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D16-1007table_2 | Comparison of different position features. | 2 | [['Position Feature', 'plain text PF'], ['Position Feature', 'TPF1'], ['Position Feature', 'TPF2']] | 1 | [['F1']] | [['83.21'], ['83.99'], ['83.90']] | column | ['F1'] | ['Position Feature'] | <table border='1' class='dataframe'> <thead> <tr style='text-align: right;'> <th></th> <th>F1</th> </tr> </thead> <tbody> <tr> <td>Position Feature || plain text PF</td> <td>83.21</td> </tr> <tr> <td>Position Feature || TPF1</td> <td>83.99</td> </tr> <tr> <td>P... | Table 2 | table_2 | D16-1007 | 8 | emnlp2016 | Table 2 summarizes the performances of proposed model when different position features are exploited. To concentrate on studying the effect of position features, we do not involve lexical features in this section. As the table shows, the position feature on plain text is still effective in our model and we accredit its... | [1, 2, 1, 1, 1, 2, 2, 0, 0] | ['Table 2 summarizes the performances of proposed model when different position features are exploited.', 'To concentrate on studying the effect of position features, we do not involve lexical features in this section.', 'As the table shows, the position feature on plain text is still effective in our model and we accr... | [None, None, ['plain text PF', 'TPF1', 'TPF2'], ['TPF1', 'TPF2'], ['TPF1', 'TPF2'], None, ['TPF1', 'TPF2'], None, None] | 1 |
D16-1010table_3 | Pearson correlation values between human and model preferences for each construction and the verb-bias score; training on raw frequencies and 2 constructions. All correlations significant with p-value < 0.001, except the one value with *. Best result for each row is marked in boldface. | 1 | [['DO'], ['PD'], ['DO-PD']] | 2 | [['AB (Connectionist)', '-'], ['BFS (Bayesian)', 'Level 1'], ['BFS (Bayesian)', 'Level 2']] | [['0.06*', '0.23', '0.25'], ['0.33', '0.38', '0.32'], ['0.39', '0.53', '0.59']] | column | ['correlation', 'correlation', 'correlation'] | ['AB (Connectionist)', 'BFS (Bayesian)'] | <table border='1' class='dataframe'> <thead> <tr style='text-align: right;'> <th></th> <th>AB (Connectionist) || -</th> <th>BFS (Bayesian) || Level 1</th> <th>BFS (Bayesian) || Level 2</th> </tr> </thead> <tbody> <tr> <td>DO</td> <td>[0.06]</td> <td>0.23</td> <td>0.25... | Table 3 | table_3 | D16-1010 | 8 | emnlp2016 | Table 3 presents the correlation results for the two models’ preferences for each construction and the verb bias score. The AB model does not correlate with the judgments for the DO. However, the model produces significant positive correlations with the PD judgments and with the verb bias score. The BFS model, on the... | [1, 1, 1, 1, 1] | ['Table 3 presents the correlation results for the two models’ preferences for each construction and the verb bias score.', 'The AB model does not correlate with the judgments for the DO.', 'However, the model produces significant positive correlations with the PD judgments and with the verb bias score.', 'The BFS mo... | [['AB (Connectionist)', 'BFS (Bayesian)'], ['AB (Connectionist)', 'DO'], ['AB (Connectionist)', 'PD'], ['DO', 'PD', 'DO-PD', 'AB (Connectionist)', 'BFS (Bayesian)'], ['Level 2', 'BFS (Bayesian)']] | 1 |
D16-1011table_4 | Comparison between rationale models (middle and bottom rows) and the baselines using full title or body (top row). | 1 | [['Full title'], ['Full body'], ['Independent'], ['Independent'], ['Dependent'], ['Dependent']] | 1 | [['MAP (dev)'], ['MAP (test)'], ['% words']] | [['56.5', '60.0', '10.1'], ['54.2', '53.0', '89.9'], ['55.7', '53.6', '9.7'], ['56.3', '52.6', '19.7'], ['56.1', '54.6', '11.6'], ['56.5', '55.6', '32.8']] | column | ['MAP (dev)', 'MAP (test)', '% words'] | ['Independent', 'Dependent'] | <table border='1' class='dataframe'> <thead> <tr style='text-align: right;'> <th></th> <th>MAP (dev)</th> <th>MAP (test)</th> <th>% words</th> </tr> </thead> <tbody> <tr> <td>Full title</td> <td>56.5</td> <td>60.0</td> <td>10.1</td> </tr> <tr> <td>Full body... | Table 4 | table_4 | D16-1011 | 8 | emnlp2016 | Results. Table 4 presents the results of our rationale model. We explore a range of hyper-parameter values. We include two runs for each version. The first one achieves the highest MAP on the development set, The second run is selected to compare the models when they use roughly 10% of question text (7 words on average... | [2, 1, 2, 2, 1, 2, 1, 1] | ['Results.', 'Table 4 presents the results of our rationale model.', 'We explore a range of hyper-parameter values.', 'We include two runs for each version.', 'The first one achieves the highest MAP on the development set, The second run is selected to compare the models when they use roughly 10% of question text (7 wo... | [None, None, None, None, ['Independent', 'Dependent', 'MAP (dev)'], None, ['Dependent', 'MAP (dev)', 'Full title'], ['Independent', 'Dependent', 'Full title', 'Full body']] | 1 |
D16-1018table_2 | Spearman’s rank correlation results on the SCWS dataset | 4 | [['Model', 'Huang', 'Similarity Metrics', 'AvgSim'], ['Model', 'Huang', 'Similarity Metrics', 'AvgSimC'], ['Model', 'Chen', 'Similarity Metrics', 'AvgSim'], ['Model', 'Chen', 'Similarity Metrics', 'AvgSimC'], ['Model', 'Neelakantan', 'Similarity Metrics', 'AvgSim'], ['Model', 'Neelakantan', 'Similarity Metrics', 'AvgSi... | 1 | [['ρ × 100']] | [['62.8'], ['65.7'], ['66.2'], ['68.9'], ['67.2'], ['69.2'], ['69.7'], ['63.6'], ['65.4'], ['61.2'], ['64.3'], ['65.6'], ['64.9'], ['66.1']] | column | ['correlation'] | ['Ours + CBOW', 'Ours + Skip-gram'] | <table border='1' class='dataframe'> <thead> <tr style='text-align: right;'> <th></th> <th>ρ × 100</th> </tr> </thead> <tbody> <tr> <td>Model || Huang || Similarity Metrics || AvgSim</td> <td>62.8</td> </tr> <tr> <td>Model || Huang || Similarity Metrics || AvgSimC</td> <t... | Table 2 | table_2 | D16-1018 | 7 | emnlp2016 | Table 2 shows the results of our contextdependent sense embedding models on the SCWS dataset. In this table, ρ refers to the Spearman’s rank correlation and a higher value of ρ indicates better performance. The baseline performances are from Huang et al. (2012), Chen et al. (2014), Neelakantan et al. (2014), Li and Jur... | [1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2] | ['Table 2 shows the results of our contextdependent sense embedding models on the SCWS dataset.', 'In this table, ρ refers to the Spearman’s rank correlation and a higher value of ρ indicates better performance.', 'The baseline performances are from Huang et al. (2012), Chen et al. (2014), Neelakantan et al. (2014), Li... | [None, None, ['Huang', 'Chen', 'Neelakantan', 'Li', 'Tian', 'Bartunov'], ['Ours + CBOW', 'Ours + Skip-gram'], None, ['AvgSim', 'AvgSimC'], ['Model_M', 'Model_W', 'HardSim', 'SoftSim'], ['Model'], ['Model'], ['Model'], None, None, None, None] | 1 |
D16-1021table_4 | Examples of attention weights in different hops for aspect level sentiment classification. The model only uses content attention. The hop columns show the weights of context words in each hop, indicated by values and gray color. This example shows the results of sentence “great food but the service was dreadful!” with ... | 1 | [['great'], ['food'], ['but'], ['the'], ['was'], ['dreadful'], ['!']] | 2 | [['hop 1', 'service'], ['hop 1', 'food'], ['hop 2', 'service'], ['hop 2', 'food'], ['hop 3', 'service'], ['hop 3', 'food'], ['hop 4', 'service'], ['hop 4', 'food'], ['hop 5', 'service'], ['hop 5', 'food']] | [['0.20', '0.22', '0.15', '0.12', '0.14', '0.14', '0.13', '0.12', '0.23', '0.20'], ['0.11', '0.21', '0.07', '0.11', '0.08', '0.10', '0.12', '0.11', '0.06', '0.12'], ['0.20', '0.03', '0.10', '0.11', '0.10', '0.08', '0.12', '0.11', '0.13', '0.06'], ['0.03', '0.11', '0.07', '0.11', '0.08', '0.08', '0.12', '0.11', '0.06', ... | column | ['weights', 'weights', 'weights', 'weights', 'weights', 'weights', 'weights', 'weights', 'weights', 'weights'] | ['service', 'food'] | <table border='1' class='dataframe'> <thead> <tr style='text-align: right;'> <th></th> <th>hop 1 || service</th> <th>hop 1 || food</th> <th>hop 2 || service</th> <th>hop 2 || food</th> <th>hop 3 || service</th> <th>hop 3 || food</th> <th>hop 4 || service</th> <th>hop 4 |... | Table 4 | table_4 | D16-1021 | 7 | emnlp2016 | From Table 4, we can find that in the first hop the context words “great”, “but” and “dreadful” contribute equally to the aspect “service”. While after the second hop, the weight of “dreadful” increases and finally the model correctly predict the polarity towards “service” as negative. This case shows the effects of mu... | [1, 1, 1, 1, 1, 2] | ['From Table 4, we can find that in the first hop the context words “great”, “but” and “dreadful” contribute equally to the aspect “service”.', 'While after the second hop, the weight of “dreadful” increases and finally the model correctly predict the polarity towards “service” as negative.', 'This case shows the effec... | [['great', 'but', 'dreadful', 'service'], None, ['dreadful', 'service'], ['dreadful', 'food'], ['food'], None] | 1 |
