for SMT system. | ['Model', 'dev 2016 [BOLD] TER↓', 'dev 2016 BLEU↑', 'test 2016 [BOLD] TER↓', 'test 2016 BLEU↑', 'test 2017 [BOLD] TER↓', 'test 2017 BLEU↑'] | [['Transformer all', '17.84', '73.45', '17.81', '72.79', '18.10', '71.72'], ['Transformer 1M', '17.59', '73.45', '18.29', '72.20', '18.42', '71.50'], ['Transformer 2M', '17.92', '73.37', '18.02', '72.41', '18.35', '71.57'], ['Transformer 4M', '17.75', '73.51', '17.89', '72.70', '18.09', '71.78'], ['[BOLD] Transformer x... | So far, we only trained on data that was available during WMT2017. This year, the task organizers added a new large corpus created for automatic post-editing across many domains. We experimented with domain selection algorithms for this corpus and tried to find subsets that would be better suited to the given IT domain... |
MS-UEdin Submission to the WMT2018 APE Shared Task: Dual-Source Transformer for Automatic Post-Editing | 1809.00188 | (a) PBSMT sub-task | ['Systems', '[BOLD] TER↓', 'BLEU↑'] | [['[BOLD] MS-UEdin (Ours)', '[BOLD] 18.00', '[BOLD] 72.52'], ['FBK', '18.62', '71.04'], ['POSTECH', '19.63', '69.87'], ['USAAR DFKI', '22.69', '66.16'], ['DFKI-MLT', '24.19', '63.40'], ['Baseline', '24.24', '62.99']] | For full results with information concerning statistical significance see the full shared task description Chatterjee et al. As expected, improvements are quite significant for the SMT-based system, and much smaller for the NMT-based system. Our submissions to the PBSMT sub-task strongly outperforms all submissions by ... |
MS-UEdin Submission to the WMT2018 APE Shared Task: Dual-Source Transformer for Automatic Post-Editing | 1809.00188 | (b) NMT sub-task | ['Systems', '[BOLD] TER↓', 'BLEU↑'] | [['FBK', '16.46', '75.53'], ['[BOLD] MS-UEdin (Ours)', '[BOLD] 16.50', '[BOLD] 75.44'], ['POSTECH', '16.70', '75.14'], ['Baseline', '16.84', '74.73'], ['USAAR DFKI', '17.23', '74.22'], ['DFKI-MLT', '18.84', '70.87']] | For full results with information concerning statistical significance see the full shared task description Chatterjee et al. As expected, improvements are quite significant for the SMT-based system, and much smaller for the NMT-based system. Our submissions to the PBSMT sub-task strongly outperforms all submissions by ... |
Words are not Equal: Graded Weighting Model for building Composite Document Vectors | 1512.03549 | Table 5: Results on IMDB Movie Review Dataset | ['[BOLD] Method', '[BOLD] Accuracy'] | [['Maas et al.(2011)', '88.89'], ['NBSVM-bi (Wang & Manning, 2012)', '91.22'], ['NBSVM-uni (Wang & Manning, 2012)', '88.29'], ['SVM-uni (Wang & Manning, 2012)', '89.16'], ['Paragraph Vector (Le and Mikolov(2014))', '92.58'], ['Weighted WordVector+Wiki(Our Method)', '88.60'], ['Weighted WordVector+TfIdf(Our Method)', '9... | The main contributor for improvement in results is our new document vector which overcomes the weaknesses of BOW and document vectors taken separately. |
Words are not Equal: Graded Weighting Model for building Composite Document Vectors | 1512.03549 | Table 1: Comparison of accuracies on 3 Datasets (IMDB, Amazon Electronics Review and Hindi Movie Reviews (IITB)) for various types of document composition models. The state of the art for these tasks are: IMDB: 92.58% [Le and Mikolov2014]; Amazon:85.90% [Dredze et al.2008], Hindi:79.0% [Bakliwal et al.2012]. | ['[BOLD] Method', '[BOLD] IMDB', '[BOLD] Amazon', '[BOLD] Hindi'] | [['RNNLM (Baseline)', '86.45', '90.03', '78.84'], ['Paragraph Vector ', '92.58', '91.30', '74.57'], ['Averaged Vector', '88.42', '88.52', '79.62'], ['Weighted Average Vector', '89.56', '88.63', '85.90'], ['Composite Document Vector', '93.91', '92.17', '90.30']] | A surprising event in Information Theory has higher information content than an expected event (Shanon, 1948). The same happens when we give weights to word vectors. We give more weight to events which evoke surprise and less weight to events which are expected. In this work we present an early experiment on the possib... |
Words are not Equal: Graded Weighting Model for building Composite Document Vectors | 1512.03549 | Table 2: Results of Vector Composition with different Operations | ['Composition', 'Accuracy'] | [['Multiplication', '50.30'], ['Average', '88.42'], ['Idf Graded Weighted Average', '[BOLD] 89.56']] | We, therefore, adopt both simple and idf weighted average methods in our work. The advantage with addition is that, it doesnot increase the dimension of the vector and captures high level semantics with ease. They also use additive composition to reflect semantic dependencies. queen - king ≈ clearly show that vectors o... |
Words are not Equal: Graded Weighting Model for building Composite Document Vectors | 1512.03549 | Table 3: Accuracies on our newly released 700-Movie Review Dataset | ['[BOLD] Method', '[BOLD] Feature Selection', '[BOLD] Accuracy'] | [['Document Vector + tfidf', 'None', '74.57'], ['Document Vector + tfidf', 'PCA(n=50)', '76.33'], ['Document Vector + tfidf', 'ANOVA-F', '88.07'], ['Weighted Word Vector + tfidf', 'None', '76.43'], ['Weighted Word Vector + tfidf', 'ANOVA-F', '90.37'], ['Weighted Word Vector + tfidf', 'PCA(n=50)', '78.61']] | Dimensionality Reduction is the process of reducing the number of random variables in such a way that the remaining variables effectively reproduce most of the variability of the dataset. The reason for using such techniques is because of the curse of dimensionality which is a phenomena that occurs in high-dimension bu... |
Words are not Equal: Graded Weighting Model for building Composite Document Vectors | 1512.03549 | Table 9: Accuracies for Product Review and Movie Review Datasets. | ['[BOLD] Features', '[BOLD] Accuracy(1)', '[BOLD] Accuracy(2)'] | [['WordVector Averaging', '78.0', '79.62'], ['WordVector+tf-idf', '90.73', '89.52'], ['WordVector+tf-idf without stop words', '91.14', '89.97'], ['Weighted WordVector', '89.71', '85.90'], ['Weighted WordVector+tfidf', '[BOLD] 92.89', '[BOLD] 90.30']] | We see that there is a slight improvement in accuracy on both datasets once we remove stop-words but the major breakthrough occurs once we used weighted averaging technique for construction of document vectors from word vectors. |
Sequential Short-Text Classification with Recurrent and Convolutional Neural Networks | 1603.03827 | Table 4: Accuracy (%) of our models and other methods from the literature. The majority class model predicts the most frequent class. SVM: [Dernoncourt et al.2016]. Graphical model: [Ji and Bilmes2006]. Naive Bayes: [Lendvai and Geertzen2007]. HMM: [Stolcke et al.2000]. Memory-based Learning: [Rotaru2002]. All five mod... | ['[BOLD] Model', 'DSTC\xa04', 'MRDA', 'SwDA'] | [['CNN', '65.5', '[BOLD] 84.6', '[BOLD] 73.1'], ['LSTM', '[BOLD] 66.2', '84.3', '69.6'], ['Majority class', '25.8', '59.1', '33.7'], ['SVM', '57.0', '–', '–'], ['Graphical model', '–', '81.3', '–'], ['Naive Bayes', '–', '82.0', '–'], ['HMM', '–', '–', '71.0'], ['Memory-based Learning', '–', '–', '72.3'], ['Interlabeler... | Overall, our model shows competitive results, while requiring no human-engineered features. Rigorous comparisons are challenging to draw, as many important details such as text preprocessing and train/valid/test split may vary, and many studies fail to perform several runs despite the randomness in some parts of the tr... |
