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Design and Optimization of a Speech Recognition Front-End for Distant-Talking Control of a Music Playback Device | 1405.1379 | Table 2: Command Accuracy (%) for different commands at different SERs. | ['SER (dB) params.', '−35∼−30 ASR', '−35∼−30 POLQA', '−30∼−25 ASR', '−30∼−25 POLQA', '−25∼−20 ASR', '−25∼−20 POLQA'] | [['BACK', '73', '47', '83', '50', '90', '53'], ['NEXT', '70', '50', '90', '57', '90', '63'], ['PLAY', '80', '67', '94', '80', '96', '83'], ['PAUSE', '76', '50', '87', '57', '87', '60']] | 4.2.2 Recording of Real-World Commands We used eight subjects (male/female, native/non-native) who uttered the command list at a distance of around 1m from the microphone of the Beats Pill™ portable speaker while music was playing. We used four different music tracks in the echo path, where the starting point of the tr... |
Design and Optimization of a Speech Recognition Front-End for Distant-Talking Control of a Music Playback Device | 1405.1379 | Table 1: Phone Accuracy (%) for the noisy TIMIT database . | ['noise model', 'mix uni.', 'mix bi.', 'babble uni.', 'babble bi.', 'music uni.', 'music bi.', 'factory uni.', 'factory bi.'] | [['ASR', '[BOLD] 22.7', '[BOLD] 37.4', '[BOLD] 22.4', '[BOLD] 37.0', '[BOLD] 22.2', '[BOLD] 36.3', '[BOLD] 21.6', '[BOLD] 36.5'], ['POLQA', '21.7', '35.7', '21.6', '35.6', '21.1', '35.3', '21.4', '35.5']] | In order to provide a fair comparison, we also tuned the parameters to maximize the mean opinion score (MOS) using the Perceptual Objective Listening Quality Assessment (POLQA) , through the same GA setup and the same noisy TIMIT database. To assess the performance of our tuning method, we tested on data not used in th... |
Scaling through abstractions – high-performance vectorial wave simulations for seismic inversion with Devito | 2004.10519 | TABLE I: Per-grid-point FLOPs of the finite-difference TTI wave-equation stencil with different spatial discretization orders. | ['spatial order', 'w/o optimizations', 'w/ optimizations'] | [['4', '501', '95'], ['8', '539', '102'], ['12', '1613', '160'], ['16', '5489', '276']] | Owing to the very high number of floating-point operations (FLOPs) needed per grid point for the weighted rotated Laplacian, this anisotropic wave-equation is extremely challenging to implement. The version without FLOP-reducing optimizations is a direct translation of the discretized operators into stencil expressions... |
Fixed Encoder Self-Attention Patterns in Transformer-Based Machine Translation | 2002.10260 | Table 6: Accuracy scores of the German–English models on the ContraWSD and MuCoW test suites. | ['Encoder heads', 'ContraWSD 6+1', 'ContraWSD 6+6', 'MuCoW 6+1', 'MuCoW 6+6'] | [['8L', '[BOLD] 0.804', '0.831', '[BOLD] 0.741', '0.761'], ['7Ftoken+1L', '0.793', '[BOLD] 0.834', '0.734', '[BOLD] 0.772 '], ['7Ftoken (H8 disabled)', '0.761', '0.816', '0.721', '0.757']] | Overall, the model with 6 decoder layers and fixed attentive patterns (7Ftoken+1L) achieves higher accuracy than the model with all learnable attention heads (8L), while the 1-layer decoder models show the opposite effect. It appears that having 6 decoder layers can effectively cope with WSD despite having only one lea... |
Memeify: A Large-Scale Meme Generation System | 1910.12279 | Table 3. Average ratings of volunteers for original and generated memes (baseline and ours) across themes. ARO represents average ratings for original memes, ARG represents average ratings for generated memes (our model), and ARB represents average ratings for generated memes (baseline model). | ['[BOLD] Theme', '[BOLD] ARO', '[BOLD] ARG', '[BOLD] ARB'] | [['Normie', '3.1', '2.9', '2.7'], ['Savage', '3.6', '3.6', '3.4'], ['Depressing', '3.2', '3.1', '3'], ['Unexpected', '3.5', '3.3', '3.3'], ['Frustrated', '3.2', '3', '2.9'], ['Wholesome', '2.8', '2.7', '2.6'], ['Overall', '3.23', '3.1', '2.98']] | To evaluate user satisfaction levels, we conducted a rating study. We generated a batch of 100 memes for each theme by randomly picking classes within each theme, individually for the baseline model and our model. For each theme, we showed a set of 5 generated memes from our model, 5 generated memes from the baseline m... |
Factor Graph Attention | 1904.05880 | Table 3: Performance of discriminative models on VisDial v1.0 test-std. Higher is better for MRR and recall@k, while lower is better for mean rank and NDCG. (*) denotes use of external knowledge. | ['Model', 'MRR', 'R@1', 'R@5', 'R@10', 'Mean', 'NDCG'] | [['LF\xa0', '0.554', '40.95', '72.45', '82.83', '5.95', '0.453'], ['HRE\xa0', '0.542', '39.93', '70.45', '81.50', '6.41', '0.455'], ['MN\xa0', '0.555', '40.98', '72.30', '83.30', '5.92', '0.475'], ['CorefNMN (ResNet-152)\xa0*', '0.615', '47.55', '78.10', '88.80', '4.40', '0.547'], ['NMN (ResNet-152)\xa0*', '0.588', '44... | Our submission to the challenge significantly improved all metrics except for NDCG. We report our results in Tab. While the challenge did allow use of any external resources to improve the model, we only changed our approach to use an ensemble of 5 trained Factor Graph Attention models which were initialized randomly. ... |