D16-1025table_2 | Overall results on the HE Set: BLEU, computed against the original reference translation, and TER, computed with respect to the targeted post-edit (HTER) and multiple postedits (mTER). | 2 | [['system', 'PBSY'], ['system', 'HPB'], ['system', 'SPB'], ['system', 'NMT']] | 1 | [['BLEU'], ['HTER'], ['mTER']] | [['25.3', '28.0', '21.8'], ['24.6', '29.9', '23.4'], ['25.8', '29.0', '22.7'], ['31.1*', '21.1*', '16.2*']] | column | ['BLEU', 'HTER', 'mTER'] | ['NMT'] | <table border='1' class='dataframe'> <thead> <tr style='text-align: right;'> <th></th> <th>BLEU</th> <th>HTER</th> <th>mTER</th> </tr> </thead> <tbody> <tr> <td>system || PBSY</td> <td>25.3</td> <td>28.0</td> <td>21.8</td> </tr> <tr> <td>system || HPB</td> ... | Table 2 | table_2 | D16-1025 | 4 | emnlp2016 | 4 Overall Translation Quality. Table 2 presents overall system results according to HTER and mTER, as well as BLEU computed against the original TED Talks reference translation. We can see that NMT clearly outperforms all other approaches both in terms of BLEU and TER scores. Focusing on mTER results, the gain obtained... | [2, 1, 1, 1, 1, 2, 0, 0] | ['4 Overall Translation Quality.', 'Table 2 presents overall system results according to HTER and mTER, as well as BLEU computed against the original TED Talks reference translation.', 'We can see that NMT clearly outperforms all other approaches both in terms of BLEU and TER scores.', 'Focusing on mTER results, the ga... | [None, ['system', 'HTER', 'mTER', 'BLEU'], ['NMT', 'BLEU', 'HTER', 'mTER'], ['mTER', 'NMT', 'PBSY'], ['mTER', 'HTER'], ['HTER', 'mTER'], None, None] | 1 |
D16-1025table_4 | Word reordering evaluation in terms of shift operations in HTER calculation and of KRS. For each system, the number of generated words, the number of shift errors and their corresponding percentages are reported. | 2 | [['system', 'PBSY'], ['system', 'HPB'], ['system', 'SPB'], ['system', 'NMT']] | 1 | [['#words'], ['#shifts'], ['%shifts'], ['KRS']] | [['11517', '354', '3.1', '84.6'], ['11417', '415', '3.6', '84.3'], ['11420', '398', '3.5', '84.5'], ['11284', '173', '1.5*', '88.3*']] | column | ['#words', '#shifts', '%shifts', 'KRS'] | ['NMT'] | <table border='1' class='dataframe'> <thead> <tr style='text-align: right;'> <th></th> <th>#words</th> <th>#shifts</th> <th>%shifts</th> <th>KRS</th> </tr> </thead> <tbody> <tr> <td>system || PBSY</td> <td>11517</td> <td>354</td> <td>3.1</td> <td>84.6</td> ... | Table 4 | table_4 | D16-1025 | 7 | emnlp2016 | 5.3 Word order errors. To analyse reordering errors, we start by focusing on shift operations identified by the HTER metrics. The first three columns of Table 4 show, respectively: (i) the number of words generated by each system (ii) the number of shifts required to align each system output to the corresponding post-e... | [2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 1, 1] | ['5.3 Word order errors.', 'To analyse reordering errors, we start by focusing on shift operations identified by the HTER metrics.', 'The first three columns of Table 4 show, respectively: (i) the number of words generated by each system (ii) the number of shifts required to align each system output to the correspondin... | [None, None, ['#words', '#shifts', '%shifts'], ['NMT', 'system', '#shifts', '%shifts'], ['NMT', 'PBSY'], None, None, ['KRS'], ['KRS'], ['KRS'], ['KRS', 'NMT'], ['KRS', 'NMT', 'system']] | 1 |
D16-1032table_2 | Human evaluation results on the generated and true recipes. Scores range in [1, 5]. | 2 | [['Model', 'Attention'], ['Model', 'EncDec'], ['Model', 'NN'], ['Model', 'NN-Swap'], ['Model', 'Checklist'], ['Model', 'Checklist+'], ['Model', 'Truth']] | 1 | [['Syntax'], ['Ingredient use'], ['Follows goal']] | [['4.47', '3.02', '3.47'], ['4.58', '3.29', '3.61'], ['4.22', '3.02', '3.36'], ['4.11', '3.51', '3.78'], ['4.58', '3.80', '3.94'], ['4.39', '3.95', '4.10'], ['4.39', '4.03', '4.34']] | column | ['Syntax', 'Ingridient use', 'Follows goal'] | ['Checklist', 'Checklist+'] | <table border='1' class='dataframe'> <thead> <tr style='text-align: right;'> <th></th> <th>Syntax</th> <th>Ingredient use</th> <th>Follows goal</th> </tr> </thead> <tbody> <tr> <td>Model || Attention</td> <td>4.47</td> <td>3.02</td> <td>3.47</td> </tr> <tr> ... | Table 2 | table_2 | D16-1032 | 8 | emnlp2016 | Table 2 shows the averaged scores over the responses. The checklist models outperform all baselines in generating recipes that follow the provided agenda closely and accomplish the desired goal, where NN in particular often generates the wrong dish. Perhaps surprisingly, both the Attention and EncDec baselines and the ... | [1, 1, 1, 2] | ['Table 2 shows the averaged scores over the responses.', 'The checklist models outperform all baselines in generating recipes that follow the provided agenda closely and accomplish the desired goal, where NN in particular often generates the wrong dish.', 'Perhaps surprisingly, both the Attention and EncDec baselines ... | [None, ['Checklist', 'Checklist+', 'NN', 'Model'], ['Attention', 'EncDec', 'Checklist'], None] | 1 |
D16-1035table_4 | Performance comparison with other state-of-the-art systems on RST-DT. | 2 | [['System', 'Joty et al. (2013)'], ['System', 'Ji and Eisenstein. (2014)'], ['System', 'Feng and Hirst. (2014)'], ['System', 'Li et al. (2014a)'], ['System', 'Li et al. (2014b)'], ['System', 'Heilman and Sagae. (2015)'], ['System', 'Ours'], ['System', 'Human']] | 1 | [['S'], ['N'], ['R']] | [['82.7', '68.4', '55.7'], ['82.1', '71.1', '61.6'], ['85.7', '71.0', '58.2'], ['84.0', '70.8', '58.6'], ['83.4', '73.8', '57.8'], ['83.5', '68.1', '55.1'], ['85.8', '71.1', '58.9'], ['88.7', '77.7', '65.8']] | column | ['S', 'N', 'R'] | ['Ours'] | <table border='1' class='dataframe'> <thead> <tr style='text-align: right;'> <th></th> <th>S</th> <th>N</th> <th>R</th> </tr> </thead> <tbody> <tr> <td>System || Joty et al. (2013)</td> <td>82.7</td> <td>68.4</td> <td>55.7</td> </tr> <tr> <td>System || Ji a... | Table 4 | table_4 | D16-1035 | 8 | emnlp2016 | Table 4 shows the performance for our system and those systems. Our system achieves the best result in span and relatively lower performance in nucleus and relation identification comparing with the corresponding best results but still better than most systems. No system achieves the best result on all three metrics. T... | [1, 1, 1, 0, 0] | ['Table 4 shows the performance for our system and those systems.', 'Our system achieves the best result in span and relatively lower performance in nucleus and relation identification comparing with the corresponding best results but still better than most systems.', 'No system achieves the best result on all three me... | [None, ['Ours', 'System'], ['System', 'S', 'N', 'R'], None, None] | 1 |
D16-1038table_7 | Domain Transfer Results. We conduct the evaluation on TAC-KBP corpus with the split of newswire (NW) and discussion form (DF) documents. Here, we choose MSEP-EMD and MSEP-CorefESA+AUG+KNOW as the MSEP approach for event detection and co-reference respectively. We use SSED and SupervisedBase as the supervised modules fo... | 3 | [['Event Detection', 'In Domain', 'Train NW Test NW'], ['Event Detection', 'Out of Domain', 'Train DF Test NW'], ['Event Detection', 'In Domain', 'Train DF Test DF'], ['Event Detection', 'Out of Domain', 'Train NW Test DF'], ['Event Co-reference', 'In Domain', 'Train NW Test NW'], ['Event Co-reference', 'Out of Domain'... | 1 | [['MSEP'], ['Supervised']] | [['58.5', '63.7'], ['55.1', '54.8'], ['57.9', '62.6'], ['52.8', '52.3'], ['73.2', '73.6'], ['71', '70.1'], ['68.6', '68.9'], ['67.9', '67']] | column | ['F1', 'F1'] | ['MSEP'] | <table border='1' class='dataframe'> <thead> <tr style='text-align: right;'> <th></th> <th>MSEP</th> <th>Supervised</th> </tr> </thead> <tbody> <tr> <td>Event Detection || In Domain || Train NW Test NW</td> <td>58.5</td> <td>63.7</td> </tr> <tr> <td>Event Detection |... | Table 7 | table_7 | D16-1038 | 9 | emnlp2016 | 4.7 Domain Transfer Evaluation. To demonstrate the superiority of the adaptation capabilities of the proposed MSEP system, we test its performance on new domains and compare with the supervised system. TAC-KBP corpus contains two genres: newswire (NW) and discussion forum (DF), and they have roughly equal number of doc... | [2, 2, 2, 1, 1, 1, 2] | ['4.7 Domain Transfer Evaluation.', 'To demonstrate the superiority of the adaptation capabilities of the proposed MSEP system, we test its performance on new domains and compare with the supervised system.', 'TAC-KBP corpus contains two genres: newswire (NW) and discussion forum (DF), and they have roughly equal numbe... | [None, ['MSEP', 'Supervised'], None, ['Train NW Test DF'], ['MSEP'], ['MSEP', 'Supervised', 'Out of Domain', 'Event Detection', 'Event Co-reference'], None] | 1 |