Training Augmentation with Adversarial Examplesfor Robust Speech Recognition | 1806.02782 | Table 2: WER comparison on CHiME-4 single-channel track evaluation sets with adversarial examples (AdvEx) (ϵ=0.1). | ['system', 'et05_simu BUS', 'et05_simu CAF', 'et05_simu PED', 'et05_simu STR', 'et05_simu [BOLD] AVE.', 'et05_real BUS', 'et05_real CAF', 'et05_real PED', 'et05_real STR', 'et05_real [BOLD] AVE.'] | [['Baseline', '20.25', '30.69', '26.62', '28.74', '26.57', '43.95', '33.64', '25.95', '18.68', '30.55'], ['AdvEx', '19.65', '29.29', '24.75', '26.95', '[BOLD] 25.16', '41.00', '31.34', '24.74', '18.23', '[BOLD] 28.82']] | We see that within a reasonable range (ϵ<0.25) the proposed approach brings consistent gain. Relative WER reductions obtained on et05_real and et05_simu sets were 5.7% and 5.3%, respectively. The proposed approach was able to bring consistent improvements for all types of noises, whether in simulated or real environmen... |
Training Augmentation with Adversarial Examplesfor Robust Speech Recognition | 1806.02782 | Table 1: WER comparison on the Aurora-4 evaluation set with adversarial examples (AdvEx) (ϵ=0.3) | ['[EMPTY]', 'A', 'B', 'C', 'D', 'AVG.'] | [['Baseline', '3.21', '6.08', '6.41', '18.11', '11.05'], ['AdvEx', '3.51', '5.84', '5.79', '14.75', '9.49'], ['WER reduction (%)', '-9.4', '3.9', '9.7', '18.6', '14.1']] | Based on the results, ϵ=0.3 is chosen as the best perturbation weight to train the Aurora-4 model. The model trained on WSJ0m serves as the baseline. With ϵ=0.3, the augmented data training achieves 9.49% WER averaged across the four test sets, a 14.1% relative improvement over the baseline. For the test set with the h... |
SPEECH-TO-SPEECH TRANSLATION BETWEEN UNTRANSCRIBED UNKNOWN LANGUAGES | 1910.00795 | Table 2: Our experiment results based on BTEC Japanese-English speech-to-speech translation. | ['[BOLD] Model (JA-EN) [BOLD] Baseline Tacotron with MFCC source', '[BOLD] Model (JA-EN) [BOLD] Baseline Tacotron with MFCC source', '[BOLD] BLEU -', '[BOLD] METEOR -'] | [['[BOLD] Proposed Speech2Code', '[BOLD] Proposed Speech2Code', '[BOLD] Proposed Speech2Code', '[BOLD] Proposed Speech2Code'], ['Codebook', 'Time Reduction', '[EMPTY]', '[EMPTY]'], ['32', '4', '14.8', '15'], ['32', '8', '14.2', '15.6'], ['32', '12', '16', '16'], ['64', '4', '10.8', '12.1'], ['64', '8', '14.2', '14.7'],... | We tried several hyperparameters, including codebook size and time-reduction factor. Our best performance was produced by a codebook of 128 and a time-reduction factor 8 with a score of 15.3 BLEU and 15.3 METEOR. |
SPEECH-TO-SPEECH TRANSLATION BETWEEN UNTRANSCRIBED UNKNOWN LANGUAGES | 1910.00795 | Table 1: Our experiment results based on BTEC French-English speech-to-speech translation: | ['[BOLD] Model (FR-EN) [BOLD] Baseline Tacotron with MFCC input', '[BOLD] Model (FR-EN) [BOLD] Baseline Tacotron with MFCC input', '[BOLD] BLEU -', '[BOLD] METEOR -'] | [['[BOLD] Proposed Speech2Code', '[BOLD] Proposed Speech2Code', '[BOLD] Proposed Speech2Code', '[BOLD] Proposed Speech2Code'], ['Codebook', 'Time Reduction', '[EMPTY]', '[EMPTY]'], ['32', '4', '19.4', '19.1'], ['32', '8', '23.8', '22.2'], ['32', '12', '23.2', '22.1'], ['64', '4', '16.1', '16.9'], ['64', '8', '24.4', '2... | We tried several hyperparameters, including codebook size and time-reduction factor. Our best performance was produced by codebook of 64 and a time-reduction factor of 12 with a score of 25.0 BLEU and 23.2 METEOR. |
Many Languages, One Parser | 1602.01595 | Table 5: Effect of automatically predicting language ID and POS tags with MaLOPa on LAS scores. | ['LAS', 'language ID', 'coarse POS', 'target language de', 'target language en', 'target language es', 'target language fr', 'target language it', 'target language pt', 'target language sv', 'average'] | [['[EMPTY]', 'gold', 'gold', '78.6', '84.2', '83.4', '82.4', '89.1', '84.2', '82.6', '83.5'], ['[EMPTY]', 'predicted', 'gold', '78.5', '80.2', '83.4', '82.1', '88.9', '83.9', '82.5', '82.7'], ['[EMPTY]', 'gold', 'predicted', '71.2', '79.9', '80.5', '78.5', '85.0', '78.4', '75.5', '78.4'], ['[EMPTY]', 'predicted', 'pred... | The macro average language ID prediction accuracy on the test set across sentences is 94.7%. The macro average accuracy of the POS tagger is 93.3%. the four configurations: {gold language ID, predicted language ID} × {gold POS tags, predicted POS tags}. The performance of the parser suffers mildly (–0.8 LAS points) whe... |
Adapting Word Representations Across Corpora | 1906.02688 | Table 8: Micro and Macro accuracies over all classes and Macro accuracy for the 50% rarest classes. | ['Method', 'Micro', 'Macro', 'Rare classes'] | [['Tgt', '73.4', '47.8', '25.7'], ['Yang17C', '74.9', '50.2', '28.0'], ['Reg-Our', '75.0', '51.8', '31.0'], ['Src-Tune', '75.0', '52.6', '32.3'], ['WECT', '[BOLD] 75.4', '[BOLD] 53.4', '[BOLD] 33.8']] | We also show the macro accuracy of the 50% rarest classes. We find that our snippet selection method WECT provides the highest gains over the baseline. Further, regularizing using our word stability measure provides more useful embeddings than Yang17’s stability scores. Also, while Src-Tune performs better than regular... |
Adapting Word Representations Across Corpora | 1906.02688 | Table 3: Perplexity of the trained language model on various target domains (lower is better). | ['[EMPTY]', 'Physics', 'Gaming', 'Android', 'Unix'] | [['Tgt', '113.7', '163.9', '116.5', '123.1'], ['Src-Tune', '114.9', '161.9', '116.6', '122.1'], ['Yang17C', '114.0', '164.9', '116.9', '122.1'], ['Reg-Our', '112.1', '162.2', '118.9', '122.7'], ['WECT:word', '111.2', '162.0', '114.2', '121.9'], ['WECT:ctxt', '113.2', '166.5', '116.5', '123.6'], ['WECT', '[BOLD] 110.6',... | We observe that perplexity is lowest with our method of jointly training with target and selected source snippets. Surprisingly, the Src-Tune method does not perform as well on the LM task. On two of the four domains Src-Tune increases perplexity beyond the Tgt baseline. Likewise, Yang17C while providing gains over Tgt... |
Studio Ousia’s Quiz Bowl Question Answering System | 1803.08652 | Table 2: Results for Neural Type Predictor. | ['Model Name', 'Metric', 'Sent 1', 'Sent 1–2', 'Sent 1–3', 'Full'] | [['Coarse-grained CNN', 'Accuracy', '0.95', '0.96', '0.97', '0.98'], ['Fine-grained CNN', 'Precision@1', '0.93', '0.95', '0.96', '0.97'], ['Fine-grained CNN', 'Accuracy', '0.56', '0.64', '0.69', '0.73'], ['Fine-grained CNN', 'F1', '0.83', '0.87', '0.89', '0.91']] | The coarse-grained model performed very accurately; the accuracies exceeded 95% for incomplete questions and 98% for full questions. The fine-grained model also achieved good results; its Precision@1 scores were comparable to the accuracies of the coarse-grained model. However, the model suffered when it came to predic... |