Factor Graph Attention | 1904.05880 | Table 1: Performance of discriminative models on VisDial v0.9. Higher is better for MRR and recall@k, while lower is better for mean rank. (*) denotes use of external knowledge. | ['Model', 'MRR', 'R@1', 'R@5', 'R@10', 'Mean'] | [['LF\xa0', '0.5807', '43.82', '74.68', '84.07', '5.78'], ['HRE\xa0', '0.5846', '44.67', '74.50', '84.22', '5.72'], ['HREA\xa0', '0.5868', '44.82', '74.81', '84.36', '5.66'], ['MN\xa0', '0.5965', '45.55', '76.22', '85.37', '5.46'], ['HieCoAtt-QI\xa0', '0.5788', '43.51', '74.49', '83.96', '5.84'], ['AMEM\xa0', '0.6160',... | Visual question answering comparison: We first compare against a variety of baselines (see Tab. Note that almost all of the baselines (except LF, HRE and MN and SF-QIH-se-2) use attention, i.e., attention is an important element in any model. Note that our model uses the entire set of answers to predict each answer ’s ... |
Factor Graph Attention | 1904.05880 | Table 2: Performance on the question generation task. Higher is better for MRR and recall@k, while lower is better for mean rank. | ['Model', 'MRR', 'R@1', 'R@5', 'R@10', 'Mean'] | [['SF-QIH-se-2\xa0', '0.4060', '26.76', '55.17', '70.39', '9.32'], ['FGA', '[BOLD] 0.4138', '[BOLD] 27.42', '[BOLD] 56.33', '[BOLD] 71.32', '[BOLD] 9.1']] | We adapted to this task, by changing the input utilities to the previous interaction (Q+A)t−1 instead of the current question Qt. Our model also improves previous state-of-the-art results (see Tab. |
Source Dependency-Aware Transformer with Supervised Self-Attention | 1909.02273 | Table 1: Case-insensitive BLEU scores (%) for Chinese-to-English translation on NIST datasets. “+CSH” denotes model only trained under the supervision of child attentional adjacency matrix (β = 0). “+PSH” denotes model only trained under the supervision of parent attentional adjacency matrix (α = 0). “+CSH+PSH” is trai... | ['System', 'NIST2005', 'NIST2008', 'NIST2012', 'Average'] | [['RNNsearch', '38.41', '30.01', '28.48', '32.30'], ['Tree2Seq ', '39.44', '31.03', '29.22', '33.23'], ['SE-NMT (Wu et al. 2017)', '40.01', '31.44', '29.45', '33.63'], ['Transformer', '43.89', '34.83', '32.59', '37.10'], ['+CSH', '44.21', '36.63', '33.57', '38.14'], ['+PSH', '44.24', '36.17', '33.86', '38.09'], ['+CSH+... | We report on case-insensitive BLEU here since English words are lowercased. From the table we can see that syntax-aware RNN models always outperform the RNNsearch baseline. However, the performance of the Transformer is much higher than that of all RNN-based methods. In Transformer+CSH, we use only the child attentiona... |
Source Dependency-Aware Transformer with Supervised Self-Attention | 1909.02273 | Table 2: Evaluation results on the English-to-Japanese translation task. | ['System', 'BLEU', 'RIBES'] | [['RNNsearch', '34.83', '80.92'], ['Eriguchi et al. (2016)', '34.91', '81.66'], ['Transformer', '36.24', '81.90'], ['+CSH', '36.83', '82.15'], ['+PSH', '36.75', '82.09'], ['+CSH+PSH', '[BOLD] 37.22', '[BOLD] 82.37']] | We conduct experiments on the WAT2016 English-to-Japanese translation task in this section. Our baseline systems include RNNsearch, a tree2seq attentional NMT model using tree-LSTM proposed by Eriguchi et al. (2016) and Transformer. According to the table, our Transformer+CSH and Transformer+PSH outperform Transformer ... |
Source Dependency-Aware Transformer with Supervised Self-Attention | 1909.02273 | Table 3: BLEU scores (%) for Chinese-to-English (Zh-En), English-to-Chinese (En-Zh) translation on WMT2017 datasets and English-to-German (En-De) task. Both char-level BLEU (CBLEU) and word-level BLEU (WBLEU) are used as metrics for the En-Zh task. | ['System', 'Zh-En', 'En-Zh CBLEU', 'En-Zh WBLEU', 'En-De'] | [['Transformer', '21.29', '32.12', '19.14', '25.71'], ['+CSH', '21.60', '32.46', '19.54', '26.01'], ['+PSH', '21.67', '32.37', '19.53', '25.87'], ['+CSH+PSH', '[BOLD] 22.15', '[BOLD] 33.03', '[BOLD] 20.19', '[BOLD] 26.31']] | To verify the effect of syntax knowledge on large-scale translation tasks, we further conduct three experiments on the WMT2017 bidirectional English-Chinese tasks and WMT2014 English-to-German. For the Chinese-to-English, our proposed method outperforms baseline by 0.86 BLEU score. For the English-to-Chinese task, the ... |
ERNIE: Enhanced Language Representation with Informative Entities | 1905.07129 | Table 5: Results of various models on FewRel and TACRED (%). | ['Model', 'FewRel P', 'FewRel R', 'FewRel F1', 'TACRED P', 'TACRED R', 'TACRED F1'] | [['CNN', '69.51', '69.64', '69.35', '70.30', '54.20', '61.20'], ['PA-LSTM', '-', '-', '-', '65.70', '64.50', '65.10'], ['C-GCN', '-', '-', '-', '69.90', '63.30', '66.40'], ['BERT', '85.05', '85.11', '84.89', '67.23', '64.81', '66.00'], ['ERNIE', '88.49', '88.44', '[BOLD] 88.32', '69.97', '66.08', '[BOLD] 67.97']] | As FewRel does not have any null instance where there is not any relation between entities, we adopt macro averaged metrics to present the model performances. Since FewRel is built by checking whether the sentences contain facts in Wikidata, we drop the related facts in KGs before pre-training for fair comparison. As t... |