D16-1039table_2 | Performance results for the BLESS and ENTAILMENT datasets. | 4 | [['Model', 'SVM+Yu', 'Dataset', 'BLESS'], ['Model', 'SVM+Word2Vecshort', 'Dataset', 'BLESS'], ['Model', 'SVM+Word2Vec', 'Dataset', 'BLESS'], ['Model', 'SVM+Ourshort', 'Dataset', 'BLESS'], ['Model', 'SVM+Our', 'Dataset', 'BLESS'], ['Model', 'SVM+Yu', 'Dataset', 'ENTAIL'], ['Model', 'SVM+Word2Vecshort', 'Dataset', 'ENTAI... | 1 | [['Accuracy']] | [['90.4%'], ['83.8%'], ['84.0%'], ['91.1%'], ['93.6%'], ['87.5%'], ['82.8%'], ['83.3%'], ['88.2%'], ['91.7%']] | column | ['accuracy'] | ['SVM+Our'] | <table border='1' class='dataframe'> <thead> <tr style='text-align: right;'> <th></th> <th>Accuracy</th> </tr> </thead> <tbody> <tr> <td>Model || SVM+Yu || Dataset || BLESS</td> <td>90.4%</td> </tr> <tr> <td>Model || SVM+Word2Vecshort || Dataset || BLESS</td> <td>83.8%</t... | Table 2 | table_2 | D16-1039 | 7 | emnlp2016 | Table 2 shows the performance of the three supervised models in Experiment 1. Our approach achieves significantly better performance than Yu’s method and Word2Vec method in terms of accuracy (t-test, p-value < 0.05) for both BLESS and ENTAILMENT datasets. Specifically, our approach improves the average accuracy by 4% c... | [1, 1, 1, 1, 1, 2, 1, 2, 2] | ['Table 2 shows the performance of the three supervised models in Experiment 1.', 'Our approach achieves significantly better performance than Yu’s method and Word2Vec method in terms of accuracy (t-test, p-value < 0.05) for both BLESS and ENTAILMENT datasets.', 'Specifically, our approach improves the average accuracy... | [None, ['SVM+Ourshort', 'SVM+Our', 'BLESS', 'ENTAIL', 'Accuracy'], ['SVM+Ourshort', 'SVM+Our', 'SVM+Yu', 'SVM+Word2Vecshort', 'SVM+Word2Vec'], ['SVM+Word2Vecshort', 'SVM+Word2Vec'], ['SVM+Ourshort', 'SVM+Our', 'SVM+Yu'], ['SVM+Yu'], ['SVM+Ourshort', 'SVM+Our'], ['SVM+Word2Vecshort', 'SVM+Word2Vec'], ['SVM+Word2Vecshort... | 1 |
D16-1039table_3 | Performance results for the general domain datasets when using one domain for training and another domain for testing. | 6 | [['Model', 'SVM+Yu', 'Training', 'BLESS', 'Testing', 'ENTAIL'], ['Model', 'SVM+Word2Vecshort', 'Training', 'BLESS', 'Testing', 'ENTAIL'], ['Model', 'SVM+Word2Vec', 'Training', 'BLESS', 'Testing', 'ENTAIL'], ['Model', 'SVM+Ourshort', 'Training', 'BLESS', 'Testing', 'ENTAIL'], ['Model', 'SVM+Our', 'Training', 'BLESS', 'T... | 1 | [['Accuracy']] | [['83.7%'], ['76.5%'], ['77.1%'], ['85.8%'], ['89.4%'], ['87.1%'], ['78.0%'], ['78.9%'], ['87.1%'], ['90.6%']] | column | ['accuracy'] | ['SVM+Our'] | <table border='1' class='dataframe'> <thead> <tr style='text-align: right;'> <th></th> <th>Accuracy</th> </tr> </thead> <tbody> <tr> <td>Model || SVM+Yu || Training || BLESS || Testing || ENTAIL</td> <td>83.7%</td> </tr> <tr> <td>Model || SVM+Word2Vecshort || Training || BLESS... | Table 3 | table_3 | D16-1039 | 7 | emnlp2016 | Experiment 2. This experiment aims to evaluate the generalization capability of our extracted term embeddings. In the experiment, we train the classifier on the BLESS dataset, test it on the ENTAILMENT dataset and vice versa. Similarly, we exclude from the training set any pair of terms that has one term appearing in t... | [2, 2, 2, 2, 1, 1] | ['Experiment 2.', 'This experiment aims to evaluate the generalization capability of our extracted term embeddings.', 'In the experiment, we train the classifier on the BLESS dataset, test it on the ENTAILMENT dataset and vice versa.', 'Similarly, we exclude from the training set any pair of terms that has one term app... | [None, None, ['BLESS', 'ENTAIL'], None, ['SVM+Our', 'Model'], ['SVM+Our']] | 1 |
D16-1043table_5 | Performance on common coverage subsets of the datasets (MEN* and SimLex*). | 3 | [['Source', 'Wikipedia', 'Text'], ['Source', 'Google', 'Visual'], ['Source', 'Google', 'MM'], ['Source', 'Bing', 'Visual'], ['Source', 'Bing', 'MM'], ['Source', 'Flickr', 'Visual'], ['Source', 'Flickr', 'MM'], ['Source', 'ImageNet', 'Visual'], ['Source', 'ImageNet', 'MM'], ['Source', 'ESPGame', 'Visual'], ['Source', 'E... | 6 | [['Arch.', 'AlexNet', 'Agg.', 'Mean', 'Type/Eval', 'SL'], ['Arch.', 'AlexNet', 'Agg.', 'Mean', 'Type/Eval', 'MEN'], ['Arch.', 'AlexNet', 'Agg.', 'Max', 'Type/Eval', 'SL'], ['Arch.', 'AlexNet', 'Agg.', 'Max', 'Type/Eval', 'MEN'], ['Arch.', 'GoogLeNet', 'Agg.', 'Mean', 'Type/Eval', 'SL'], ['Arch.', 'GoogLeNet', 'Agg.', '... | [['0.248', '0.654', '0.248', '0.654', '0.248', '0.654', '0.248', '0.654', '0.248', '0.654', '0.248', '0.654'], ['0.406', '0.549', '0.402', '0.552', '0.420', '0.570', '0.434', '0.579', '0.430', '0.576', '0.406', '0.560'], ['0.366', '0.691', '0.344', '0.693', '0.366', '0.701', '0.342', '0.699', '0.378', '0.701', '0.341',... | column | ['similarity', 'similarity', 'similarity', 'similarity', 'similarity', 'similarity', 'similarity', 'similarity', 'similarity', 'similarity', 'similarity', 'similarity'] | ['VGGNet'] | <table border='1' class='dataframe'> <thead> <tr style='text-align: right;'> <th></th> <th>Arch. || AlexNet || Agg. || Mean || Type/Eval || SL</th> <th>Arch. || AlexNet || Agg. || Mean || Type/Eval || MEN</th> <th>Arch. || AlexNet || Agg. || Max || Type/Eval || SL</th> <th>Arch. || AlexNet ... | Table 5 | table_5 | D16-1043 | 6 | emnlp2016 | 5.2 Common subset comparison. Table 5 shows the results on the common subset of the evaluation datasets, where all word pairs have images in each of the data sources. First, note the same patterns as before: multi-modal representations perform better than linguistic ones. Even for the poorly performing ESP Game dataset... | [2, 1, 1, 1, 1, 1, 1, 1] | ['5.2 Common subset comparison.', 'Table 5 shows the results on the common subset of the evaluation datasets, where all word pairs have images in each of the data sources.', 'First, note the same patterns as before: multi-modal representations perform better than linguistic ones.', 'Even for the poorly performing ESP G... | [None, None, None, ['ESPGame', 'VGGNet', 'SL', 'MEN'], ['Google', 'Bing', 'Flickr', 'ImageNet', 'ESPGame'], None, None, ['VGGNet', 'ImageNet', 'Google', 'Bing', 'Flickr']] | 1 |
D16-1044table_1 | Comparison of multimodal pooling methods. Models are trained on the VQA train split and tested on test-dev. | 2 | [['Method', 'Element-wise Sum'], ['Method', 'Concatenation'], ['Method', 'Concatenation + FC'], ['Method', 'Concatenation + FC + FC'], ['Method', 'Element-wise Product'], ['Method', 'Element-wise Product + FC'], ['Method', 'Element-wise Product + FC + FC'], ['Method', 'MCB (2048 × 2048 → 16K)'], ['Method', 'Full Biline... | 1 | [['Accuracy']] | [['56.50'], ['57.49'], ['58.40'], ['57.10'], ['58.57'], ['56.44'], ['57.88'], ['59.83'], ['58.46'], ['58.69'], ['55.97'], ['57.05'], ['58.36'], ['62.50']] | column | ['accuracy'] | ['MCB (2048 × 2048 → 16K)', 'MCB (128 × 128 → 4K)', 'MCB (d = 16K) with VGG-19', 'MCB (d = 16K) with Attention'] | <table border='1' class='dataframe'> <thead> <tr style='text-align: right;'> <th></th> <th>Accuracy</th> </tr> </thead> <tbody> <tr> <td>Method || Element-wise Sum</td> <td>56.50</td> </tr> <tr> <td>Method || Concatenation</td> <td>57.49</td> </tr> <tr> <td>Met... | Table 1 | table_1 | D16-1044 | 6 | emnlp2016 | 4.3 Ablation Results. We compare the performance of non-bilinear and bilinear pooling methods in Table 1. We see that MCB pooling outperforms all non-bilinear pooling methods, such as eltwise sum, concatenation, and eltwise product. One could argue that the compact bilinear method simply has more parameters than the no... | [2, 1, 1, 1, 2, 1, 2, 2, 1, 1, 1, 1] | ['4.3 Ablation Results.', 'We compare the performance of non-bilinear and bilinear pooling methods in Table 1.', 'We see that MCB pooling outperforms all non-bilinear pooling methods, such as eltwise sum, concatenation, and eltwise product.', 'One could argue that the compact bilinear method simply has more parameters ... | [None, None, ['MCB (2048 × 2048 → 16K)', 'MCB (128 × 128 → 4K)', 'MCB (d = 16K) with VGG-19', 'MCB (d = 16K) with Attention', 'Method'], None, None, ['MCB (2048 × 2048 → 16K)'], ['MCB (2048 × 2048 → 16K)'], ['MCB (2048 × 2048 → 16K)'], None, ['MCB (d = 16K) with VGG-19'], ['MCB (d = 16K) with Attention'], ['MCB (d = 16... | 1 |
D16-1045table_1 | Overall Synthetic Data Results. Aand Bdenote an aggressive and a balanced approaches, respectively. Acc. (std) is the average and the standard deviation of the accuracy across 10 test sets. # Wins is the number of test sets on which the SWVP algorithm outperforms CSP. Gener. is the number of times the best β hyper-para... | 2 | [['Model', 'B-WM'], ['Model', 'B-WMR'], ['Model', 'A-WM'], ['Model', 'A-WMR'], ['Model', 'CSP']] | 2 | [['simple(++), learnable(+++)', 'Acc. (std)'], ['simple(++), learnable(+++)', '# Wins'], ['simple(++), learnable(+++)', 'Gener.'], ['simple(++), learnable(++)', 'Acc. (std)'], ['simple(++), learnable(++)', '# Wins'], ['simple(++), learnable(++)', 'Gener.'], ['simple(+), learnable(+)', 'Acc. (std)'], ['simple(+), learna... | [['75.47(3.05)', '9/10', '10/10', '63.18 (1.32)', '9/10', '10/10', '28.48 (1.9)', '5/10', '10/10'], ['75.96 (2.42)', '8/10', '10/10', '63.02 (2.49)', '9/10', '10/10', '24.31 (5.2)', '4/10', '10/10'], ['74.18 (2.16)', '7/10', '10/10', '61.65 (2.30)', '9/10', '10/10', '30.45 (1.0)', '6/10', '10/10'], ['75.17 (3.07)', '7/... | column | ['Acc. (std)', '# Wins', 'Gener.', 'Acc. (std)', '# Wins', 'Gener.', 'Acc. (std)', '# Wins', 'Gener.'] | ['B-WM', 'B-WMR', 'A-WM', 'A-WMR'] | <table border='1' class='dataframe'> <thead> <tr style='text-align: right;'> <th></th> <th>simple(++), learnable(+++) || Acc. (std)</th> <th>simple(++), learnable(+++) || # Wins</th> <th>simple(++), learnable(+++) || Gener.</th> <th>simple(++), learnable(++) || Acc. (std)</th> <th>simp... | Table 1 | table_1 | D16-1045 | 8 | emnlp2016 | Synthetic Data. Table 1 presents our results. In all three setups an SWVP algorithm is superior. Averaged accuracy differences between the best performing algorithms and CSP are: 3.72 (B-WMR, (simple(++), learnable(+++))), 5.29 (B-WM, (simple(++), learnable(++))) and 5.18 (A-WM, (simple(+), learnable(+))). In all setup... | [2, 1, 1, 1, 1, 1, 1] | ['Synthetic Data.', 'Table 1 presents our results.', 'In all three setups an SWVP algorithm is superior.', 'Averaged accuracy differences between the best performing algorithms and CSP are: 3.72 (B-WMR, (simple(++), learnable(+++))), 5.29 (B-WM, (simple(++), learnable(++))) and 5.18 (A-WM, (simple(+), learnable(+))).',... | [None, None, ['B-WM', 'B-WMR', 'A-WM', 'A-WMR'], ['B-WM', 'B-WMR', 'A-WM', 'A-WMR'], ['B-WM', 'B-WMR', 'A-WM', 'A-WMR', 'CSP'], ['CSP'], ['B-WM', 'B-WMR', 'A-WM', 'A-WMR', 'CSP']] | 1 |