Studio Ousia’s Quiz Bowl Question Answering System | 1803.08652 | Table 3: Accuracies of our question answering system. NQS and NTP stand for Neural Quiz Solver and Neural Type Predictor, respectively. | ['Name', 'Sent 1', 'Sent 1–2', 'Sent 1–3', 'Full'] | [['Full model (NQS + NTP + IR)', '0.56', '0.78', '0.88', '0.97'], ['NQS', '0.31', '0.54', '0.70', '0.88'], ['NQS + coarse-grained NTP', '0.33', '0.56', '0.72', '0.89'], ['NQS + fine-grained NTP', '0.33', '0.57', '0.73', '0.89'], ['NQS + NTP', '0.34', '0.57', '0.73', '0.89'], ['NQS + NTP + IR-Wikipedia', '0.48', '0.71',... | Here, we tested the performance using Dataset QA, and used the output of the Answer Scorer to predict the answer. Our system performed very accurately; it achieved 56% accuracy when given only a single sentence and 97% accuracy given the full set of sentences. To further evaluate the effectiveness of each sub-model pre... |
Studio Ousia’s Quiz Bowl Question Answering System | 1803.08652 | Table 4: Accuracies of the top three QA systems submitted in the competition. | ['Name', 'Accuracy'] | [['Our system', '[BOLD] 0.85'], ['Acelove', '0.675'], ['Lunit.io', '0.6'], ['Baseline', '0.55']] | Our system achieved the best performance by a wide margin. To further evaluate the actual performance of the systems in the quiz bowl, the competition organizers performed simulated pairwise matches between the systems following the official quiz bowl rules. Our system outperformed the Acelove system (our system: 1220 ... |
Neural Net Models of Open-domain Discourse Coherence | 1606.01545 | Table 2: Performance on the open-domain binary classification dataset of 984 Wikipedia paragraphs. | ['Model', 'Accuracy'] | [['VLV-GM (MMI)', '[BOLD] 0.873'], ['VLV-GM (bi)', '0.860'], ['VLV-GM (uni)', '0.839'], ['LDA-HMM-GM (MMI)', '0.847'], ['LDA-HMM-GM (bi)', '0.837'], ['LDA-HMM-GM (uni)', '0.814'], ['Seq2Seq (MMI)', '0.840'], ['Seq2Seq (bi)', '0.821'], ['Seq2Seq (uni)', '0.803'], ['Discriminative Model', '0.715'], ['Entity Grid Model', ... | Contrary to the findings on the domain specific dataset in the previous subsection, the discriminative model does not yield compelling results, performing only slightly better than the entity grid model. We believe the poor performance is due to the sentence-level negative sampling used by the discriminative model. Due... |
Neural Net Models of Open-domain Discourse Coherence | 1606.01545 | Table 4: Adversarial Success for different models. | ['Model', 'adver-1', 'adver-2', 'adver-3'] | [['VLV-GM (MMI)', '[BOLD] 0.174', '[BOLD] 0.120', '[BOLD] 0.054'], ['LDA-HMM-GM (MMI)', '0.130', '0.104', '0.043'], ['Seq2Seq (MMI)', '0.120', '0.090', '0.039'], ['Seq2Seq (bi)', '0.108', '0.078', '0.030'], ['Seq2Seq (uni)', '0.101', '0.068', '0.024']] | As can be seen, the latent variable model VLV-GM is able to generate chunk of texts that are most indistinguishable from coherent texts from humans. This is due to its ability to handle the dependency between neighboring sentences. Performance declines as the number of turns increases due to the accumulation of errors ... |
Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN) | 1412.6632 | Table 9: Performance comparison of different versions of m-RNN models on the Flickr30K dataset. All the models adopt VggNet as the image representation. See Figure 5 for details of the models. | ['[EMPTY]', 'B-1', 'B-2', 'B-3', 'B-4'] | [['m-RNN', '0.600', '0.412', '0.278', '0.187'], ['m-RNN-NoEmbInput', '0.592', '0.408', '0.277', '0.188'], ['m-RNN-OneLayerEmb', '0.594', '0.406', '0.274', '0.184'], ['m-RNN-EmbOneInput', '0.590', '0.406', '0.274', '0.185'], ['m-RNN-visInRnn', '0.466', '0.267', '0.157', '0.101'], ['m-RNN-visInRnn-both', '0.546', '0.333'... | To validate its efficiency, we train three different m-RNN networks: m-RNN-NoEmbInput, m-RNN-OneLayerEmb, m-RNN-EmbOneInput. “m-RNN-NoEmbInput” denotes the m-RNN model whose connection between the word embedding layer \@slowromancapii@ and the multimodal layer is cut off. Thus the multimodal layer has only two inputs: ... |
Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN) | 1412.6632 | Table 3: Results of R@K and median rank (Med r) for Flickr8K dataset. “-AlexNet” denotes the image representation based on AlexNet extracted from the whole image frame. “-RCNN” denotes the image representation extracted from possible objects detected by the RCNN algorithm. | ['[EMPTY]', 'Sentence Retrival (Image to Text) R@1', 'Sentence Retrival (Image to Text) R@5', 'Sentence Retrival (Image to Text) R@10', 'Sentence Retrival (Image to Text) Med r', 'Image Retrival (Text to Image) R@1', 'Image Retrival (Text to Image) R@5', 'Image Retrival (Text to Image) R@10', 'Image Retrival (Text to I... | [['Random', '0.1', '0.5', '1.0', '631', '0.1', '0.5', '1.0', '500'], ['SDT-RNN-AlexNet', '4.5', '18.0', '28.6', '32', '6.1', '18.5', '29.0', '29'], ['Socher-avg-RCNN', '6.0', '22.7', '34.0', '23', '6.6', '21.6', '31.7', '25'], ['DeViSE-avg-RCNN', '4.8', '16.5', '27.3', '28', '5.9', '20.1', '29.6', '29'], ['DeepFE-AlexN... | This dataset was widely used as a benchmark dataset for image and sentence retrieval. We compare our model with several state-of-the-art methods: SDT-RNN (Socher et al. (Frome et al. , DeepFE ( Karpathy et al. with various image representations. Our model outperforms these methods by a large margin when using the same ... |
Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN) | 1412.6632 | Table 8: Results of m-RNN-shared model after applying consensus reranking using nearest neighbors as references (m-RNN-shared-NNref), compared with those of the original m-RNN model on our validation set and MS COCO test server. | ['MS COCO val for consensus reranking', 'MS COCO val for consensus reranking B1', 'MS COCO val for consensus reranking B2', 'MS COCO val for consensus reranking B3', 'MS COCO val for consensus reranking B4', 'MS COCO val for consensus reranking CIDEr', 'MS COCO val for consensus reranking ROUGE_L', 'MS COCO val for con... | [['m-RNN-shared', '0.686', '0.511', '0.375', '0.280', '0.842', '0.500', '0.228'], ['m-RNN-shared-NNref-BLEU', '0.718', '0.550', '0.409', '0.305', '0.909', '0.519', '0.235'], ['m-RNN-shared-NNref-CIDEr', '0.714', '0.543', '0.406', '0.304', '0.938', '0.519', '0.239'], ['m-RNN-shared-NNref-BLEU-Orcale', '0.792', '0.663', ... | For BLEU-based consensus reranking, we get an improvement of 3.5 points on our validation set and 3.3 points on the MS COCO test 2014 set in terms of BLEU4 score. For the CIDEr-based consensus reranking, we get an improvement of 9.4 points on our validation set and 9.8 points on the MS COCO test 2014 set in terms of CI... |
Order-free Learning Alleviating Exposure Bias in Multi-label Classification | 1909.03434 | Table 2: Performance on AAPD | ['Models (a) Seq2set (simp.)', 'Models (a) Seq2set (simp.)', 'maF1', 'miF1', 'ebF1', 'ACC', 'HA', 'Average'] | [['', '', '-', '0.705', '-', '-', '0.9753', '-'], ['(b) Seq2set', '(b) Seq2set', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]'], ['', '', '-', '0.698', '-', '-', '0.9751', '-'], ['(c) SGM+GE', '(c) SGM+GE', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]'], ['', '', '-', '0.710', '-',... | In the following, we show results of the baseline models and the proposed method on three text datasets. For MTL models, we show the results of the four kinds of different decoding strategies described in section Decoder Integration. For a simple comparison, we also compute averages of the five metrics as a reference. ... |