ERNIE: Enhanced Language Representation with Informative Entities | 1905.07129 | Table 2: Results of various models on FIGER (%). | ['Model', 'Acc.', 'Macro', 'Micro'] | [['NFGEC (Attentive)', '54.53', '74.76', '71.58'], ['NFGEC (LSTM)', '55.60', '75.15', '71.73'], ['BERT', '52.04', '75.16', '71.63'], ['ERNIE', '[BOLD] 57.19', '[BOLD] 76.51', '[BOLD] 73.39']] | From the results, we observe that: (1) BERT achieves comparable results with NFGEC on the macro and micro metrics. However, BERT has lower accuracy than the best NFGEC model. As strict accuracy is the ratio of instances whose predictions are identical to human annotations, it illustrates some wrong labels from distant ... |
ERNIE: Enhanced Language Representation with Informative Entities | 1905.07129 | Table 3: Results of various models on Open Entity (%). | ['Model', 'P', 'R', 'F1'] | [['NFGEC (LSTM)', '68.80', '53.30', '60.10'], ['UFET', '77.40', '60.60', '68.00'], ['BERT', '76.37', '70.96', '73.56'], ['ERNIE', '[BOLD] 78.42', '[BOLD] 72.90', '[BOLD] 75.56']] | From the table, we observe that: (1) BERT and ERNIE achieve much higher recall scores than the previous entity typing models, which means pre-training language models make full use of both the unsupervised pre-training and manually-annotated training data for better entity typing. (2) Compared to BERT, ERNIE improves t... |
ERNIE: Enhanced Language Representation with Informative Entities | 1905.07129 | Table 6: Results of BERT and ERNIE on different tasks of GLUE (%). | ['Model', 'MNLI-(m/mm)', 'QQP', 'QNLI', 'SST-2'] | [['[EMPTY]', '392k', '363k', '104k', '67k'], ['BERTBASE', '84.6/83.4', '71.2', '-', '93.5'], ['ERNIE', '84.0/83.2', '71.2', '91.3', '93.5'], ['Model', 'CoLA', 'STS-B', 'MRPC', 'RTE'], ['[EMPTY]', '8.5k', '5.7k', '3.5k', '2.5k'], ['BERTBASE', '52.1', '85.8', '88.9', '66.4'], ['ERNIE', '52.3', '83.2', '88.2', '68.8']] | We notice that ERNIE is consistent with BERTBASE on big datasets like MNLI, QQP, QNLI, and SST-2. The results become more unstable on small datasets, that is, ERNIE is better on CoLA and RTE, but worse on STS-B and MRPC. |
ERNIE: Enhanced Language Representation with Informative Entities | 1905.07129 | Table 7: Ablation study on FewRel (%). | ['Model', 'P', 'R', 'F1'] | [['BERT', '85.05', '85.11', '84.89'], ['ERNIE', '88.49', '88.44', '[BOLD] 88.32'], ['w/o entities', '85.89', '85.89', '85.79'], ['w/o dEA', '85.85', '85.75', '85.62']] | In this subsection, we explore the effects of the informative entities and the knowledgeable pre-training task (dEA) for ERNIE using FewRel dataset. w/o entities and w/o dEA refer to fine-tuning ERNIE without entity sequence input and the pre-training task dEA respectively. still injects knowledge information into lang... |
Generating Sentiment-Preserving Fake Online Reviews Using Neural Language Models and Their Human- and Machine-based Detection | 1907.09177 | Table 5: Fluency of reviews (in MOS). Bold font indicates highest score. | ['Model', 'Native', 'Amazon Non-native', 'Overall', 'Native', 'Yelp Non-native', 'Overall'] | [['Original review', '2.85', '3.09', '2.95', '[BOLD] 3.43', '[BOLD] 3.56', '[BOLD] 3.49'], ['Pretrained GPT-2', '2.93', '3.16', '3.06', '2.68', '2.72', '2.70'], ['Fine-tuned GPT-2', '3.24', '3.22', '3.23', '3.35', '3.25', '3.30'], ['mLSTM', '3.06', '[BOLD] 3.37', '3.21', '3.12', '2.96', '3.04'], ['Sentiment modeling', ... | The fine-tuning improved the fluency compared with that of the reviews generated by the pre-trained GPT-2. This suggests that an attack can be made more effective by simply fine-tuning existing models. For the Amazon dataset, the reviews generated by explicitly modeling the sentiment (sentiment modeling) had the highes... |
Generating Sentiment-Preserving Fake Online Reviews Using Neural Language Models and Their Human- and Machine-based Detection | 1907.09177 | Table 3: Rate (in %) and standard error of fake reviews preserving sentiment of original review. | ['LM', 'Amazon', 'Yelp'] | [['Pretrained GPT-2', '62.1±0.9', '64.3±1.4'], ['Fine-tuned GPT-2', '67.0±1.4', '67.7±1.2'], ['mLSTM', '63.2±0.7', '71.0±1.3'], ['Sentiment modeling', '70.7±1.3', '70.1±1.2']] | This means that a large number of fake reviews can be efficiently generated with a desired sentiment by just fine-tuning an LM. The sentiment modeling method had the highest rate for the Amazon database. This was because explicitly modeling sentiment benefits from the additional sentiment information given before the f... |
Multilingual bottleneck features for subword modelingin zero-resource languages | 1803.08863 | Table 3: Word error rates of monolingual sgmm and 10-lingual TDNN ASR system evaluated on the development sets. | ['[BOLD] Language', '[BOLD] Mono', '[BOLD] Multi'] | [['BG', '17.5', '16.9'], ['CS', '17.1', '15.7'], ['DE', '9.6', '9.3'], ['FR', '24.5', '24.0'], ['KO', '20.3', '19.3']] | The multilingual model shows small but consistent improvements for all languages except Vietnamese. Ultimately though, we are not so much interested in the performance on typical \glsasr tasks, but in whether \glsbnfs from this model also generalize to zero-resource applications on unseen languages. |