D16-1048table_2 | The performance of cross-lingually similarized Chinese dependency grammars with different configurations. | 2 | [['Grammar', 'baseline'], ['Grammar', 'proj : fixed'], ['Grammar', 'proj : proj'], ['Grammar', 'proj : nonproj'], ['Grammar', 'nonproj : fixed'], ['Grammar', 'nonproj : proj'], ['Grammar', 'nonproj : nonproj']] | 1 | [['Similarity (%)'], ['Dep. P (%)'], ['Ada. P (%)'], ['BLEU-4 (%)']] | [['34.2', '84.5', '84.5', '24.6'], ['46.3', '54.1', '82.3', '25.8 (+1.2)'], ['63.2', '72.2', '84.6', '26.1 (+1.5)'], ['64.3', '74.6', '84.7', '26.2 (+1.6)'], ['48.4', '56.1', '82.6', '20.1 (−4.5)'], ['63.6', '71.4', '84.4', '22.9 (−1.7)'], ['64.1', '73.9', '84.9', '20.7 (−3.9)']] | column | ['Similarity (%)', 'Dep. P (%)', 'Ada. P (%)', 'BLEU-4 (%)'] | ['Grammar'] | <table border='1' class='dataframe'> <thead> <tr style='text-align: right;'> <th></th> <th>Similarity (%)</th> <th>Dep. P (%)</th> <th>Ada. P (%)</th> <th>BLEU-4 (%)</th> </tr> </thead> <tbody> <tr> <td>Grammar || baseline</td> <td>34.2</td> <td>84.5</td> <td>84.... | Table 2 | table_2 | D16-1048 | 8 | emnlp2016 | 5.2.2 Selection of Searching Modes. With the hyper-parameters given by the developing procedures, cross-lingual similarization is conducted on the whole FBIS dataset. All the searching mode configurations are tried and 6 pairs of grammars are generated. For each of the 6 Chinese dependency grammars, we also give the th... | [0, 0, 0, 0, 1, 1, 2, 2, 2, 2, 2, 1, 1, 2, 0] | ['5.2.2 Selection of Searching Modes.', 'With the hyper-parameters given by the developing procedures, cross-lingual similarization is conducted on the whole FBIS dataset.', 'All the searching mode configurations are tried and 6 pairs of grammars are generated.', 'For each of the 6 Chinese dependency grammars, we also ... | [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None] | 1 |
D16-1048table_3 | The performance of the cross-lingually similarized grammar on dependency tree-based translation, compared with related work. | 2 | [['System', '(Liu et al. 2006)'], ['System', '(Chiang 2007)'], ['System', '(Xie et al. 2011)'], ['System', 'Original Grammar'], ['System', 'Similarized Grammar']] | 1 | [['NIST 04'], ['NIST 05']] | [['34.55', '31.94'], ['35.29', '33.22'], ['35.82', '33.62'], ['35.44', '33.08'], ['36.78', '35.12']] | column | ['BLEU', 'BLEU'] | ['Original Grammar', 'Similarized Grammar'] | <table border='1' class='dataframe'> <thead> <tr style='text-align: right;'> <th></th> <th>NIST 04</th> <th>NIST 05</th> </tr> </thead> <tbody> <tr> <td>System || (Liu et al. 2006)</td> <td>34.55</td> <td>31.94</td> </tr> <tr> <td>System || (Chiang 2007)</td> <t... | Table 3 | table_3 | D16-1048 | 8 | emnlp2016 | Table 3 shows the performance of the crosslingually similarized grammar on dependency treebased translation, compared with previous work (Xie et al., 2011). We also give the performance of constituency tree-based translation (Liu et al., 2006) and formal syntax-based translation (Chiang, 2007). The original grammar per... | [1, 2, 1, 1, 2] | ['Table 3 shows the performance of the crosslingually similarized grammar on dependency treebased translation, compared with previous work (Xie et al., 2011).', 'We also give the performance of constituency tree-based translation (Liu et al., 2006) and formal syntax-based translation (Chiang, 2007).', 'The original gra... | [['Similarized Grammar', 'Original Grammar', '(Xie et al. 2011)'], ['(Liu et al. 2006)', '(Chiang 2007)'], ['Original Grammar', '(Xie et al. 2011)'], ['Similarized Grammar', 'Original Grammar', '(Xie et al. 2011)'], None] | 1 |
D16-1050table_1 | BLEU scores on the NIST Chinese-English translation task. AVG = average BLEU scores on test sets. We highlight the best results in bold for each test set. “↑/⇑”: significantly better than Moses (p < 0.05/p < 0.01); “+/++”: significantly better than GroundHog (p < 0.05/p < 0.01); | 2 | [['System', 'Moses'], ['System', 'GroundHog'], ['System', 'VNMT w/o KL'], ['System', 'VNMT']] | 1 | [['MT05'], ['MT02'], ['MT03'], ['MT04'], ['MT06'], ['MT08'], ['AVG']] | [['33.68', '34.19', '34.39', '35.34', '29.20', '22.94', '31.21'], ['31.38', '33.32', '32.59', '35.05', '29.80', '22.82', '30.72'], ['31.40', '33.50', '32.92', '34.95', '28.74', '22.07', '30.44'], ['32.25', '34.50++', '33.78++', '36.72⇑++', '30.92⇑++', '24.41↑++', '32.07']] | column | ['BLEU', 'BLEU', 'BLEU', 'BLEU', 'BLEU', 'BLEU', 'BLEU'] | ['VNMT'] | <table border='1' class='dataframe'> <thead> <tr style='text-align: right;'> <th></th> <th>MT05</th> <th>MT02</th> <th>MT03</th> <th>MT04</th> <th>MT06</th> <th>MT08</th> <th>AVG</th> </tr> </thead> <tbody> <tr> <td>System || Moses</td> <td>33.68</td> <... | Table 1 | table_1 | D16-1050 | 6 | emnlp2016 | Table 1 summarizes the BLEU scores of different systems on the Chinese-English translation tasks. Clearly VNMT significantly improves translation quality in terms of BLEU on most cases, and obtains the best average results that gain 0.86 and 1.35 BLEU points over Moses and GroundHog respectively. Besides, without the K... | [1, 1, 1, 2] | ['Table 1 summarizes the BLEU scores of different systems on the Chinese-English translation tasks.', 'Clearly VNMT significantly improves translation quality in terms of BLEU on most cases, and obtains the best average results that gain 0.86 and 1.35 BLEU points over Moses and GroundHog respectively.', 'Besides, witho... | [None, ['VNMT', 'Moses', 'GroundHog'], ['VNMT w/o KL', 'GroundHog'], None] | 1 |
D16-1051table_1 | Alignment quality results for IBM2-HMM (2H) and its convex relaxation (2HC) using either HMM-style dynamic programming or “Joint” decoding. The first and last columns above are for the GIZA++ HMM initialized either with IBM Model 1 or Model 1 followed by Model 2. FA above refers to the improved IBM Model 2 (FastAlign) ... | 2 | [['Iteration', '1'], ['Iteration', '2'], ['Iteration', '3'], ['Iteration', '4'], ['Iteration', '5'], ['Iteration', '6'], ['Iteration', '7'], ['Iteration', '8'], ['Iteration', '9'], ['Iteration', '10']] | 5 | [['Training', '2H', 'Decoding', 'HMM', 'AER'], ['Training', '2H', 'Decoding', 'HMM', 'F-Measure'], ['Training', '2H', 'Decoding', 'Joint', 'AER'], ['Training', '2H', 'Decoding', 'Joint', 'F-Measure'], ['Training', '2HC', 'Decoding', 'HMM', 'AER'], ['Training', '2HC', 'Decoding', 'HMM', 'F-Measure'], ['Training', '2HC',... | [['0.0956', '0.7829', '0.1076', '0.7797', '0.1538', '0.7199', '0.1814', '0.6914', '0.5406', '0.2951', '0.1761', '0.7219'], ['0.0884', '0.7854', '0.0943', '0.7805', '0.1093', '0.7594', '0.1343', '0.733', '0.1625', '0.7111', '0.0873', '0.8039'], ['0.0844', '0.7899', '0.0916', '0.7806', '0.1023', '0.7651', '0.1234', '0.74... | column | ['AER', 'F-Measure', 'AER', 'F-Measure', 'AER', 'F-Measure', 'AER', 'F-Measure', 'AER', 'F-Measure', 'AER', 'F-Measure'] | ['HMM'] | <table border='1' class='dataframe'> <thead> <tr style='text-align: right;'> <th></th> <th>Training || 15210H || Decoding || HMM || AER</th> <th>Training || 15210H || Decoding || HMM || F-Measure</th> <th>Training || 15210H || Decoding || Joint || AER</th> <th>Training || 15210H || Decoding... | Table 1 | table_1 | D16-1051 | 9 | emnlp2016 | Table 1 shows the alignment summary statistics for the 447 sentences present in the Hansard test data. We present alignments quality scores using either the FastAlign IBM Model 2, the GIZA++ HMM, and our model and its relaxation using either the “HMM” or “Joint” decoding. First, we note that in deciding the decoding st... | [1, 2, 1, 2, 1, 1] | ['Table 1 shows the alignment summary statistics for the 447 sentences present in the Hansard test data.', 'We present alignments quality scores using either the FastAlign IBM Model 2, the GIZA++ HMM, and our model and its relaxation using either the “HMM” or “Joint” decoding.', 'First, we note that in deciding the dec... | [None, ['Training', 'HMM', 'Joint', 'Decoding'], ['2H', 'HMM', 'Joint'], ['HMM'], ['HMM', '2H', '2HC'], ['2H', 'IBM2', 'AER', 'HMM', 'F-Measure', 'FA']] | 1 |