Order-free Learning Alleviating Exposure Bias in Multi-label Classification | 1909.03434 | Table 3: Performance comparisons on Reuters-21578 | ['Models SVM', 'Models SVM', 'maF1', 'miF1', 'ebF1', 'ACC', 'HA', 'Average'] | [['', '', '0.468', '0.787', '-', '-', '-', '-'], ['EncDec', 'EncDec', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]'], ['', '', '0.457', '0.855', '0.891', '0.828', '0.996', '0.805'], ['Baselines', 'Baselines', 'Baselines', 'Baselines', 'Baselines', 'Baselines', 'Baselines', 'Baselines'], ['BR', 'BR', ... | These results demonstrate again the superiority of OCD and the performance gains afforded by the MTL framework. Since there are over 80% of test samples only have one label in this corpus, to truly know the effect of proposed approaches to multi-label classification , we also provide results only on test samples with m... |
Order-free Learning Alleviating Exposure Bias in Multi-label Classification | 1909.03434 | Table 4: Performance comparisons on Audio set. | ['Models Baselines', 'Models Baselines', 'maF1 Baselines', 'miF1 Baselines', 'ebF1 Baselines', 'ACC Baselines', 'HA Baselines', 'Average Baselines'] | [['BR', 'BR', '0.349', '0.480', '0.416', '0.086', '[BOLD] 0.9957', '0.465'], ['Seq2seq', 'Seq2seq', '0.345', '0.448', '0.421', '[BOLD] 0.140', '0.9942', '0.470'], ['Seq2seq + SS', 'Seq2seq + SS', '0.340', '0.448', '0.419', '0.137', '0.9943', '0.468'], ['Order-free RNN', 'Order-free RNN', '0.310', '0.438', '0.410', '0.0... | In the following, we show results of the baseline models and the proposed method on three text datasets. For MTL models, we show the results of the four kinds of different decoding strategies described in section Decoder Integration. For a simple comparison, we also compute averages of the five metrics as a reference. ... |
Order-free Learning Alleviating Exposure Bias in Multi-label Classification | 1909.03434 | Table 5: Performance comparison on resplited AAPD, whose test set contains 2000 samples whose label sets occur in the training set (Seen test set) and 2000 samples are not (Unseen test set). OCD (correct prefix) means we only sample correct labels in the training phase. | ['Models', 'Seen test set miF1', 'Seen test set ebF1', 'Unseen test set miF1', 'Unseen test set ebF1'] | [['Seq2seq', '0.730', '0.749', '0.508', '0.503'], ['Seq2seq + SS', '0.736', '0.754', '0.517', '0.515'], ['Order-free RNN', '0.732', '0.746', '0.496', '0.494'], ['Order-free RNN + SS', '0.724', '0.740', '0.520', '0.517'], ['OCD (correct prefix)', '0.726', '0.741', '0.513', '0.515'], ['OCD', '[BOLD] 0.746', '[BOLD] 0.771... | OCD (correct prefix) means we only sample correct labels in the training phase, so this model has not encountered wrong prefix during training. Clearly, all models perform worse on unseen test set. We can see that SS improves the performance significantly on the unseen test set for both seq2seq and order free RNN. Addi... |
Order-free Learning Alleviating Exposure Bias in Multi-label Classification | 1909.03434 | Table 12: Performance comparisons on Reuters-21578 with more than one label. | ['Models Baselines', 'Models Baselines', 'maF1 Baselines', 'miF1 Baselines', 'ebF1 Baselines', 'ACC Baselines', 'HA Baselines', 'Average Baselines'] | [['BR', 'BR', '0.315', '0.706', '0.712', '0.365', '0.9850', '0.617'], ['Seq2seq', 'Seq2seq', '0.316', '0.712', '0.718', '0.405', '0.9855', '0.627'], ['Seq2seq+SS', 'Seq2seq+SS', '0.325', '0.718', '0.722', '0.380', '0.9859', '0.626'], ['Order-free RNN', 'Order-free RNN', '0.331', '0.730', '0.735', '0.425', '0.9862', '0.... | The smaller test set has 405 samples. However, the performance gap between baseline models and proposed methods are larger, which strengthen the superiority of OCD and MTL. |
Few-Shot Representation Learning for Out-Of-Vocabulary Words | 1907.00505 | Table 2: Performance on Named Entity Recognition and Part-of-Speech Tagging tasks. All methods are evaluated on test data containing OOV words. Results demonstrate that the proposed approach, HiCE + Morph + MAML, improves the downstream model by learning better representations for OOV words. | ['Methods', 'Named Entity Recognition (F1-score) Rare-NER', 'Named Entity Recognition (F1-score) Bio-NER', 'POS Tagging (Acc) Twitter POS'] | [['Word2vec', '0.1862', '0.7205', '0.7649'], ['FastText', '0.1981', '0.7241', '0.8116'], ['Additive', '0.2021', '0.7034', '0.7576'], ['nonce2vec', '0.2096', '0.7289', '0.7734'], ['` [ITALIC] a\xa0la\xa0carte', '0.2153', '0.7423', '0.7883'], ['HiCE w/o Morph', '0.2394', '0.7486', '0.8194'], ['HiCE + Morph', '0.2375', '0... | HiCE outperforms the baselines in all the settings. Compared to the best baseline `a la carte, the relative improvements are 12.4%, 2.9% and 5.1% for Rare-NER, Bio-NER, and Twitter POS, respectively. As aforementioned, the ratio of OOV words in Rare-NER is high. As a result, all the systems perform worse on Rare-NER th... |
Few-Shot Representation Learning for Out-Of-Vocabulary Words | 1907.00505 | Table 1: Performance on the Chimera benchmark dataset with different numbers of context sentences, which is measured by Spearman correlation. Baseline results are from the corresponding papers. | ['Methods', '2-shot', '4-shot', '6-shot'] | [['Word2vec', '0.1459', '0.2457', '0.2498'], ['FastText', '0.1775', '0.1738', '0.1294'], ['Additive', '0.3627', '0.3701', '0.3595'], ['Additive, no stop words', '0.3376', '0.3624', '0.4080'], ['nonce2vec', '0.3320', '0.3668', '0.3890'], ['` [ITALIC] a\xa0la\xa0carte', '0.3634', '0.3844', '0.3941'], ['HiCE w/o Morph', '... | In particular, our method (HiCE+Morph+MAML) Compared with the current state-of-the-art method, `a la carte, the relative improvements (i.e., the performance difference divided by the baseline performance) of HiCE are 4.0%, 5.4% and 9.3% in terms of 2,4,6-shot learning, respectively. We also compare our results with tha... |
To Normalize, or Not to Normalize:The Impact of Normalization on Part-of-Speech Tagging | 1707.05116 | Table 3: Effect of different models on canonical/non-canonical words. | ['[EMPTY]', 'Bilty', '+Norm', '+Vecs', '+Comb'] | [['canonical', '86.1', '85.6', '91.2', '90.1'], ['non-canon.', '50.8', '70.3', '71.1', '78.5']] | Word embeddings have a higher impact on standard, canonical tokens. It is interesting to note that word embeddings and normalization both have a similar yet complementary effect on the words to be normalized (non-canonical). The improvements on non-canonical words seem to be complementary. The combined model additional... |
The Role of Pragmatic and Discourse Contextin Determining Argument Impact | 2004.03034 | Table 6: F1 scores of each model for the claims with various context length values. | ['[EMPTY]', 'C [ITALIC] l=1', 'C [ITALIC] l=2', 'C [ITALIC] l=3', 'C [ITALIC] l=4'] | [['BERT models', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]'], ['Claim only', '48.61±3.16', '53.15±1.95', '54.51±1.91', '50.89±2.95'], ['Claim + Parent', '51.49±2.63', '54.78±2.95', '54.94±2.72', '51.94±2.59'], ['Claim + Context [ITALIC] f(2)', '52.84±2.55', '53.77±1.00', '55.24±2.52', '57.04±1.19'], ['Claim + Context [... | To understand for what kinds of claims the best performing contextual model is more effective, we evaluate the BERT model with flat context representation for claims with context length values 1, 2, 3 and 4 separately. For the claims with context length 1, adding Contextf(3) and Contextf(4) representation along with th... |