Multilingual bottleneck features for subword modelingin zero-resource languages | 1803.08863 | Table 2: Average precision scores on the same-different task (dev sets), showing the effects of applying vtln to the input features for the utd and/or cae systems. cae input is either MFCC or MFCC+VTLN. Topline results (rows 5-6) train cAE on gold standard pairs, rather than UTD output. Baseline results (final rows) di... | ['[BOLD] UTD input', '[BOLD] cAE input', '[BOLD] ES', '[BOLD] HA', '[BOLD] HR', '[BOLD] SV', '[BOLD] TR', '[BOLD] ZH'] | [['PLP', '[EMPTY]', '28.6', '39.9', '26.9', '22.2', '25.2', '20.4'], ['PLP', '+VTLN', '46.2', '48.2', '36.3', '37.9', '31.4', '35.7'], ['PLP+VTLN', '[EMPTY]', '40.4', '45.7', '35.8', '25.8', '25.9', '26.9'], ['PLP+VTLN', '+VTLN', '[BOLD] 51.5', '[BOLD] 52.9', '[BOLD] 39.6', '[BOLD] 42.9', '[BOLD] 33.4', '[BOLD] 44.4'],... | We find that \glscae features as trained previously are slightly better than MFCC+VTLN, but can be improved considerably by applying \glsvtln to the input of both \glsutd and \glscae training—indeed, even using gold pairs as \glscae input applying VTLN is beneficial. This suggests that \glscae training and VTLN abstrac... |
Multilingual bottleneck features for subword modelingin zero-resource languages | 1803.08863 | Table 3: Word error rates of monolingual sgmm and 10-lingual TDNN ASR system evaluated on the development sets. | ['[EMPTY]', '[BOLD] Mono', '[BOLD] Multi'] | [['PL', '16.5', '15.1'], ['PT', '20.5', '19.9'], ['RU', '27.5', '26.9'], ['TH', '34.3', '33.3'], ['VI', '11.3', '11.6']] | The multilingual model shows small but consistent improvements for all languages except Vietnamese. Ultimately though, we are not so much interested in the performance on typical \glsasr tasks, but in whether \glsbnfs from this model also generalize to zero-resource applications on unseen languages. |
Multilingual bottleneck features for subword modelingin zero-resource languages | 1803.08863 | Table 4: ap on the same-different task when training cae on the 10-lingual bnfs from above (cAE-BNF) with UTD and gold standard word pairs (test set results). Baselines are MFCC+VTLN and the cAE models from rows 4 and 6 of Table 2 that use MFCC+VTLN as input features. Best result without target language supervision in ... | ['[BOLD] Features', '[BOLD] ES', '[BOLD] HA', '[BOLD] HR', '[BOLD] SV', '[BOLD] TR', '[BOLD] ZH'] | [['MFCC+VTLN', '44.1', '22.3', '25.0', '34.3', '17.9', '33.4'], ['cAE UTD', '72.1', '41.6', '41.6', '53.2', '29.3', '52.8'], ['cAE gold', '85.1', '66.3', '58.9', '67.1', '47.9', '70.8'], ['10-lingual BNFs', '[BOLD] 85.3', '[BOLD] 71.0', '[BOLD] 56.8', '72.0', '[BOLD] 65.3', '77.5'], ['cAE-BNF UTD', '85.0', '67.4', '40.... | We trained the \glscae with the same sets of same-word pairs as before, but replaced VTLN-adapted MFCCs with the 10-lingual \glsbnfs as input features without any other changes in the training procedure. The limiting factor appears to be the quality of the \glsutd pairs. With gold standard pairs, the \glscae features i... |
Incremental Parsing with Minimal Features Using Bi-Directional LSTM | 1606.06406 | Table 2: Development and test set results for shift-reduce dependency parser on Penn Treebank using only (s1, s0, q0) positional features. | ['Parser', 'Dev UAS', 'Dev LAS', 'Test UAS', 'Test LAS'] | [['C & M 2014', '92.0', '89.7', '91.8', '89.6'], ['Dyer et al.\xa02015', '93.2', '90.9', '93.1', '90.9'], ['Weiss et al.\xa02015', '-', '-', '93.19', '91.18'], ['+ Percept./Beam', '-', '-', '93.99', '92.05'], ['Bi-LSTM', '93.31', '91.01', '93.21', '91.16'], ['2-Layer Bi-LSTM', '93.67', '91.48', '93.42', '91.36']] | Despite the minimally designed feature representation, relatively few training iterations, and lack of pre-computed embeddings, the parser performed on par with state-of-the-art incremental dependency parsers, and slightly outperformed the state-of-the-art greedy parser. |
Incremental Parsing with Minimal Features Using Bi-Directional LSTM | 1606.06406 | Table 3: Ablation studies on PTB dev set (wsj 22). Forward and backward context, and part-of-speech input were all critical to strong performace. | ['Parser', 'UAS', 'LAS'] | [['Bi-LSTM Hierarchical†', '93.31', '91.01'], ['† - Hierarchical Actions', '92.94', '90.96'], ['† - Backward-LSTM', '91.12', '88.72'], ['† - Forward-LSTM', '91.85', '88.39'], ['† - tag embeddings', '92.46', '89.81']] | We found that performance could be improved, however, by factoring out the decision over structural actions (i.e., shift, left-reduce, or right-reduce) and the decision of which arc label to assign upon a reduce. We therefore use separate classifiers for those decisions, each with its own fully-connected hidden and out... |
Incremental Parsing with Minimal Features Using Bi-Directional LSTM | 1606.06406 | Table 5: Test F-scores for constituency parsing on Penn Treebank and CTB-5. | ['Parser', '[ITALIC] b', 'English greedy', 'English beam', 'Chinese greedy', 'Chinese beam'] | [['zhu+:2013', '16', '86.08', '90.4', '75.99', '85.6'], ['Mi & Huang (05)', '32', '84.95', '90.8', '75.61', '83.9'], ['Vinyals et al.\xa0(05)', '10', '-', '90.5', '-', '-'], ['Bi-LSTM', '-', '89.75', '-', '79.44', '-'], ['2-Layer Bi-LSTM', '-', '[BOLD] 89.95', '-', '[BOLD] 80.13', '-']] | Although our work are definitely less accurate than those beam-search parsers, we achieve the highest accuracy among greedy parsers, for both English and Chinese. |