D16-1062table_3 | Comparison of Fleiss’ κ scores with scores from SNLI quality control sentence pairs. | 2 | [['Fleiss’κ', 'Contradiction'], ['Fleiss’κ', 'Entailment'], ['Fleiss’κ', 'Neutral'], ['Fleiss’κ', 'Overall']] | 1 | [['4GS'], ['5GS'], ['Bowman et al. 2015']] | [['0.37', '0.59', '0.77'], ['0.48', '0.63', '0.72'], ['0.41', '0.54', '0.6'], ['0.43', '0.6', '0.7']] | column | ['Fleiss’κ', 'Fleiss’κ', 'Fleiss’κ'] | ['4GS'] | <table border='1' class='dataframe'> <thead> <tr style='text-align: right;'> <th></th> <th>4GS</th> <th>5GS</th> <th>Bowman et al. 2015</th> </tr> </thead> <tbody> <tr> <td>Fleiss’κ || Contradiction</td> <td>0.37</td> <td>0.59</td> <td>0.77</td> </tr> <tr> ... | Table 3 | table_3 | D16-1062 | 6 | emnlp2016 | Table 3 shows that the level of agreement as measured by the Fleiss’κ score is much lower when the number of annotators is increased, particularly for the 4GS set of sentence pairs, as compared to scores noted in Bowman et al. (2015). The decrease in agreement is particularly large with regard to contradiction. This co... | [1, 1, 2, 2, 2] | ['Table 3 shows that the level of agreement as measured by the Fleiss’κ score is much lower when the number of annotators is increased, particularly for the 4GS set of sentence pairs, as compared to scores noted in Bowman et al. (2015).', 'The decrease in agreement is particularly large with regard to contradiction.', ... | [['Fleiss’κ', '4GS', 'Bowman et al. 2015'], ['Contradiction'], None, None, None] | 1 |
D16-1062table_5 | Theta scores and area under curve percentiles for LSTM trained on SNLI and tested on GSIRT . We also report the accuracy for the same LSTM tested on all SNLI quality control items (see Section 3.1). All performance is based on binary classification for each label. | 4 | [['Item', 'Set', '5GS', 'Entailment'], ['Item', 'Set', '5GS', 'Contradiction'], ['Item', 'Set', '5GS', 'Neutral'], ['Item', 'Set', '4GS', 'Contradiction'], ['Item', 'Set', '4GS', 'Neutral']] | 1 | [['Theta Score'], ['Percentile'], ['Test Acc.']] | [['-0.133', '44.83%', '96.5%'], ['1.539', '93.82%', '87.9%'], ['0.423', '66.28%', '88%'], ['1.777', '96.25%', '78.9%'], ['0.441', '67%', '83%']] | column | ['Theta Score', 'Percentile', 'Test Acc.'] | ['4GS', '5GS'] | <table border='1' class='dataframe'> <thead> <tr style='text-align: right;'> <th></th> <th>Theta Score</th> <th>Percentile</th> <th>Test Acc.</th> </tr> </thead> <tbody> <tr> <td>Item || Set || 5GS || Entailment</td> <td>-0.133</td> <td>44.83%</td> <td>96.5%</td> <... | Table 5 | table_5 | D16-1062 | 8 | emnlp2016 | The theta scores from IRT in Table 5 show that, compared to AMT users, the system performed well above average for contradiction items compared to human performance, and performed around the average for entailment and neutral items. For both the neutral and contradiction items, the theta scores are similar across the 4... | [1, 1, 2, 2, 2] | ['The theta scores from IRT in Table 5 show that, compared to AMT users, the system performed well above average for contradiction items compared to human performance, and performed around the average for entailment and neutral items.', 'For both the neutral and contradiction items, the theta scores are similar across ... | [['Theta Score', 'Contradiction', 'Entailment', 'Neutral'], ['Neutral', 'Contradiction', 'Theta Score', '4GS', '5GS', 'Test Acc.'], None, ['Theta Score'], ['Theta Score', 'Test Acc.']] | 1 |
D16-1063table_2 | Performance of different rho functions on Text8 dataset with 17M tokens. | 2 | [['Task', 'Similarity'], ['Task', 'Analogy']] | 2 | [['Robi', '-'], ['ρ0', 'off'], ['ρ0', 'on'], ['ρ1', 'off'], ['ρ1', 'on'], ['ρ2', 'off'], ['ρ2', 'on'], ['ρ3', 'off'], ['ρ3', 'on']] | [['41.2', '69.0', '71.0', '66.7', '70.4', '66.8', '70.8', '68.1', '68.0'], ['22.7', '24.9', '31.9', '34.3', '44.5', '32.3', '40.4', '33.6', '42.9']] | column | ['Robi', 'ρ0', 'ρ0', 'ρ1', 'ρ1', 'ρ2', 'ρ2', 'ρ3', 'ρ3'] | ['Similarity', 'Analogy'] | <table border='1' class='dataframe'> <thead> <tr style='text-align: right;'> <th></th> <th>Robi || -</th> <th>ρ0 || off</th> <th>ρ0 || on</th> <th>ρ1 || off</th> <th>ρ1 || on</th> <th>ρ2 || off</th> <th>ρ2 || on</th> <th>ρ3 || off</th> <th>ρ3 || on</th> </tr> </... | Table 2 | table_2 | D16-1063 | 7 | emnlp2016 | It can be seen from Table 2 that adding the weight rw,c improves performance in all the cases, especially on the word analogy task. Among the four ρ functions, ρ0 performs the best on the word similarity task but suffers notably on the analogy task, while ρ1 = log performs the best overall. Given these observations, wh... | [1, 1, 2] | ['It can be seen from Table 2 that adding the weight rw,c improves performance in all the cases, especially on the word analogy task.', 'Among the four ρ functions, ρ0 performs the best on the word similarity task but suffers notably on the analogy task, while ρ1 = log performs the best overall.', 'Given these observat... | [['Analogy', 'Similarity'], ['ρ0', 'ρ1', 'Similarity', 'Analogy'], ['ρ1']] | 1 |
D16-1065table_3 | Comparison between our joint approaches and the pipelined counterparts. | 4 | [['Dataset', 'LDC2013E117', 'System', 'JAMR (fixed)'], ['Dataset', 'LDC2013E117', 'System', 'System 1'], ['Dataset', 'LDC2013E117', 'System', 'System 2'], ['Dataset', 'LDC2014T12', 'System', 'JAMR (fixed)'], ['Dataset', 'LDC2014T12', 'System', 'System 1'], ['Dataset', 'LDC2014T12', 'System', 'System 2']] | 1 | [['P'], ['R'], ['F1']] | [['0.67', '0.58', '0.62'], ['0.72', '0.65', '0.68'], ['0.73', '0.69', '0.71'], ['0.68', '0.59', '0.63'], ['0.74', '0.63', '0.68'], ['0.73', '0.68', '0.71']] | column | ['P', 'R', 'F1'] | ['System 1', 'System 2'] | <table border='1' class='dataframe'> <thead> <tr style='text-align: right;'> <th></th> <th>P</th> <th>R</th> <th>F1</th> </tr> </thead> <tbody> <tr> <td>Dataset || LDC2013E117 || System || JAMR(fixed)</td> <td>0.67</td> <td>0.58</td> <td>0.62</td> </tr> <tr> ... | Table 3 | table_3 | D16-1065 | 8 | emnlp2016 | 4.4 Joint Model vs. Pipelined Model. In this section, we compare the overall performance of our joint model to the pipelined model, JAMR. To give a fair comparison, we first implemented system 1 only using the same features (i.e., features 1- 4 in Table 1) as JAMR for concept fragments. Table 3 gives the results on the... | [2, 2, 2, 1, 1, 2, 2, 1] | ['4.4 Joint Model vs. Pipelined Model.', 'In this section, we compare the overall performance of our joint model to the pipelined model, JAMR.', 'To give a fair comparison, we first implemented system 1 only using the same features (i.e., features 1- 4 in Table 1) as JAMR for concept fragments.', 'Table 3 gives the res... | [None, None, None, None, ['F1', 'System 1', 'JAMR (fixed)'], ['System 2'], None, ['System 2']] | 1 |
D16-1065table_4 | Final results of various methods. | 4 | [['Dataset', 'LDC2013E117', 'System', 'CAMR*'], ['Dataset', 'LDC2013E117', 'System', 'CAMR'], ['Dataset', 'LDC2013E117', 'System', 'Our approach'], ['Dataset', 'LDC2014T12', 'System', 'CAMR*'], ['Dataset', 'LDC2014T12', 'System', 'CAMR'], ['Dataset', 'LDC2014T12', 'System', 'CCG-based'], ['Dataset', 'LDC2014T12', 'Syst... | 1 | [['P'], ['R'], ['F1']] | [['.69', '.67', '.68'], ['.71', '.69', '.70'], ['.73', '.69', '.71'], ['.70', '.66', '.68'], ['.72', '.67', '.70'], ['.67', '.66', '.66'], ['.73', '.68', '.71']] | column | ['P', 'R', 'F1'] | ['Our approach'] | <table border='1' class='dataframe'> <thead> <tr style='text-align: right;'> <th></th> <th>P</th> <th>R</th> <th>F1</th> </tr> </thead> <tbody> <tr> <td>Dataset || LDC2013E117 || System || CAMR*</td> <td>.69</td> <td>.67</td> <td>.68</td> </tr> <tr> <td>Dat... | Table 4 | table_4 | D16-1065 | 8 | emnlp2016 | We give a comparison between our approach and other state-of-the-art AMR parsers, including CCGbased parser (Artzi et al., 2015) and dependencybased parser (Wang et al., 2015b). For comparison purposes, we give two results from two different versions of dependency-based AMR parser: CAMR* and CAMR. Compared to the latte... | [2, 2, 2, 1] | ['We give a comparison between our approach and other state-of-the-art AMR parsers, including CCGbased parser (Artzi et al., 2015) and dependency-based parser (Wang et al., 2015b).', 'For comparison purposes, we give two results from two different versions of dependency-based AMR parser: CAMR* and CAMR.', 'Compared to ... | [None, ['CAMR*', 'CAMR'], None, ['Our approach', 'System']] | 1 |