The Role of Pragmatic and Discourse Contextin Determining Argument Impact | 2004.03034 | Table 1: Number of claims for the given range of number of votes. There are 19,512 claims in the dataset with 3 or more votes. Out of the claims with 3 or more votes, majority of them have 5 or more votes. | ['# impact votes', '# claims'] | [['[3,5)', '4,495'], ['[5,10)', '5,405'], ['[10,15)', '5,338'], ['[15,20)', '2,093'], ['[20,25)', '934'], ['[25,50)', '992'], ['[50,333)', '255']] | Distribution of impact votes. There are 19,512 claims in total with 3 or more votes. Out of the claims with 3 or more votes, majority of them have 5 or more votes. We limit our study to the claims with at least 5 votes to have a more reliable assignment for the accumulated impact label for each claim. |
The Role of Pragmatic and Discourse Contextin Determining Argument Impact | 2004.03034 | Table 2: Number of claims, with at least 5 votes, above the given threshold of agreement percentage for 3-class and 5-class cases. When we combine the low impact and high impact classes, there are more claims with high agreement score. | ['[EMPTY]', '3-class case', '5-class case'] | [['Agreement score', 'Number of claims', 'Number of claims'], ['>50%', '10,848', '7,304'], ['>60%', '7,386', '4,329'], ['>70%', '4,412', '2,195'], ['>80%', '2,068', '840']] | We see that when we combine the low impact and high impact classes, there are more claims with high agreement score. This may imply that distinguishing between no impact-low impact and high impact-very high impact classes is difficult. To decrease the sparsity issue, in our experiments, we use 3-class representation fo... |
The Role of Pragmatic and Discourse Contextin Determining Argument Impact | 2004.03034 | Table 3: Number of votes for the given impact label. There are 241,884 total votes and majority of them belongs to the category medium impact. | ['Impact label', '# votes- all claims'] | [['No impact', '32,681'], ['Low impact', '37,457'], ['Medium impact', '60,136'], ['High impact', '52,764'], ['Very high impact', '58,846'], ['Total # votes', '241,884']] | Impact label statistics. The claims have 241,884 total votes. The majority of the impact votes belong to medium impact category. We observe that users assign more high impact and very high impact votes than low impact and no impact votes respectively. |
The Role of Pragmatic and Discourse Contextin Determining Argument Impact | 2004.03034 | Table 4: Number of claims for the given range of context length, for claims with more than 5 votes and an agreement score greater than 60%. | ['Context length', '# claims'] | [['1', '1,524'], ['2', '1,977'], ['3', '1,181'], ['[4,5]', '1,436'], ['(5,10]', '1,115'], ['>10', '153']] | Context length (Cl) for a particular claim C is defined by number of claims included in the argument path starting from the thesis until the claim C. We observe that more than half of these claims have 3 or higher context length. |
GECToR – Grammatical Error Correction: Tag, Not Rewrite | 2005.12592 | Table 6: Varying encoders from pretrained Transformers in our sequence labeling system. Training was done on data from training stage II only. | ['[BOLD] Encoder', '[BOLD] CoNLL-2014 (test) [BOLD] P', '[BOLD] CoNLL-2014 (test) [BOLD] R', '[BOLD] CoNLL-2014 (test) [BOLD] F0.5', '[BOLD] BEA-2019 (dev) [BOLD] P', '[BOLD] BEA-2019 (dev) [BOLD] R', '[BOLD] BEA-2019 (dev) [BOLD] F0.5'] | [['LSTM', '51.6', '15.3', '35.0', '-', '-', '-'], ['ALBERT', '59.5', '31.0', '50.3', '43.8', '22.3', '36.7'], ['BERT', '65.6', '36.9', '56.8', '48.3', '29.0', '42.6'], ['GPT-2', '61.0', '6.3', '22.2', '44.5', '5.0', '17.2'], ['RoBERTa', '[BOLD] 67.5', '38.3', '[BOLD] 58.6', '[BOLD] 50.3', '30.5', '[BOLD] 44.5'], ['XLNe... | Encoders from pretrained transformers. We fine-tuned BERT Devlin et al. Radford et al. XLNet Yang et al. et al. We also added LSTM with randomly initialized embeddings (dim=300) as a baseline. BERT, RoBERTa and XLNet encoders perform better than GPT-2 and ALBERT, so we used them only in our next experiments. We hypothe... |
GECToR – Grammatical Error Correction: Tag, Not Rewrite | 2005.12592 | Table 7: Comparison of single models and ensembles. The M2 score for CoNLL-2014 (test) and ERRANT for the BEA-2019 (test) are reported. In ensembles we simply average output probabilities from single models. | ['[BOLD] GEC system', '[BOLD] Ens.', '[BOLD] CoNLL-2014 (test) [BOLD] P', '[BOLD] CoNLL-2014 (test) [BOLD] R', '[BOLD] CoNLL-2014 (test) [BOLD] F0.5', '[BOLD] BEA-2019 (test) [BOLD] P', '[BOLD] BEA-2019 (test) [BOLD] R', '[BOLD] BEA-2019 (test) [BOLD] F0.5'] | [['Zhao et al. ( 2019 )', '[EMPTY]', '67.7', '40.6', '59.8', '-', '-', '-'], ['Awasthi et al. ( 2019 )', '[EMPTY]', '66.1', '43.0', '59.7', '-', '-', '-'], ['Kiyono et al. ( 2019 )', '[EMPTY]', '67.9', '[BOLD] 44.1', '61.3', '65.5', '[BOLD] 59.4', '64.2'], ['Zhao et al. ( 2019 )', '✓', '74.1', '36.3', '61.3', '-', '-',... | Finally, our best single-model, GECToR (XLNet) achieves F0.5 = 65.3 on CoNLL-2014 (test) and F0.5 = 72.4 on BEA-2019 (test). Best ensemble model, GECToR (BERT + RoBERTa + XLNet) |
GECToR – Grammatical Error Correction: Tag, Not Rewrite | 2005.12592 | Table 8: Inference time for NVIDIA Tesla V100 on CoNLL-2014 (test), single model, batch size=128. | ['[BOLD] GEC system', '[BOLD] Time (sec)'] | [['Transformer-NMT, beam size = 12', '4.35'], ['Transformer-NMT, beam size = 4', '1.25'], ['Transformer-NMT, beam size = 1', '0.71'], ['GECToR (XLNet), 5 iterations', '0.40'], ['GECToR (XLNet), 1 iteration', '0.20']] | Speed comparison. We measured the model’s average inference time on NVIDIA Tesla V100 on batch size 128. For sequence tagging we don’t need to predict corrections one-by-one as in autoregressive transformer decoders, so inference is naturally parallelizable and therefore runs many times faster. Our sequence tagger’s in... |
Structurally Sparsified Backward Propagation for Faster Long Short-Term Memory Training | 1806.00512 | Table 3: Performance of trained image captioning models. Higher BLEU-4 score means better performance. | ['Training Method', 'BLEU-4', 'Improvement'] | [['Dense', '31.0', '0'], ['Coarse-grained', '30.3', '-2.26%'], ['3/4 coarse + 1/4 dense', '30.8', '-0.65%'], ['Fine-grained', '30.6', '-1.29%'], ['3/4 fine + 1/4 dense', '31.1', '+0.32%']] | Compared to the 5.92% performance loss in language modeling, this result demonstrates generality to applications beyond language model datasets. Moreover, the performance gap between the sparse training and the dense training can be mitigated by the dense after sparse approach. Interestingly, the 3-to-1 sparse-to-dense... |
Structurally Sparsified Backward Propagation for Faster Long Short-Term Memory Training | 1806.00512 | Table 2: Performance of trained language models by dense after sparse method. Lower perplexity means better performance. | ['Training Method', 'Perplexity', 'Improvement'] | [['Dense', '93.101', '0'], ['Coarse-grained', '98.611', '-5.92%'], ['1/2 coarse + 1/2 dense', '88.675', '+4.75%'], ['3/4 coarse + 1/4 dense', '91.388', '+1.84%'], ['5/6 coarse + 1/6 dense', '99.47', '-6.84%'], ['Fine-grained', '96.410', '-3.55%'], ['1/2 fine + 1/2 dense', '88.607', '+4.83%'], ['3/4 fine + 1/4 dense', '... | The model is first trained with the coarse-grained method or the fine-grained method, and then trained with the regular dense training model. With 75% steps of sparse training and 25% steps of dense training, the resulted model achieves slightly better performance than the baseline model. The number of total training s... |