Incremental Parsing with Minimal Features Using Bi-Directional LSTM | 1606.06406 | Table 6: Hyperparameters and training settings. | ['[EMPTY]', 'Dependency', 'Constituency'] | [['[BOLD] Embeddings', '[BOLD] Embeddings', '[BOLD] Embeddings'], ['Word (dims)', '50', '100'], ['Tags (dims)', '20', '100'], ['Nonterminals (dims)', '-', '100'], ['Pretrained', 'No', 'No'], ['[BOLD] Network details', '[BOLD] Network details', '[BOLD] Network details'], ['LSTM units (each direction)', '200', '200'], ['... | All experiments were conducted with minimal hyperparameter tuning. We also applied dropout (with p=0.5) to the output of each LSTM layer (separately for each connection in the case of the two-layer network). |
Neural Recovery Machine for Chinese Dropped Pronoun | 1605.02134 | Table 6: Experimental results on dropped pronoun recovery. ∗ indicate that our approach is statistical significant over all the baselines (within 0.95 confidence interval using the t-test). | ['[BOLD] Models', '[BOLD] Accuracy [BOLD] DPI', '[BOLD] Accuracy [BOLD] DPI', '[BOLD] Accuracy [BOLD] DPG', '[BOLD] Accuracy [BOLD] DPG'] | [['[BOLD] Models', '[BOLD] OntoNotes 4.0', '[BOLD] Baidu Zhidao', '[BOLD] OntoNotes 4.0', '[BOLD] Baidu Zhidao'], ['[ITALIC] SVMSL', '0.59', '0.755', '0.2', '0.456'], ['[ITALIC] SVMSS', '0.5', '0.5', '0.073', '0.315'], ['[ITALIC] SVMDL', '0.562', '0.739', '[BOLD] 0.21', '0.47'], ['[ITALIC] SVMDS', '0.5', '0.5', '0.073'... | We compare the performance of the proposed NRM and the baselines in both of the OntoNotes 4.0 and Baidu Zhidao datasets. the Baidu Zhidao dataset for DPG task. To compare the results of the SVMSL and SVMDL, we can see that the dense representation of the dropped hypothesis has better impact on the fine grained task, na... |
Neural Recovery Machine for Chinese Dropped Pronoun | 1605.02134 | Table 7: Experimental results on zero pronoun resolution. ∗ indicate that our approach is statistical significant over all the baselines (within 0.95 confidence interval using the t-test). | ['[EMPTY]', '[BOLD] Automatic Parsing & Automatic AZP [BOLD] SOTA', '[BOLD] Automatic Parsing & Automatic AZP [BOLD] SOTA', '[BOLD] Automatic Parsing & Automatic AZP [BOLD] SOTA', '[BOLD] Automatic Parsing & Automatic AZP [BOLD] ZPSNN', '[BOLD] Automatic Parsing & Automatic AZP [BOLD] ZPSNN', '[BOLD] Automatic Par... | [['[EMPTY]', '[BOLD] R', '[BOLD] P', '[BOLD] F', '[BOLD] R', '[BOLD] P', '[BOLD] F', '[BOLD] R', '[BOLD] P', '[BOLD] F'], ['overall', '19.6', '15.5', '17.3', '31.6', '24.5', '27.6', '[BOLD] 34.4∗', '[BOLD] 24.8∗', '[BOLD] 28.8∗'], ['NW', '11.9', '14.3', '13.0', '[BOLD] 20.0', '[BOLD] 19.4', '[BOLD] 19.7', '[BOLD] 20.0'... | on zero pronoun resolution task. Meanwhile, the ZPSNN+NRM significantly outperforms the SOTA approach in statistics. For further analysis, we will show the case study on the following section. |
Natural Language Generation for Non-Expert Users | 1909.08250 | Table 7: BLUE and ROUGE score | ['[EMPTY]', 'People', 'Mathematics', 'Food & drink'] | [['[BOLD] BLEU', '39.1', '33.4', '52.2'], ['[BOLD] ROUGE-1', '20', '17.9', '14.6'], ['[BOLD] ROUGE-2', '6.7', '6.7', '5.5'], ['[BOLD] ROUGE-L', '11.4', '10.5', '8.13']] | Note that the average BLUE score is calculated only on BLEU assessable sentences, while average ROUGE score is calculated on the sentences whose structure can be recognized and encoded by our system. We note that the BLEU or ROUGE score might not be sufficiently high for a good quality translation. We believe that two ... |
Natural Language Generation for Non-Expert Users | 1909.08250 | Table 6: BLEU assessable sentences | ['[EMPTY]', 'People', 'Mathematics', 'Food & drink'] | [['[BOLD] #sentences', '15', '24', '23'], ['[BOLD] #sentences recognized', '15', '22', '23'], ['[BOLD] BLEU assessable', '10', '15', '11']] | With the small set of rules introducing in this paper to recognize sentence structure, there would be very limited 4-gram in the generated text appearing in original Wikipedia corpus. Therefore, we use BLEU-3 with equal weight distribution instead of BLEU-4 to assess the generated content. Out of 62 sentences from 3 po... |
[ | 2002.01207 | Table 9: Comparison to other systems for full diacritization | ['Setup', 'WER%'] | [['MSA', 'MSA'], ['[BOLD] Our System', '[BOLD] 6.0'], ['Microsoft ATKS', '12.2'], ['Farasa', '12.8'], ['RDI (rashwan2015deep)', '16.0'], ['MADAMIRA (pasha2014madamira)', '19.0'], ['MIT (belinkov2015arabic)', '30.5'], ['CA', 'CA'], ['Our system', '[BOLD] 4.3'], ['Our best MSA system on CA', '14.7']] | As the results show for MSA, our overall diacritization WER is 6.0% while the state of the art system has a WER of 12.2%. As for CA, our best system produced an error rate of 4.3%, which is significantly better than using our best MSA system to diacritize CA. |
[ | 2002.01207 | Table 3: Error analysis: Core word error types for MSA | ['Error', 'Freq.', '%', 'Explanation', 'Examples'] | [['Wrong selection', '215', '40.8', 'Homographs with different diacritized forms', '“qaSor” (قَصْر¿ – palace) vs. “qaSar” (قَصَر¿ – he limited)'], ['Foreign word', '124', '23.5', 'transliterated words including 96 foreign named entities', 'wiykiymaAnoyaA (وِيكِيمَانْيَا¿ – Wikimania)'], ['Invalid diacritized form', '57... | For error analysis, we analyzed all the errors (527 errors). The most prominent error type arises from the selection of a valid diacritized form that does not match the context (40.8%). Perhaps, including POS tags as a feature or augmenting the PRIOR feature with POS tag information and a bigram language model may redu... |