D16-1065table_5 | Final results on the full LDC2014T12 dataset. | 4 | [['Dataset', 'LDC2014T12', 'System', 'JAMR (fixed)'], ['Dataset', 'LDC2014T12', 'System', 'CAMR*'], ['Dataset', 'LDC2014T12', 'System', 'CAMR'], ['Dataset', 'LDC2014T12', 'System', 'SMBT-based'], ['Dataset', 'LDC2014T12', 'System', 'Our approach']] | 1 | [['P'], ['R'], ['F1']] | [['.64', '.53', '.58'], ['.68', '.60', '.64'], ['.70', '.62', '.66'], ['-', '-', '.67'], ['.70', '.62', '.66']] | column | ['P', 'R', 'F1'] | ['Our approach'] | <table border='1' class='dataframe'> <thead> <tr style='text-align: right;'> <th></th> <th>P</th> <th>R</th> <th>F1</th> </tr> </thead> <tbody> <tr> <td>Dataset || LDC2014T12 || System || JAMR (fixed)</td> <td>.64</td> <td>.53</td> <td>.58</td> </tr> <tr> <... | Table 5 | table_5 | D16-1065 | 8 | emnlp2016 | We also evaluate our parser on the full LDC2014T12 dataset. We use the training/development/test split recommended in the release: 10,312 sentences for training, 1368 sentences for development and 1371 sentences for testing. For comparison, we include the results of JAMR, CAMR*, CAMR and SMBT-based parser (Pust et al.,... | [2, 2, 2, 1, 1] | ['We also evaluate our parser on the full LDC2014T12 dataset.', 'We use the training/development/test split recommended in the release: 10,312 sentences for training, 1368 sentences for development and 1371 sentences for testing.', 'For comparison, we include the results of JAMR, CAMR*, CAMR and SMBT-based parser (Pust... | [['LDC2014T12'], None, ['JAMR (fixed)', 'CAMR*', 'CAMR', 'SMBT-based', 'Our approach'], ['Our approach', 'CAMR*', 'CAMR'], ['Our approach', 'SMBT-based']] | 1 |
D16-1068table_2 | Per language UAS for the fully supervised setup. Model names are as in Table 1, ‘e’ stands for ensemble. Best results for each language and parsing model order are highlighted in bold. | 2 | [['language', 'swedish'], ['language', 'bulgarian'], ['language', 'chinese'], ['language', 'czech'], ['language', 'dutch'], ['language', 'japanese'], ['language', 'catalan'], ['language', 'english']] | 2 | [['First Order', 'TurboParser'], ['First Order', 'BGI-PP'], ['First Order', 'BGI-PP+i+b'], ['First Order', 'BGI-PP+i+b+e'], ['Second Order', 'TurboParser'], ['Second Order', 'BGI-PP'], ['Second Order', 'BGI-PP+i+b'], ['Second Order', 'BGI-PP+i+b+e']] | [['87.12', '86.35', '86.93', '87.12', '88.65', '86.14', '87.85', '89.29'], ['90.66', '90.22', '90.42', '90.66', '92.43', '89.73', '91.50', '92.58'], ['84.88', '83.89', '84.17', '84.17', '86.53', '81.33', '85.18', '86.59'], ['83.53', '83.46', '83.44', '83.44', '86.35', '84.91', '86.26', '87.50'], ['88.48', '88.56', '88.... | column | ['UAS', 'UAS', 'UAS', 'UAS', 'UAS', 'UAS', 'UAS', 'UAS'] | ['BGI-PP+i+b', 'BGI-PP+i+b+e'] | <table border='1' class='dataframe'> <thead> <tr style='text-align: right;'> <th></th> <th>First Order || TurboParser</th> <th>First Order || BGI-PP</th> <th>First Order || BGI-PP+i+b</th> <th>First Order || BGI-PP+i+b+e</th> <th>Second Order || TurboParser</th> <th>Second Order |... | Table 2 | table_2 | D16-1068 | 8 | emnlp2016 | Table 2 complements our results, providing UAS values for each of the 8 languages participating in this setup. The UAS difference between BGI-PP+i+b and the TurboParser are (+0.24)-(- 0.71) in first order parsing and (+0.18)-(-2.46) in second order parsing. In the latter case, combining these two models (BGI+PP+i+b+e) ... | [1, 1, 1] | ['Table 2 complements our results, providing UAS values for each of the 8 languages participating in this setup.', 'The UAS difference between BGI-PP+i+b and the TurboParser are (+0.24)-(- 0.71) in first order parsing and (+0.18)-(-2.46) in second order parsing.', 'In the latter case, combining these two models (BGI+PP... | [['language', 'First Order', 'Second Order'], ['BGI-PP+i+b', 'TurboParser', 'First Order', 'Second Order'], ['BGI-PP+i+b+e', 'TurboParser', 'language']] | 1 |
D16-1071table_3 | Word relation results. MRR per language and POS type for all models. unfiltered is the unfiltered nearest neighbor search space; filtered is the nearest neighbor search space that contains only one POS. ‡ (resp. †): significantly worse than LAMB (sign test, p < .01, resp. p < .05). Best unfiltered/filtered result per r... | 4 | [['lang', 'cz', 'POS', 'a'], ['lang', 'cz', 'POS', 'n'], ['lang', 'cz', 'POS', 'v'], ['lang', 'cz', 'POS', 'all'], ['lang', 'de', 'POS', 'a'], ['lang', 'de', 'POS', 'n'], ['lang', 'de', 'POS', 'v'], ['lang', 'de', 'POS', 'all'], ['lang', 'en', 'POS', 'a'], ['lang', 'en', 'POS', 'n'], ['lang', 'en', 'POS', 'v'], ['lang'... | 3 | [['unfiltered', 'form', 'real'], ['unfiltered', 'form', 'opt'], ['unfiltered', 'form', 'sum'], ['unfiltered', 'STEM', 'real'], ['unfiltered', 'STEM', 'opt'], ['unfiltered', 'STEM', 'sum'], ['unfiltered', '-', 'LAMB'], ['filtered', 'form', 'real'], ['filtered', 'form', 'opt'], ['filtered', 'form', 'sum'], ['filtered', '... | [['0.03', '0.04', '0.05', '0.02', '0.05', '0.05', '0.06', '0.03‡', '0.05†', '0.07', '0.04†', '0.08', '0.08', '0.09'], ['0.15‡', '0.21‡', '0.24‡', '0.18‡', '0.27‡', '0.26‡', '0.30', '0.17‡', '0.23‡', '0.26‡', '0.20‡', '0.29‡', '0.28‡', '0.32'], ['0.07‡', '0.13‡', '0.16†', '0.08‡', '0.14‡', '0.16‡', '0.18', '0.09‡', '0.1... | column | ['MRR', 'MRR', 'MRR', 'MRR', 'MRR', 'MRR', 'MRR', 'MRR', 'MRR', 'MRR', 'MRR', 'MRR', 'MRR', 'MRR'] | ['LAMB'] | <table border='1' class='dataframe'> <thead> <tr style='text-align: right;'> <th></th> <th>unfiltered || form || real</th> <th>unfiltered || form || opt</th> <th>unfiltered || form || sum</th> <th>unfiltered || STEM || real</th> <th>unfiltered || STEM || opt</th> <th>unfiltered ||... | Table 3 | table_3 | D16-1071 | 7 | emnlp2016 | Results. The MRR results in the left half of Table 3 (“unfiltered”) show that for all languages and for all POS, form real has the worst performance among the form models. This comes at no surprise since this model does barely know anything about word forms and lemmata. The form opt model improves these results based o... | [2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] | ['Results.', 'The MRR results in the left half of Table 3 (“unfiltered”) show that for all languages and for all POS, form real has the worst performance among the form models.', 'This comes at no surprise since this model does barely know anything about word forms and lemmata.', 'The form opt model improves these resu... | [None, ['lang', 'POS', 'unfiltered'], None, ['form'], ['form', 'sum', 'opt'], ['lang'], ['form', 'sum', 'de'], ['de'], ['STEM'], ['lang', 'POS', 'STEM', 'sum', 'opt'], ['STEM'], ['es'], ['STEM'], ['LAMB', 'lang', 'POS'], ['LAMB'], ['cz'], ['LAMB', 'form', 'sum', 'opt', 'de'], ['LAMB']] | 1 |
D16-1071table_5 | Polarity classification results. Bold is best per language and column. | 4 | [['lang', 'cz', 'features', 'Brychcin et al. (2013)'], ['lang', 'cz', 'features', 'form'], ['lang', 'cz', 'features', 'STEM'], ['lang', 'cz', 'features', 'LAMB'], ['lang', 'en', 'features', 'Hagen et al. (2015)'], ['lang', 'en', 'features', 'form'], ['lang', 'en', 'features', 'STEM'], ['lang', 'en', 'features', 'LAMB']... | 1 | [['acc'], ['F1']] | [['-', '81.53'], ['80.86', '80.75'], ['81.51', '81.39'], ['81.21', '81.09'], ['-', '64.84'], ['66.78', '62.21'], ['66.95', '62.06'], ['67.49', '63.01']] | column | ['acc', 'F1'] | ['LAMB'] | <table border='1' class='dataframe'> <thead> <tr style='text-align: right;'> <th></th> <th>acc</th> <th>F1</th> </tr> </thead> <tbody> <tr> <td>lang || cz || features || Brychcin et al. (2013)</td> <td>-</td> <td>81.53</td> </tr> <tr> <td>lang || cz || features || fo... | Table 5 | table_5 | D16-1071 | 8 | emnlp2016 | Results. Table 5 lists the 10-fold cross-validation results (accuracy and macro F1) on the CSFD dataset. LAMB/STEM results are consistently better than form results. In our analysis, we found the following example for the benefit of normalization: “popis a nazev za- ´ jmavy a film je takov ´ a filma ´ ˇrska pras ´ arna... | [2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 1] | ['Results.', 'Table 5 lists the 10-fold cross-validation results (accuracy and macro F1) on the CSFD dataset.', 'LAMB/STEM results are consistently better than form results.', 'In our analysis, we found the following example for the benefit of normalization: “popis a nazev za- ´ jmavy a film je takov ´ a filma ´ ˇrska ... | [None, ['acc', 'F1'], ['LAMB', 'STEM'], None, ['LAMB'], ['form', 'LAMB'], ['form', 'LAMB'], ['LAMB', 'form', 'STEM'], ['LAMB', 'en'], None, ['Hagen et al. (2015)']] | 1 |
D16-1072table_2 | POS tagging performance of online and offline pruning with different r and λ on CTB5 and PD. | 5 | [['Online Pruning', 'r', '2', 'λ', '0.98'], ['Online Pruning', 'r', '4', 'λ', '0.98'], ['Online Pruning', 'r', '8', 'λ', '0.98'], ['Online Pruning', 'r', '16', 'λ', '0.98'], ['Online Pruning', 'r', '8', 'λ', '0.90'], ['Online Pruning', 'r', '8', 'λ', '0.95'], ['Online Pruning', 'r', '8', 'λ', '0.99'], ['Online Pruning'... | 2 | [['Accuracy (%)', 'CTB5-dev'], ['Accuracy (%)', 'PD-dev'], ['#Tags (pruned)', 'CTB-side'], ['#Tags (pruned)', 'PD-side']] | [['94.25', '95.03', '2.0', '2.0'], ['95.06', '95.66', '3.9', '4.0'], ['95.14', '95.83', '6.3', '7.4'], ['95.12', '95.81', '7.8', '14.1'], ['95.15', '95.79', '3.7', '6.3'], ['95.13', '95.82', '5.1', '7.1'], ['95.15', '95.74', '7.4', '7.9'], ['95.15', '95.76', '8.0', '8.0'], ['94.95', '96.05', '4.1', '5.1'], ['95.15', '9... | column | ['Accuracy (%)', 'Accuracy (%)', '#Tags (pruned)', '#Tags (pruned)'] | ['Online Pruning', 'Offline Pruning'] | <table border='1' class='dataframe'> <thead> <tr style='text-align: right;'> <th></th> <th>Accuracy (%) || CTB5-dev</th> <th>Accuracy (%) || PD-dev</th> <th>#Tags (pruned) || CTB-side</th> <th>#Tags (pruned) || PD-side</th> </tr> </thead> <tbody> <tr> <td>Online Pruning || r ||... | Table 2 | table_2 | D16-1072 | 5 | emnlp2016 | 5 Experiments on POS Tagging. 5.1 Parameter Tuning. For both online and offline pruning, we need to decide the maximum number of single-side tag candidates r and the accumulative probability threshold λ for further truncating the candidates. Table 2 shows the tagging accuracies and the averaged numbers of single-side t... | [2, 2, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] | ['5 Experiments on POS Tagging.', '5.1 Parameter Tuning.', 'For both online and offline pruning, we need to decide the maximum number of single-side tag candidates r and the accumulative probability threshold λ for further truncating the candidates.', 'Table 2 shows the tagging accuracies and the averaged numbers of si... | [None, None, ['Online Pruning', 'Offline Pruning', 'λ', 'r'], None, ['Online Pruning'], ['λ', 'r', 'CTB5-dev', 'PD-dev'], ['r'], ['λ', 'r'], ['λ'], ['r', 'λ'], ['r', 'λ'], ['λ'], ['r', 'λ', 'CTB5-dev'], ['r', 'λ'], ['r', 'λ', 'CTB-side', 'PD-side']] | 1 |