Structurally Sparsified Backward Propagation for Faster Long Short-Term Memory Training | 1806.00512 | Table 4: Performance of trained NMT models. Higher BLEU score means better performance. | ['Training Method', 'BLEU', 'Improvement'] | [['Dense', '20.32', '0'], ['Coarse-grained', '19.60', '-3.5%'], ['3/4 coarse + 1/4 dense', '20.30', '-0.1%'], ['5/6 coarse + 1/6 dense', '20.18', '-0.7%'], ['Fine-grained', '19.92', '-2.0%'], ['3/4 fine + 1/4 dense', '20.45', '+0.64%'], ['5/6 fine + 1/6 dense', '20.17', '-0.74%']] | The BLEU scores are the validation BLEU scores at the end of the training (after 600,000 steps). Although our sparsifying methods enforce 50% sparsity in the gate gradients, the resulting models achieve acceptable BLEU scores. Even compared to the dense SGD, the coarse-grain sparsifying only suffers a 3.5% BLEU score d... |
Multi-Domain Dialogue Acts and Response Co-Generation | 2004.12363 | Table 3: Results of different act generation methods, where BiLSTM, Word-CNN and Transformer are baselines from Chen et al. (2019). MarCo is our act generator trained jointly with the response generator and Transformer (GEN) is that without joint training. | ['Method', 'F1'] | [['BiLSTM', '71.4'], ['Word-CNN', '71.5'], ['Transformer', '73.1'], ['Transformer (GEN)', '73.2'], ['MarCo', '[BOLD] 73.9']] | To evaluate the performance of our act generator, we compare it with several baseline methods mentioned in Chen et al. We use MarCo to represent our act generator which is trained jointly with the response generator, and use Transformer (GEN) to denote our act generator without joint training. But after trained jointly... |
Multi-Domain Dialogue Acts and Response Co-Generation | 2004.12363 | Table 1: Overall results on the MultiWOZ 2.0 dataset. | ['Dialog Act', 'Model', 'Inform', 'Success', 'BLEU', 'Combined Score'] | [['Without Act', 'LSTM', '71.29', '60.96', '18.80', '84.93'], ['Without Act', 'Transformer', '71.10', '59.90', '19.10', '84.60'], ['Without Act', 'TokenMoE', '75.30', '59.70', '16.81', '84.31'], ['Without Act', 'Structured Fusion', '82.70', '72.10', '16.34', '93.74'], ['One-hot Act', 'SC-LSTM', '74.50', '62.50', '20.50... | From the table we can notice that our co-generation model (MarCo) outperforms all the baselines in Inform Rate, Request Success, and especially in combined score which is an overall metric. By comparing the two HDSA models, we can find HDSA derives its main performance from the external BERT, which can also be used to ... |
Unsupervised Pretraining for Sequence to Sequence Learning | 1611.02683 | Table 1: English→German performance on WMT test sets. Our pretrained model outperforms all other models. Note that the model without pretraining uses the LM objective. | ['[ITALIC] System', '[ITALIC] ensemble?', '[ITALIC] BLEU [ITALIC] newstest2014', '[ITALIC] BLEU [ITALIC] newstest2015'] | [['Phrase Based MT (Williams et\xa0al., 2016 )', '-', '21.9', '23.7'], ['Supervised NMT (Jean et\xa0al., 2015 )', 'single', '-', '22.4'], ['Edit Distance Transducer NMT (Stahlberg et\xa0al., 2016 )', 'single', '21.7', '24.1'], ['Edit Distance Transducer NMT (Stahlberg et\xa0al., 2016 )', 'ensemble 8', '22.9', '25.7... | Equally impressive is the fact that our best single model outperforms the previous state of the art ensemble of 4 models. Our ensemble of 5 models matches or exceeds the previous best ensemble of 12 models. |
An Interpretable Knowledge Transfer Model for Knowledge Base Completion | 1704.05908 | Table 5: Different λ’s effect on our model performance. The compared models are trained for 2000 epochs | ['[BOLD] Method', '[BOLD] WN18 MR', '[BOLD] WN18 H10', '[BOLD] FB15k MR', '[BOLD] FB15k H10'] | [['[ITALIC] λ=0.0003', '[BOLD] 217', '95.0', '[BOLD] 68', '80.4'], ['[ITALIC] λ=0.001', '223', '[BOLD] 95.2', '73', '80.6'], ['[ITALIC] λ=0.003', '239', '[BOLD] 95.2', '82', '[BOLD] 80.9']] | We compare how different value of λ would influence our model’s performance in Table. With large λ and higher domain sampling probability, our model’s Hits@10 increases while mean rank also increases. The rise of mean rank is due to higher probability of generating a valid triple as a negative sample causing the energy... |
An Interpretable Knowledge Transfer Model for Knowledge Base Completion | 1704.05908 | Table 2: Link prediction results on two datasets. Higher Hits@10 or lower Mean Rank indicates better performance. Following Nguyen et al. (2016b) and Shen et al. (2016), we divide the models into two groups. The first group contains intrinsic models without using extra information. The second group make use of addition... | ['[BOLD] Model', '[BOLD] Additional Information', '[BOLD] WN18 Mean Rank', '[BOLD] WN18 Hits@10', '[BOLD] FB15k Mean Rank', '[BOLD] FB15k Hits@10'] | [['SE Bordes et\xa0al. ( 2011 )', 'No', '985', '80.5', '162', '39.8'], ['Unstructured Bordes et\xa0al. ( 2014 )', 'No', '304', '38.2', '979', '6.3'], ['TransE (Bordes et\xa0al., 2013 )', 'No', '251', '89.2', '125', '47.1'], ['TransH (Wang et\xa0al., 2014 )', 'No', '303', '86.7', '87', '64.4'], ['TransR (Lin et\xa0al.... | The overall link prediction results Our model consistently outperforms previous models without external information on both the metrics of WN18 and FB15k. On WN18, we even achieve a much better mean rank with comparable Hits@10 than current state-of-the-art model IRN employing external information. |
An Interpretable Knowledge Transfer Model for Knowledge Base Completion | 1704.05908 | Table 3: Performance of model with dense attention vectors or sparse attention vectors. MR, H10 and Time denotes mean rank, Hits@10 and training time per epoch respectively | ['[BOLD] Method', '[BOLD] WN18 MR', '[BOLD] WN18 H10', '[BOLD] WN18 Time', '[BOLD] FB15k MR', '[BOLD] FB15k H10', '[BOLD] FB15k Time'] | [['Dense', '[BOLD] 199', '94.0', '4m34s', '69', '79.4', '4m30s'], ['Dense + ℓ1', '228', '[BOLD] 94.2', '4m25s', '131', '78.9', '5m47s'], ['Sparse', '207', '94.1', '[BOLD] 2m32s', '[BOLD] 67', '[BOLD] 79.6', '[BOLD] 1m52s']] | Generally, ITransF with sparse attention has slightly better or comparable performance comparing to dense attention. We see that ℓ1 regularization does not produce a sparse attention, especially on FB15k. |
An Interpretable Knowledge Transfer Model for Knowledge Base Completion | 1704.05908 | Table 4: Different methods to obtain sparse representations | ['[BOLD] Method', '[BOLD] WN18 MR', '[BOLD] WN18 H10', '[BOLD] FB15k MR', '[BOLD] FB15k H10'] | [['Sparse Encoding', '211', '86.6', '66', '79.1'], ['ITransF', '[BOLD] 205', '[BOLD] 94.2', '[BOLD] 65', '[BOLD] 81.0']] | On both benchmarks, ITransF achieves significant improvement against sparse encoding on pretrained model. This performance gap should be expected since the objective function of sparse encoding methods is to minimize the reconstruction loss rather than optimize the criterion for link prediction. |