[ | 2002.01207 | Table 4: MSA case errors accounting from more than 1% of errors | ['Error', 'Count', '%', 'Most Common Causes'] | [['a ⇔ u', '133', '19.3', '[ITALIC] POS error: ex. “ka$afa” (كَشَفَ¿ – he exposed) vs. “ka$ofu” (كَشْفُ¿ – exposure) & [ITALIC] Subject vs. object: ex. “tuwHy [BOLD] mivolu” (تُوحِي مِثْلُ¿ – [BOLD] such indicates) vs. “tuwHy [BOLD] mivola” (تُوحِي مِثْلَ¿ – she indicates [BOLD] such)'], ['i ⇔ a', '130', '18.9', '... | For example, the most common error type involves guessing a fatHa (a) instead of damma (u) or vice versa (19.3%). The most common reasons for this error type, based on inspecting the errors, were due to: POS errors (ex. a word is tagged as a verb instead of a noun); and a noun is treated as a subject instead of an obje... |
[ | 2002.01207 | Table 5: CA case errors accounting from more than 1% of errors | ['Error', 'Count', '%', 'Most Common Causes'] | [['a ⇔ u', '2,907', '28.4', '[ITALIC] Subject vs. object: ex. “wafaqa [BOLD] yawoma” (وَفَقَ يَوْمَ¿ – he matches the day) vs. ex. “wafaqa [BOLD] yawomu” (وَفَقَ يَوْمُ¿ – the day matches) & [ITALIC] False subject (object behaves like subject in passive tense): ex. “yufar~iqu [BOLD] qaDaA’a” (يُفَرِّقُ الْقَضَاءَ¿ ... | The error types are similar to those observed for MSA. Some errors are more syntactic and morphological in nature and can be addressed using better POS tagging and identification of indeclinability, particularly as they relate to named entities and nouns with feminine markers. Other errors such as incorrect attachment,... |
[ | 2002.01207 | Table 6: Error analysis: Core word error types for CA | ['Error', 'Freq.', '%', 'Explanation', 'Examples'] | [['Invalid diacritized form', '195', '38.8', 'invalid form', '“>aqosaAm” (أِقْسَام¿ – portions) vs. “>aqasaAm” (أَقَسَام¿)'], ['Wrong selection', '157', '31.4', 'Homographs with different diacritized forms', '“raAfoE” (رَقْع¿ – lifting) vs. “rafaE” (رَفَع¿ – he lifted)'], ['Affix diacritization error', '66', '13.2', 'S... | We randomly selected and analyzed 500 errors (5.2% of the errors). The two most common errors involve the system producing completely correct diacritized forms (38.8%) or correct forms that don’t match the context (31.4%). The relatively higher percentage of completely incorrect guesses, compared to MSA, may point to t... |
[ | 2002.01207 | Table 7: Comparing our system to state-of-the-art systems – Core word diacritics | ['System', 'Error Rate WER', 'Error Rate DER'] | [['MSA', 'MSA', 'MSA'], ['[BOLD] Our system', '[BOLD] 2.9', '[BOLD] 0.9'], ['(rashwan2015deep)', '3.0', '1.0'], ['Farasa', '3.3', '1.1'], ['Microsoft ATKS', '5.7', '2.0'], ['MADAMIRA', '6.7', '1.9'], ['(belinkov2015arabic)', '14.9', '3.9'], ['CA', 'CA', 'CA'], ['Our system', '2.2', '0.9'], ['Our best MSA system on CA',... | Moreover, post correction improved results overall. We compare our results to five other systems, namely Farasa (darwish2017arabic), MADAMIRA (pasha2014madamira), RDI (Rashwan et al., 2015), MIT (Belinkow and Glass, 2015), and Microsoft ATKS (microsoft2013diac). As the results show, our results beat the current state-o... |
Multimodal Analytics for Real-world News using Measures of Cross-modal Entity Consistency | 2003.10421 | Table 1. Number of test documents |D|, unique entities T∗ in all articles, and mean amount of unique entities ¯¯¯¯T in articles containing a given entity type (for context this is the mean amount of nouns as explained in Section 3.1.2) for TamperedNews (left) and News400 (right). Valid image-text relations for News400 ... | ['[BOLD] TamperedNews dataset [BOLD] Documents', '[BOLD] TamperedNews dataset | [ITALIC] D|', '[BOLD] TamperedNews dataset [ITALIC] T∗', '[BOLD] TamperedNews dataset ¯¯¯¯ [ITALIC] T'] | [['All\xa0(context)', '72,561', '—', '121.40'], ['With persons', '34,051', '4,784', '4.03'], ['With locations', '67,148', '3,455', '4.90'], ['With events', '16,786', '897', '1.33']] | They were both manipulated to perform experiments for cross-modal consistency verification. Experiments and comparisons to related work (jaiswal2017multimedia; sabir2018deep) on datasets such as MEIR (sabir2018deep) are not reasonable since 1) they do not contain public persons or events, and 2) rely on pre-defined ref... |
Multimodal Analytics for Real-world News using Measures of Cross-modal Entity Consistency | 2003.10421 | Table 1. Number of test documents |D|, unique entities T∗ in all articles, and mean amount of unique entities ¯¯¯¯T in articles containing a given entity type (for context this is the mean amount of nouns as explained in Section 3.1.2) for TamperedNews (left) and News400 (right). Valid image-text relations for News400 ... | ['[BOLD] News400 dataset [BOLD] Documents', '[BOLD] News400 dataset | [ITALIC] D|', '[BOLD] News400 dataset [ITALIC] T∗', '[BOLD] News400 dataset ¯¯¯¯ [ITALIC] T'] | [['All (verified context)', '400 (91)', '—', '137.35'], ['With persons (verified)', '322 (116)', '424', '5.41'], ['With locations (verified)', '389 (69)', '451', '9.22'], ['With events (verified)', '170 (31)', '39', '1.84']] | They were both manipulated to perform experiments for cross-modal consistency verification. Experiments and comparisons to related work (jaiswal2017multimedia; sabir2018deep) on datasets such as MEIR (sabir2018deep) are not reasonable since 1) they do not contain public persons or events, and 2) rely on pre-defined ref... |