D16-1072table_3 | POS tagging performance of difference approaches on CTB5 and PD. | 1 | [['Coupled (Offline)'], ['Coupled (Online)'], ['Coupled (No Prune)'], ['Coupled (Relaxed)'], ['Guide-feature'], ['Baseline'], ['Li et al. (2012b)']] | 2 | [['Accuracy (%)', 'CTB5-test'], ['Accuracy (%)', 'PD-test'], ['Speed', 'Toks/Sec']] | [['94.83', '95.90', '246'], ['94.74', '95.95', '365'], ['94.58', '95.79', '3'], ['94.63', '95.87', '127'], ['94.35', '95.63', '584'], ['94.07', '95.82', '1573'], ['94.60', '—', '—']] | column | ['Accuracy (%)', 'Accuracy (%)', 'Speed'] | ['Coupled (Offline)', 'Coupled (Online)'] | <table border='1' class='dataframe'> <thead> <tr style='text-align: right;'> <th></th> <th>Accuracy (%) || CTB5-test</th> <th>Accuracy (%) || PD-test</th> <th>Speed || Toks/Sec</th> </tr> </thead> <tbody> <tr> <td>Coupled (Offline)</td> <td>94.83</td> <td>95.90</td> <... | Table 3 | table_3 | D16-1072 | 6 | emnlp2016 | 5.2 Main Results. Table 3 summarizes the accuracies on the test data and the tagging speed during the test phase. “Coupled (No Prune)” refers to the coupled model with complete mapping in Li et al. (2015), which maps each one-side tag to all the-other-side tags. “Coupled (Relaxed)” refers the coupled model with relaxed... | [0, 1, 2, 2, 2, 1, 1, 1] | ['5.2 Main Results.', 'Table 3 summarizes the accuracies on the test data and the tagging speed during the test phase.', '“Coupled (No Prune)” refers to the coupled model with complete mapping in Li et al. (2015), which maps each one-side tag to all the-other-side tags.', '“Coupled (Relaxed)” refers the coupled model w... | [None, None, ['Coupled (No Prune)'], ['Coupled (Relaxed)'], ['Li et al. (2012b)'], ['Coupled (Offline)', 'Coupled (Online)'], ['Coupled (Offline)', 'CTB5-test'], ['PD-test']] | 1 |
D16-1072table_4 | WS&POS tagging performance of online and offline pruning with different r and λ on CTB5 and PD. | 5 | [['Online Pruning', 'r', '8', 'λ', '1.00'], ['Online Pruning', 'r', '16', 'λ', '0.95'], ['Online Pruning', 'r', '16', 'λ', '0.99'], ['Online Pruning', 'r', '16', 'λ', '1.00'], ['Offline Pruning', 'r', '16', 'λ', '0.99']] | 2 | [['Accuracy (%)', 'CTB5-dev'], ['Accuracy (%)', 'PD-dev'], ['#Tags (pruned)', 'CTB-side'], ['#Tags (pruned)', 'PD-side']] | [['90.41', '89.91', '8.0', '8.0'], ['90.65', '90.22', '15.9', '16.0'], ['90.77', '90.49', '16.0', '16.0'], ['90.79', '90.49', '16.0', '16.0'], ['91.64', '91.92', '2.5', '3.5']] | column | ['Accuracy (%)', 'Accuracy (%)', '#Tags (pruned)', '#Tags (pruned)'] | ['Online Pruning'] | <table border='1' class='dataframe'> <thead> <tr style='text-align: right;'> <th></th> <th>Accuracy (%) || CTB5-dev</th> <th>Accuracy (%) || PD-dev</th> <th>#Tags (pruned) || CTB-side</th> <th>#Tags (pruned) || PD-side</th> </tr> </thead> <tbody> <tr> <td>Online Pruning || r ||... | Table 4 | table_4 | D16-1072 | 6 | emnlp2016 | Table 4 shows results for tuning r and λ. From the results, we can see that in the online pruning method, λ seems useless and r becomes the only threshold for pruning unlikely single-side tags. The accuracies are much inferior to those from the offline pruning approach. We believe that the accuracies can be further imp... | [1, 1, 1, 2, 1] | ['Table 4 shows results for tuning r and λ.', 'From the results, we can see that in the online pruning method, λ seems useless and r becomes the only threshold for pruning unlikely single-side tags.', 'The accuracies are much inferior to those from the offline pruning approach.', 'We believe that the accuracies can be ... | [None, ['Online Pruning', 'λ'], ['Online Pruning', 'Offline Pruning'], ['r'], ['r', 'λ']] | 1 |
D16-1072table_5 | WS&POS tagging performance of difference approaches on CTB5 and PD. | 1 | [['Coupled (Offline)'], ['Coupled (Online)'], ['Guide-feature'], ['Baseline']] | 2 | [['F (%) on CTB5-test', 'Only WS'], ['F (%) on CTB5-test', 'Joint WS&POS'], ['F (%) on PD-test', 'Only WS'], ['F (%) on PD-test', 'Joint WS&POS'], ['Speed (Char/Sec)', '-']] | [['95.55', '90.58', '96.12', '92.44', '115'], ['94.94', '89.58', '95.60', '91.56', '26'], ['95.07', '89.79', '95.66', '91.61', '27'], ['94.88', '89.49', '96.28', '92.47', '119']] | column | ['F (%) on CTB5-test', 'F (%) on CTB5-test', 'F (%) on PD-test', 'F (%) on PD-test', 'Speed (Char/Sec)'] | ['Coupled (Offline)'] | <table border='1' class='dataframe'> <thead> <tr style='text-align: right;'> <th></th> <th>P/R/F (%) on CTB5-test || Only WS</th> <th>P/R/F (%) on CTB5-test || Joint WS&POS</th> <th>P/R/F (%) on PD-test || Only WS</th> <th>P/R/F (%) on PD-test || Joint WS&POS</th> <th>Speed || ... | Table 5 | table_5 | D16-1072 | 7 | emnlp2016 | 6.2 Main Results. Table 5 summarizes the accuracies on the test data and the tagging speed (characters per second) during the test phase. “Coupled (No Prune)” is not tried due to the prohibitive tag set size in joint WS&POS tagging, and “Coupled (Relaxed)” is also skipped since it seems impossible to manually design re... | [2, 1, 2, 1, 1, 2] | ['6.2 Main Results.', 'Table 5 summarizes the accuracies on the test data and the tagging speed (characters per second) during the test phase.', '“Coupled (No Prune)” is not tried due to the prohibitive tag set size in joint WS&POS tagging, and “Coupled (Relaxed)” is also skipped since it seems impossible to manually d... | [None, None, None, ['Speed (Char/Sec)'], ['F (%) on CTB5-test', 'Coupled (Offline)'], None] | 1 |
D16-1072table_6 | WS&POS tagging performance of difference approaches on CTB5X and PD. | 1 | [['Coupled (Offline)'], ['Guide-feature'], ['Baseline'], ['Sun and Wan (2012)'], ['Jiang et al. (2009)']] | 2 | [['F (%) on CTB5X-test', 'Only WS'], ['F (%) on CTB5X-test', 'Joint WS&POS']] | [['98.01', '94.39'], ['97.96', '94.06'], ['97.37', '93.23'], ['—', '94.36'], ['98.23', '94.03']] | column | ['F (%) on CTB5X-test', 'F (%) on CTB5X-test'] | ['Coupled (Offline)'] | <table border='1' class='dataframe'> <thead> <tr style='text-align: right;'> <th></th> <th>F (%) on CTB5X-test || Only WS</th> <th>F (%) on CTB5X-test || Joint WS&POS</th> </tr> </thead> <tbody> <tr> <td>Coupled (Offline)</td> <td>98.01</td> <td>94.39</td> </tr> <tr> ... | Table 6 | table_6 | D16-1072 | 8 | emnlp2016 | 6.4 Comparison with Previous Work. In order to compare with previous work, we also run our models on CTB5X and PD, where CTB5X adopts a different data split of CTB5 and is widely used in previous research on joint WS&POS tagging (Jiang et al., 2009; Sun and Wan, 2012). CTB5X-dev/test only contain 352/348 sentences resp... | [2, 2, 2, 1, 1, 1, 2] | ['6.4 Comparison with Previous Work.', 'In order to compare with previous work, we also run our models on CTB5X and PD, where CTB5X adopts a different data split of CTB5 and is widely used in previous research on joint WS&POS tagging (Jiang et al., 2009; Sun and Wan, 2012).', 'CTB5X-dev/test only contain 352/348 senten... | [None, None, None, ['F (%) on CTB5X-test'], ['Coupled (Offline)', 'Guide-feature', 'Baseline', 'F (%) on CTB5X-test'], ['Jiang et al. (2009)', 'F (%) on CTB5X-test', 'Coupled (Offline)'], ['Sun and Wan (2012)']] | 1 |
D16-1075table_3 | Performance of various approaches on stream summarization on five topics. | 1 | [['Random'], ['NB'], ['B-HAC'], ['TaHBM'], ['Ge et al. (2015b)'], ['BINet-NodeRank'], ['BINet-AreaRank']] | 2 | [['sports', 'P@50'], ['sports', 'P@100'], ['politics', 'P@50'], ['politics', 'P@100'], ['disaster', 'P@50'], ['disaster', 'P@100'], ['military', 'P@50'], ['military', 'P@100'], ['comprehensive', 'P@50'], ['comprehensive', 'P@100']] | [['0.02', '0.08', '0', '0', '0.02', '0.04', '0', '0', '0.02', '0.03'], ['0.08', '0.12', '0.18', '0.19', '0.42', '0.36', '0.18', '0.17', '0.38', '0.31'], ['0.10', '0.13', '0.30', '0.26', '0.50', '0.47', '0.30', '0.22', '0.36', '0.32'], ['0.18', '0.15', '0.30', '0.29', '0.50', '0.43', '0.46', '0.36', '0.38', '0.33'], ['0... | column | ['P@50', 'P@100', 'P@50', 'P@100', 'P@50', 'P@100', 'P@50', 'P@100', 'P@50', 'P@100'] | ['BINet-NodeRank', 'BINet-AreaRank'] | <table border='1' class='dataframe'> <thead> <tr style='text-align: right;'> <th></th> <th>sports || P@50</th> <th>sports || P@100</th> <th>politics || P@50</th> <th>politics || P@100</th> <th>disaster || P@50</th> <th>disaster || P@100</th> <th>military || P@50</th> <th... | Table 3 | table_3 | D16-1075 | 7 | emnlp2016 | The results are shown in Table 3. It can be clearly observed that BINet-based approaches outperform baselines and perform comparably to the state-ofthe-art model on generating the summaries on most topics: AreaRank achieves the significant improvement over the state-of-the-art model on sports and disasters, and perform... | [1, 1, 1, 1, 1, 1, 2, 2, 2] | ['The results are shown in Table 3.', 'It can be clearly observed that BINet-based approaches outperform baselines and perform comparably to the state-ofthe-art model on generating the summaries on most topics: AreaRank achieves the significant improvement over the state-of-the-art model on sports and disasters, and pe... | [None, ['BINet-NodeRank', 'BINet-AreaRank'], ['sports', 'politics', 'disaster', 'military', 'comprehensive'], ['BINet-AreaRank'], ['sports', 'politics'], ['BINet-AreaRank'], None, ['BINet-AreaRank'], ['BINet-NodeRank', 'BINet-AreaRank']] | 1 |