AMUSED: A Multi-Stream Vector Representation Method for Use in Natural Dialogue | 1912.10160 | Table 3: Human based evaluation is conducted for 5 different components in the network as well as KV memory networks. AMUSED achieves the highest percent gain over specified baseline model. The scale is 1-10. | ['[BOLD] Model', '[BOLD] Coherence', '[BOLD] Context Aware', '[BOLD] Non Monotonicity', '[BOLD] Average Rating', '[BOLD] %gain'] | [['Bi-GRU & GCN only', '6.82', '7.35', '6.77', '6.98', 'Baseline'], ['BERT only', '7.61', '7.24', '6.33', '7.06', '1.14'], ['BERT with Bi-GRU & GCN', '7.54', '6.91', '7.38', '7.27', '4.15'], ['BERT and External KB', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]'], ['with Bi-GRU & GCN', '7.16', '7.34', '7.72', '7... | To monitor the effect of each neural component, we get it rated by experts either in isolation or in conjunction with other components. Such a study helps us understand the impact of different modules on a human based conversation. Dialogue system proposed by Zhang et al. |
AMUSED: A Multi-Stream Vector Representation Method for Use in Natural Dialogue | 1912.10160 | Table 2: Precision @1 comparison between different methods. Precision@1 % tell us the number of times the correct response from the dataset comes up. Details in Section 5.4.2 | ['[BOLD] Method', '[BOLD] Precision@1'] | [['Seq2Seq', '0.092'], ['Profile Memory', '0.092'], ['IR Baseline', '0.214'], ['AMUSED(Persona Chat)', '[BOLD] 0.326'], ['AMUSED(DSTC)', '[BOLD] 0.78']] | Accuracy on this binary classification problem has been used to select the best network. Furthermore, we perform ablation studies using different modules to understand the effect of each component in the network. A 4 layer neural network with ReLU activation in its hidden layers and softmax in the final layer is used a... |
First-Pass Large Vocabulary Continuous Speech Recognition using Bi-Directional Recurrent DNNs | 1408.2873 | Table 2: Train and test set character error rate (CER) results for a deep neural network (DNN) without recurrence, recurrent deep neural network with forward temporal connections (RDNN), and a bi-directional recurrent deep neural network (BRDNN). All models have 5 hidden layers. The DNN and RDNN both have 2,048 hidden ... | ['Model', 'Parameters (M)', 'Train CER', 'Test CER'] | [['DNN', '16.8', '3.8', '22.3'], ['RDNN', '22.0', '4.2', '13.5'], ['BRDNN', '20.9', '2.8', '10.7']] | Previous experiments with DNN-HMM systems found minimal benefits from recurrent connections in DNN acoustic models. It is natural to wonder whether recurrence, and especially bi-directional recurrence, is an essential aspect of our architecture. To evaluate the impact of recurrent connections we compare the train and t... |
First-Pass Large Vocabulary Continuous Speech Recognition using Bi-Directional Recurrent DNNs | 1408.2873 | Table 1: Word error rate (WER) and character error rate (CER) results from a BDRNN trained with the CTC loss function. As a baseline (No LM) we decode by choosing the most likely label at each timestep and performing standard collapsing as done in CTC training. We compare this baseline against our modified prefix-searc... | ['Model', 'CER', 'WER'] | [['No LM', '10.0', '35.8'], ['Dictionary LM', '8.5', '24.4'], ['Bigram LM', '5.7', '14.1']] | We trained a BRDNN with 5 hidden layers, all with 1824 hidden units, for a total of 20.9M free parameters. The third hidden layer of the network has recurrent connections. We use the Nesterov accelerated gradient optimization algorithm as described in Sutskever et al. After each full pass through the training set we di... |
Incorporating Interlocutor-Aware Context into Response Generation on Multi-Party Chatbots | 1910.13106 | Table 6: Performances over different memory types. | ['[BOLD] Memory Type', '[BOLD] Referenced', '[BOLD] Referenced', '[BOLD] Unreferenced', '[BOLD] Unreferenced'] | [['[BOLD] Memory Type', '[BOLD] BLEU', '[BOLD] ROUGE', '[BOLD] Length', '[BOLD] #Noun'], ['[BOLD] addressee memory', '[BOLD] 10.63', '8.73', '11.34', '[BOLD] 1.68'], ['all utterance memory', '10.39', '[BOLD] 8.78', '[BOLD] 11.38', '1.37'], ['latest memory', '10.43', '8.40', '10.16', '1.28'], ['speaker memory', '10.03',... | Comparative Results. It can see that our method, the addressee memory, achieves the best or near-best performances on all metrics. Although memorizing all utterances is competitive, the complexity of all utterance memory is n times compared with the one in the addressee memory, where n is the number of utterances in a ... |
Incorporating Interlocutor-Aware Context into Response Generation on Multi-Party Chatbots | 1910.13106 | Table 4: Performances on sparse and plentiful learning data with different numbers of interlocutor’s dialogue turns, where the test data is divided into different intervals according to the number of dialogue turns in training dataset said by target addressee (named as interlocutor’s dialogue turns). | ['[BOLD] Interlocutor’s', '[BOLD] Persona Model', '[BOLD] Persona Model', '[BOLD] ICRED (ours)', '[BOLD] ICRED (ours)'] | [['[BOLD] Dialogue Turns', '[BOLD] \xa0BLEU', '[BOLD] ROUGE', '[BOLD] BLEU', '[BOLD] ROUGE'], ['[0, 100]', '8.47', '6.72', '10.63', '8.60'], ['(100, 1000]', '8.87', '7.14', '10.50', '8.61'], ['(1000, 5000]', '9.48', '7.74', '[BOLD] 10.77', '[BOLD] 8.90'], ['(5000, +∞)', '[BOLD] 9.51', '[BOLD] 7.80', '10.60', '8.79']] | Comparative Results. We can clearly see that the persona model has a sparsity issue: it performs very poorly on sparse learning data (e.g., BLEU score = 8.47 on “[0, 100]”) while it achieves good performances on plentiful learning data (e.g., BLEU score = 9.51 on “(5000, +∞)”), which demonstrates that the fixed person ... |
Incorporating Interlocutor-Aware Context into Response Generation on Multi-Party Chatbots | 1910.13106 | Table 5: Ablation Experiments by removing the main components. | ['[BOLD] Model', '[BOLD] Referenced', '[BOLD] Referenced', '[BOLD] Unreferenced', '[BOLD] Unreferenced'] | [['[BOLD] Model', '[BOLD] BLEU', '[BOLD] ROUGE', '[BOLD] Length', '[BOLD] #Noun'], ['[BOLD] ICRED', '[BOLD] 10.63', '[BOLD] 8.73', '[BOLD] 11.34', '[BOLD] 1.68'], ['w/o Adr_Mem', '10.25', '8.23', '10.73', '1.27'], ['w/o Ctx_Spk_Vec', '10.13', '8.22', '10.86', '1.59'], ['w/o Ctx_Adr_Vec', '9.95', '8.18', '10.93', '1.26'... | Comparative Results. We can see that removing any component causes obvious performance degradation. In particular, “w/o Ctx_Adr_Vec” performs the worst on almost all of the metrics, which demonstrates the importance of contextual information for the target addressee. |
Cross-Lingual Machine Reading Comprehension | 1909.00361 | Table 3: Zero-shot cross-lingual machine reading comprehension results on Japanese and French SQuAD data. † are extracted in Asai et al. (2018). | ['[EMPTY]', '[BOLD] Japanese [BOLD] EM', '[BOLD] Japanese [BOLD] F1', '[BOLD] French [BOLD] EM', '[BOLD] French [BOLD] F1'] | [['Back-Translation†', '24.8', '42.6', '23.5', '44.0'], ['+Runtime MT†', '37.0', '52.2', '40.7', '61.9'], ['GNMT+BERT [ITALIC] Len', '26.9', '46.2', '39.1', '67.0'], ['+SimpleMatch', '37.3', '58.0', '47.4', '71.5'], ['BERT [ITALIC] SQ− [ITALIC] Bmul', '61.3', '73.4', '57.6', '77.1']] | In this paper, we propose a simple but effective approach called SimpleMatch to align translated answer to original passage span. While one may argue that using neural machine translation attention to project source answer to original target passage span is ideal as used in Asai et al. However, to extract attention val... |