Multimodal Analytics for Real-world News using Measures of Cross-modal Entity Consistency | 2003.10421 | Table 5. Results for document verification (DV) and collection retrieval for the the News400 dataset. Results are reported for all available and verified documents |D|. | ['[BOLD] Test set (| [ITALIC] D|)', '[BOLD] DV [BOLD] VA', '[BOLD] Collection Retrieval [BOLD] AUC', '[BOLD] Collection Retrieval [BOLD] AP-clean', '[BOLD] Collection Retrieval [BOLD] AP-clean', '[BOLD] Collection Retrieval [BOLD] AP-clean', '[BOLD] Collection Retrieval [BOLD] AP-tampered', '[BOLD] Collection Ret... | [['[BOLD] Test set (| [ITALIC] D|)', '[BOLD] VA', '[BOLD] AUC', '@25%', '@50%', '@100%', '@25%', '@50%', '@100%'], ['[BOLD] Persons (116)', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]'], ['Random', '0.94', '0.92', '100.0', '100.0', '93.79', '87.68', '87.57', '86.77'], ['PsC', '... | Since the number of documents is rather limited and cross-modal mutual presence of entities was manually verified, results for News400 are reported for all documents with verified relations. However, results while retrieving tampered documents are noticeable worse. This is mainly caused by the fact that some untampered... |
MHSAN: Multi-Head Self-Attention Network for Visual Semantic Embedding | 2001.03712 | Table 5: Impact of diversity regularization on performance of retrieval. The lower the diversity loss, the more diverse the local regions focused by the multiple attention maps. | ['Diversity Regularization', 'Sentence Retrieval', 'Image Retrieval', 'Diversity Loss'] | [['W/', '[BOLD] 73.5', '[BOLD] 59.1', '[BOLD] 5.59'], ['W/O', '72.3', '58.4', '7.36']] | In this subsection, we experiment to observe the effect of diversity regularization in the loss. We conjecture that diversity regularization encourages encoded vectors to contain various semantic meanings in an image, reducing the redundancy of the encoded vectors and attention weight vectors. Visualization. We qualita... |
MHSAN: Multi-Head Self-Attention Network for Visual Semantic Embedding | 2001.03712 | Table 3: Effect of self-attention (1-head) in each encoder. We denote the image encoder using selective spatial pooling and the text encoder using the last hidden state which are used in Engilberg [3] as W/O attention. We report only R@1 for effective comparison. | ['Image Encoder', 'Text Encoder', 'Sentence Retrieval(R@1)', 'Image Retrieval(R@1)'] | [['W/O attention ', 'W/O attention ', '69.8', '55.9'], ['Our image encoder(1-head)', 'W/O attention ', '[BOLD] 70.1', '55.8'], ['W/O attention ', 'Our text encoder(1-head)', '69.9', '[BOLD] 56.2']] | For this experiment, we use the MS-COCO dataset. To see the effect of the single-head self-attention mechanism at each encoder, we apply the single-head attention to each encoder separately. Visualization. We observe that self-attention with a single-head captures the missing parts on the selective spatial pooling, Thr... |
Improvements to Deep Convolutional Neural Networks for LVCSR | 1309.1501 | Table 13: WER on Broadcast News, 400 hrs | ['model', 'dev04f', 'rt04'] | [['Hybrid DNN', '15.1', '13.4'], ['DNN-based Features', '15.3', '13.5'], ['Old CNN-based Features ', '13.4', '12.2'], ['Proposed CNN-based Features', '13.6', '12.5'], ['Proposed Hybrid CNN', '[BOLD] 12.7', '[BOLD] 11.7']] | While the proposed 512-hybrid CNN-based feature system did improve (14.1 WER) over the old CNN (14.8 WER), performance slightly deteriorates after CNN-based features are extracted from the network. However, the 5,999-hybrid CNN offers between a 13-16% relative improvement over the DNN hybrid system, and between a 4-5% ... |
Improvements to Deep Convolutional Neural Networks for LVCSR | 1309.1501 | Table 2: WER as a Function of # of Convolutional Layers | ['# of Convolutional vs.', 'WER'] | [['Fully Connected Layers', '[EMPTY]'], ['No conv, 6 full (DNN)', '24.8'], ['1 conv, 5 full', '23.5'], ['2 conv, 4 full', '22.1'], ['3 conv, 3 full', '22.4']] | Note that for each experiment, the number of parameters in the network is kept the same. The table shows that increasing the number of convolutional layers up to 2 helps, and then performance starts to deteriorate. Furthermore, we can see from the table that CNNs offer improvements over DNNs for the same input feature ... |
Improvements to Deep Convolutional Neural Networks for LVCSR | 1309.1501 | Table 3: WER as a function of # of hidden units | ['Number of Hidden Units', 'WER'] | [['64', '24.1'], ['128', '23.0'], ['220', '22.1'], ['128/256', '21.9']] | Again the total number of parameters in the network is kept constant for all experiments. We can observe that as we increase the number of hidden units up to 220, the WER steadily decreases. We do not increase the number of hidden units past 220 as this would require us to reduce the number of hidden units in the fully... |