D16-1078table_2 | The performances on the Abstracts sub-corpus. | 3 | [['Speculation', 'Systems', 'Baseline'], ['Speculation', 'Systems', 'CNN_C'], ['Speculation', 'Systems', 'CNN_D'], ['Negation', 'Systems', 'Baseline'], ['Negation', 'Systems', 'CNN_C'], ['Negation', 'Systems', 'CNN_D']] | 1 | [['P (%)'], ['R (%)'], ['F1'], ['PCLB (%)'], ['PCRB (%)'], ['PCS (%)']] | [['94.71', '90.54', '92.56', '84.81', '85.11', '72.47'], ['95.95', '95.19', '95.56', '93.16', '91.50', '85.75'], ['92.25', '94.98', '93.55', '86.39', '84.50', '74.43'], ['85.46', '72.95', '78.63', '84.00', '58.29', '46.42'], ['85.10', '92.74', '89.64', '81.04', '87.73', '70.86'], ['89.49', '90.54', '89.91', '91.91', '8... | column | ['P (%)', 'R (%)', 'F1', 'PCLB (%)', 'PCRB (%)', 'PCS (%)'] | ['CNN_C', 'CNN_D'] | <table border='1' class='dataframe'> <thead> <tr style='text-align: right;'> <th></th> <th>P (%)</th> <th>R (%)</th> <th>F1</th> <th>PCLB (%)</th> <th>PCRB (%)</th> <th>PCS (%)</th> </tr> </thead> <tbody> <tr> <td>Speculation || Systems || Baseline</td> <td>94.71... | Table 2 | table_2 | D16-1078 | 7 | emnlp2016 | 4.3 Experimental Results on Abstracts. Table 2 summarizes the performances of scope detection on Abstracts. In Table 2, CNN_C and CNN_D refer the CNN-based model with constituency paths and dependency paths, respectively (the same below). It shows that our CNN-based models (both CNN_C and CNN_D) can achieve better perf... | [2, 1, 2, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 0] | ['4.3 Experimental Results on Abstracts.', 'Table 2 summarizes the performances of scope detection on Abstracts.', 'In Table 2, CNN_C and CNN_D refer the CNN-based model with constituency paths and dependency paths, respectively (the same below).', 'It shows that our CNN-based models (both CNN_C and CNN_D) can achieve ... | [None, None, ['CNN_C', 'CNN_D'], ['CNN_C', 'CNN_D', 'Baseline'], ['CNN_C', 'CNN_D'], ['CNN_C', 'CNN_D', 'Baseline'], ['CNN_C', 'CNN_D'], ['CNN_C', 'CNN_D', 'PCRB (%)'], ['PCS (%)'], None, None, ['Negation', 'CNN_C', 'CNN_D'], ['Negation', 'CNN_C', 'CNN_D'], None] | 1 |
D16-1078table_4 | Comparison of our CNN-based model with the state- | 3 | [['Spe', 'System', 'Morante (2009a)'], ['Spe', 'System', 'Özgür (2009)'], ['Spe', 'System', 'Velldal (2012)'], ['Spe', 'System', 'Zou (2013)'], ['Spe', 'System', 'Ours'], ['Neg', 'System', 'Morante (2008)'], ['Neg', 'System', 'Morante (2009b)'], ['Neg', 'System', 'Li (2010)'], ['Neg', 'System', 'Velldal (2012)'], ['Neg... | 1 | [['Abstracts'], ['Cli'], ['Papers']] | [['77.13', '60.59', '47.94'], ['79.89', 'N/A', '61.13'], ['79.56', '78.69', '75.15'], ['84.21', '72.92', '67.24'], ['85.75', '73.92', '59.82'], ['57.33', 'N/A', 'N/A'], ['73.36', '87.27', '50.26'], ['81.84', '89.79', '64.02'], ['74.35', '90.74', '70.21'], ['76.90', '85.31', '61.19'], ['77.14', '89.66', '55.32']] | column | ['PCS', 'PCS', 'PCS'] | ['Ours'] | <table border='1' class='dataframe'> <thead> <tr style='text-align: right;'> <th></th> <th>Abstracts</th> <th>Cli</th> <th>Papers</th> </tr> </thead> <tbody> <tr> <td>Spe || System || Morante (2009a)</td> <td>77.13</td> <td>60.59</td> <td>47.94</td> </tr> <tr> ... | Table 4 | table_4 | D16-1078 | 9 | emnlp2016 | Table 4 compares our CNN-based models with the state-of-the-art systems. It shows that our CNNbased models can achieve higher PCSs (+1.54%) than those of the state-of-the-art systems for speculation scope detection and the second highest PCS for negation scope detection on Abstracts, and can ... | [1, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 0] | ['Table 4 compares our CNN-based models with the state-of-the-art systems.', 'It shows that our CNNbased models can achieve higher PCSs (+1.54%) than those of the state-of-the-art systems for speculation scope detection and the second highest PCS for negation scope detection on Abstracts, and... | [['System'], ['Ours', 'Abstracts', 'Cli'], ['Abstracts', 'Cli'], ['Ours', 'System'], ['Cli'], ['Ours', 'Abstracts'], None, ['Li (2010)'], ['Velldal (2012)'], None, ['Ours'], None] | 1 |
D16-1080table_4 | Effects of embedding on performance. WEU, WENU, REU and RENU represent word embedding update, word embedding without update, random embedding update and random embedding without update respectively. | 1 | [['WEU'], ['WENU'], ['REU'], ['RENU']] | 1 | [['P'], ['R'], ['F1']] | [['80.74%', '81.19%', '80.97%'], ['74.10%', '69.30%', '71.62%'], ['79.01%', '79.75%', '79.38%'], ['78.16%', '64.55%', '70.70%']] | column | ['P', 'R', 'F1'] | ['WEU'] | <table border='1' class='dataframe'> <thead> <tr style='text-align: right;'> <th></th> <th>P</th> <th>R</th> <th>F1</th> </tr> </thead> <tbody> <tr> <td>WEU</td> <td>80.74%</td> <td>81.19%</td> <td>80.97%</td> </tr> <tr> <td>WENU</td> <td>74.10%</td> ... | Table 4 | table_4 | D16-1080 | 7 | emnlp2016 | Table 4 lists the effects of word embedding. We can see that the performance when updating the word embedding is better than when not updating, and the performance of word embedding is a little better than random word embedding. The main reason is that the vocabulary size is 147,377, but the number of words from tweets... | [1, 1, 2, 2, 2] | ['Table 4 lists the effects of word embedding.', 'We can see that the performance when updating the word embedding is better than when not updating, and the performance of word embedding is a little better than random word embedding.', 'The main reason is that the vocabulary size is 147,377, but the number of words fro... | [None, ['WEU', 'WENU', 'REU', 'RENU'], None, None, None] | 1 |
D16-1083table_3 | Classification results across the behavioral features (BF), the reviewer embeddings (RE) , product embeddings (PE) and bigram of the review texts. Training uses balanced data (50:50). Testing uses two class distributions (C.D.): 50:50 (balanced) and Natural Distribution (N.D.). Improvements of our method are statistica... | 3 | [['Method', 'SPEAGLE+(80%)', '50.50.00'], ['Method', 'SPEAGLE+(80%)', 'N.D.'], ['Method', 'Mukherjee_BF', '50.50.00'], ['Method', 'Mukherjee_BF', 'N.D.'], ['Method', 'Mukherjee_BF+Bigram', '50.50.00'], ['Method', 'Mukherjee_BF+Bigram', 'N.D.'], ['Method', 'Ours_RE', '50.50.00'], ['Method', 'Ours_RE', 'N.D.'], ['Method'... | 2 | [['P', 'Hotel'], ['P', 'Restaurant'], ['R', 'Hotel'], ['R', 'Restaurant'], ['F1', 'Hotel'], ['F1', 'Restaurant'], ['A', 'Hotel'], ['A', 'Restaurant']] | [['75.7', '80.5', '83', '83.2', '79.1', '81.8', '81', '82.5'], ['26.5', '50.1', '56', '70.5', '36', '58.6', '80.4', '82'], ['82.4', '82.8', '85.2', '88.5', '83.7', '85.6', '83.8', '83.3'], ['41.4', '48.2', '84.6', '87.9', '55.6', '62.3', '82.4', '78.6'], ['82.8', '84.5', '86.9', '87.8', '84.8', '86.1', '85.1', '86.5'],... | column | ['P', 'P', 'R', 'R', 'F1', 'F1', 'A', 'A'] | ['Ours_RE', 'Ours_RE+PE', 'Ours_RE+PE+Bigram'] | <table border='1' class='dataframe'> <thead> <tr style='text-align: right;'> <th></th> <th>P || Hotel</th> <th>P || Restaurant</th> <th>R || Hotel</th> <th>R || Restaurant</th> <th>F1 || Hotel</th> <th>F1 || Restaurant</th> <th>A || Hotel</th> <th>A || Restaurant</th> ... | Table 3 | table_3 | D16-1083 | 7 | emnlp2016 | The compared results are shown in Table 3. We utilize our learnt embeddings of reviewers (Ours RE), both of reviewers’ embeddings and products’ embeddings (Ours RE+PE), respectively. Moreover, to perform fair comparison, like Mukherjee et al. (2013b), we add representations of the review text in classifier (Ours RE+PE+... | [1, 2, 2, 1, 1, 1, 2] | ['The compared results are shown in Table 3.', 'We utilize our learnt embeddings of reviewers (Ours RE), both of reviewers’ embeddings and products’ embeddings (Ours RE+PE), respectively.', 'Moreover, to perform fair comparison, like Mukherjee et al. (2013b), we add representations of the review text in classifier (Our... | [None, ['Ours_RE', 'Ours_RE+PE'], ['Ours_RE+PE+Bigram'], ['Hotel', 'Restaurant', 'Ours_RE', 'Ours_RE+PE', 'Ours_RE+PE+Bigram'], ['Ours_RE+PE', 'Ours_RE+PE+Bigram'], ['Hotel', 'Restaurant', 'Ours_RE', 'Ours_RE+PE', 'Ours_RE+PE+Bigram'], None] | 1 |
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