Cross-Lingual Machine Reading Comprehension | 1909.00361 | Table 2: Experimental results on CMRC 2018 and DRCD. † indicates unpublished works (some of the systems are using development set for training, which makes the results not directly comparable.). ♠ indicates zero-shot approach. We mark our system with an ID in the first column for reference simplicity. | ['[BOLD] #', '[BOLD] System', '[BOLD] CMRC 2018 [BOLD] Dev', '[BOLD] CMRC 2018 [BOLD] Dev', '[BOLD] CMRC 2018 [BOLD] Test', '[BOLD] CMRC 2018 [BOLD] Test', '[BOLD] CMRC 2018 [BOLD] Challenge', '[BOLD] CMRC 2018 [BOLD] Challenge', '[BOLD] DRCD [BOLD] Dev', '[BOLD] DRCD [BOLD] Dev', '[BOLD] DRCD [BOLD] Test', '[... | [['[BOLD] #', '[BOLD] System', '[BOLD] EM', '[BOLD] F1', '[BOLD] EM', '[BOLD] F1', '[BOLD] EM', '[BOLD] F1', '[BOLD] EM', '[BOLD] F1', '[BOLD] EM', '[BOLD] F1'], ['[EMPTY]', '[ITALIC] Human Performance', '[ITALIC] 91.1', '[ITALIC] 97.3', '[ITALIC] 92.4', '[ITALIC] 97.9', '[ITALIC] 90.4', '[ITALIC] 95.2', '-', '-', '[IT... | As we can see that, without using any alignment approach, the zero-shot results are quite lower regardless of using English BERT-base (#1) or BERT-large (#2). When we apply SimpleMatch (#3), we observe significant improvements demonstrating its effectiveness. The Answer Aligner (#4) could further improve the performanc... |
On NMT Search Errors and Model Errors: Cat Got Your Tongue? | 1908.10090 | Table 4: Length normalization fixes translation lengths, but prevents exact search from matching the BLEU score of Beam-10. Experiment conducted on 48.3% of the test set. | ['[BOLD] Search', '[BOLD] W/o length norm. [BOLD] BLEU', '[BOLD] W/o length norm. [BOLD] Ratio', '[BOLD] With length norm. [BOLD] BLEU', '[BOLD] With length norm. [BOLD] Ratio'] | [['Beam-10', '37.0', '1.00', '36.3', '1.03'], ['Beam-30', '36.7', '0.98', '36.3', '1.04'], ['Exact', '27.2', '0.74', '36.4', '1.03']] | We can find the global best translations under length normalization by generalizing our exact inference scheme to length dependent lower bounds γk. (e.g. zero to 1.2 times the source sentence length). (4) Exact search under length normalization does not suffer from the length deficiency anymore (last row in Tab. but it... |
On NMT Search Errors and Model Errors: Cat Got Your Tongue? | 1908.10090 | Table 1: NMT with exact inference. In the absence of search errors, NMT often prefers the empty translation, causing a dramatic drop in length ratio and BLEU. | ['[BOLD] Search', '[BOLD] BLEU', '[BOLD] Ratio', '[BOLD] #Search errors', '[BOLD] #Empty'] | [['Greedy', '29.3', '1.02', '73.6%', '0.0%'], ['Beam-10', '30.3', '1.00', '57.7%', '0.0%'], ['Exact', '2.1', '0.06', '0.0%', '51.8%']] | Our main result is shown in Tab. > token. Fig. Large beam sizes reduce the number of search errors, but the BLEU score drops because translations are too short. Even a large beam size of 100 produces 53.62% search errors. Fig. For example, Beam-10 yields 15.9% fewer search errors (absolute) than greedy decoding (57.68%... |
Adversarial Multi-task Learning for Text Classification | 1704.05742 | Table 3: Error rates of our models on 16 datasets against vanilla multi-task learning. ϕ (Books) means that we transfer the knowledge of the other 15 tasks to the target task Books. | ['[BOLD] Source Tasks', '[BOLD] Single Task LSTM', '[BOLD] Single Task BiLSTM', '[BOLD] Single Task sLSTM', '[BOLD] Single Task Avg.', '[BOLD] Transfer Models SP-MTL-SC', '[BOLD] Transfer Models SP-MTL-BC', '[BOLD] Transfer Models ASP-MTL-SC', '[BOLD] Transfer Models ASP-MTL-BC'] | [['[ITALIC] ϕ (Books)', '20.5', '19.0', '18.0', '19.2', '17.8(−1.4)', '16.3(−2.9)', '16.8(−2.4)', '16.3(−2.9)'], ['[ITALIC] ϕ (Electronics)', '19.5', '21.5', '23.3', '21.4', '15.3(−6.1)', '14.8(−6.6)', '17.8(−3.6)', '16.8(−4.6)'], ['[ITALIC] ϕ (DVD)', '18.3', '19.5', '22.0', '19.9', '14.8(−5.1)', '15.5(−4.4)', '14.5(−5... | , we can see the shared layer from ASP-MTL achieves a better performance compared with SP-MTL. Besides, for the two kinds of transfer strategies, the Bi-Channel model performs better. The reason is that the task-specific layer introduced in the Bi-Channel model can store some private features. Overall, the results indi... |
Adversarial Multi-task Learning for Text Classification | 1704.05742 | Table 2: Error rates of our models on 16 datasets against typical baselines. The numbers in brackets represent the improvements relative to the average performance (Avg.) of three single task baselines. | ['[BOLD] Task', '[BOLD] Single Task LSTM', '[BOLD] Single Task BiLSTM', '[BOLD] Single Task sLSTM', '[BOLD] Single Task Avg.', '[BOLD] Multiple Tasks MT-DNN', '[BOLD] Multiple Tasks MT-CNN', '[BOLD] Multiple Tasks FS-MTL', '[BOLD] Multiple Tasks SP-MTL', '[BOLD] Multiple Tasks ASP-MTL'] | [['Books', '20.5', '19.0', '18.0', '19.2', '17.8(−1.4)', '15.5(−3.7)', '17.5(−1.7)', '18.8(−0.4)', '16.0(−3.2)'], ['Electronics', '19.5', '21.5', '23.3', '21.4', '18.3(−3.1)', '16.8(−4.6)', '14.3(−7.1)', '15.3(−6.1)', '13.2(−8.2)'], ['DVD', '18.3', '19.5', '22.0', '19.9', '15.8(−4.1)', '16.0(−3.9)', '16.5(−3.4)', '16.0... | The column of “Single Task” shows the results of vanilla LSTM, bidirectional LSTM (BiLSTM), stacked LSTM (sLSTM) and the average error rates of previous three models. The column of “Multiple Tasks” shows the results achieved by corresponding multi-task models. From this table, we can see that the performance of most ta... |
Reciprocal Attention Fusion for Visual Question Answering | 1805.04247 | Table 3: Ablation Study on VQAv2 val-set. | ['Cat.', 'Methods', 'Val-set'] | [['I', 'RAF-I(ResNet)', '53.9'], ['[EMPTY]', 'HieCoAtt Lu et\xa0al. ( 2016 ); Goyal et\xa0al. ( 2016 )', '54.6'], ['[EMPTY]', 'RAF-I(ResNeXt)', '58.0'], ['[EMPTY]', 'MCB Fukui et\xa0al. ( 2016 ); Goyal et\xa0al. ( 2016 )', '59.1'], ['[EMPTY]', 'MUTAN Ben-Younes et\xa0al. ( 2017 )', '60.1'], ['II', 'Up-DownAnderson et\x... | We perform an extensive ablation study of the proposed model on VQAv2 Goyal et al. This ablation study helps to better understand the contribution of different components of our model towards the overall performance on the VQA task. The objective of this ablation study is to show that when the language features are com... |
Reciprocal Attention Fusion for Visual Question Answering | 1805.04247 | Table 1: Comparison of the state-of-the-art methods with our single model performance on VQAv1.0 test-dev and test-standard server. | ['Methods', 'Test-dev Y/N', 'Test-dev No.', 'Test-dev Other', 'Test-dev All', 'Test-standard Y/N', 'Test-standard No.', 'Test-standard Other', 'Test-standard All'] | [['RAF (Ours)', '[BOLD] 85.9', '[BOLD] 41.3', '[BOLD] 58.7', '[BOLD] 68.0', '[BOLD] 85.8', '41.4', '58.9', '[BOLD] 68.2'], ['ReasonNetIlievski and Feng ( 2017 )', '-', '-', '-', '-', '84.0', '38.7', '[BOLD] 60.4', '67.9'], ['MFB+CoAtt+Glove Yu et\xa0al. ( 2018 )', '85.0', '39.7', '57.4', '66.8', '85.0', '39.5', '57.4',... | Remarkably, our model outperforms all other models in the overall accuracy. We report a significant performance boost of 1.2% on the test-dev set and 0.3% on the test-standard set. It is to be noted that using multiple ensembles and data augmentation with complementary training in Visual Genome QA pairs can increase th... |
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