Improvements to Deep Convolutional Neural Networks for LVCSR | 1309.1501 | Table 6: Results with Different Pooling Types | ['Method', 'WER'] | [['Max Pooling', '18.9'], ['Stochastic Pooling', '18.8'], ['[ITALIC] lp pooing', '18.9']] | Given the success of lp and stochastic pooling, we compare both of these strategies to max-pooling on an LVCSR task. Stochastic pooling seems to provide improvements over max and lp pooling, though the gains are slight. Unlike vision tasks, in appears that in tasks such as speech recognition which have a lot more data ... |
Improvements to Deep Convolutional Neural Networks for LVCSR | 1309.1501 | Table 8: Pooling in Time | ['Method', 'WER'] | [['Baseline', '18.9'], ['Pooling in Time, Max', '18.9'], ['Pooling in Time, Stochastic', '18.8'], ['Pooling in Time, [ITALIC] lp', '18.8']] | We see that pooling in time helps slightly with stochastic and lp pooling. However, the gains are not large, and are likely to be diminished after sequence training. It appears that for large tasks with more data, regularizations such as pooling in time are not helpful, similar to other regularization schemes such as l... |
Improvements to Deep Convolutional Neural Networks for LVCSR | 1309.1501 | Table 9: WER With Improved fMLLR Features | ['Feature', 'WER'] | [['VTLN-warped log-mel+d+dd', '18.8'], ['proposed fMLLR + VTLN-warped log-mel+d+dd', '[BOLD] 18.3']] | Notice that by applying fMLLR in a decorrelated space, we can achieve a 0.5% improvement over the baseline VTLN-warped log-mel system. |
Improvements to Deep Convolutional Neural Networks for LVCSR | 1309.1501 | Table 10: WER of HF Sequence Training + Dropout | ['Non-Linearity', 'WER'] | [['Sigmoid', '15.7'], ['ReLU, No Dropout', '15.6'], ['ReLU, Dropout Fixed for CG Iterations', '[BOLD] 15.0'], ['ReLU, Dropout Per CG Iteration', '15.3']] | By using dropout but fixing the dropout mask per utterance across all CG iterations, we can achieve a 0.6% improvement in WER. Finally, if we compare this to varying the dropout mask per CG training iteration, the WER increases. This shows experimental evidence that if the dropout mask is not fixed, we cannot guarantee... |
Improvements to Deep Convolutional Neural Networks for LVCSR | 1309.1501 | Table 11: HF Seq. Training WER Per CE Iteration | ['CE Iter', '# Times Annealed', 'CE WER', 'HF WER'] | [['4', '1', '20.8', '15.3'], ['6', '2', '19.8', '15.0'], ['8', '3', '19.4', '15.0'], ['13', '7', '18.8', '15.0']] | Finally, we explore if we can reduce the number of CE iterations before moving to sequence training. A main advantage of sequence training is that it is more closely linked to the speech recognition objective function compared to cross-entropy. Using this fact, we explore how many iterations of CE are actually necessar... |
Improvements to Deep Convolutional Neural Networks for LVCSR | 1309.1501 | Table 12: WER on Broadcast News, 50 hours | ['model', 'dev04f', 'rt04'] | [['Hybrid DNN', '16.3', '15.8'], ['Old Hybrid CNN ', '15.8', '15.0'], ['Proposed Hybrid CNN', '[BOLD] 15.4', '[BOLD] 14.7'], ['DNN-based Features', '17.4', '16.6'], ['Old CNN-based Features ', '15.5', '15.2'], ['Proposed CNN-based Features', '15.3', '15.1']] | The proposed CNN hybrid system offers between a 6-7% relative improvement over the DNN hybrid, and a 2-3% relative improvement over the old CNN hybrid system. While the proposed CNN-based feature system offers a modest 1% improvement over the old CNN-based feature system, this slight improvements with feature-based sys... |
Balancing Training for Multilingual Neural Machine Translation | 2004.06748 | Table 4: Mean and variance of the average BLEU score for the Diverse group. The models trained with MultiDDS-S perform better and have less variance. | ['[BOLD] Method', '[BOLD] M2O [BOLD] Mean', '[BOLD] M2O [BOLD] Var.', '[BOLD] O2M [BOLD] Mean', '[BOLD] O2M [BOLD] Var.'] | [['MultiDDS', '26.85', '0.04', '18.20', '0.05'], ['MultiDDS-S', '26.94', '0.02', '18.24', '0.02']] | MultiDDS -S also results in smaller variance in the final model performance. We run MultiDDS and MultiDDS-S with 4 different random seeds, and record the mean and variance of the average BLEU score. |
Balancing Training for Multilingual Neural Machine Translation | 2004.06748 | Table 1: Average BLEU for the baselines and our methods. Bold indicates the highest value. | ['[EMPTY]', '[BOLD] Method', '[BOLD] M2O [BOLD] Related', '[BOLD] M2O [BOLD] Diverse', '[BOLD] O2M [BOLD] Related', '[BOLD] O2M [BOLD] Diverse'] | [['Baseline', 'Uni. ( [ITALIC] τ=∞)', '22.63', '24.81', '15.54', '16.86'], ['Baseline', 'Temp. ( [ITALIC] τ=5)', '24.00', '26.01', '16.61', '17.94'], ['Baseline', 'Prop. ( [ITALIC] τ=1)', '24.88', '26.68', '15.49', '16.79'], ['Ours', 'MultiDDS', '25.26', '26.65', '17.17', '[BOLD] 18.40'], ['Ours', 'MultiDDS-S', '[BOLD]... | First, comparing the baselines, we can see that there is no consistently strong strategy for setting the sampling ratio, with proportional sampling being best in the M2O setting, but worst in the O2M setting. Next, we can see that MultiDDS outperforms the best baseline in three of the four settings and is comparable to... |
Balancing Training for Multilingual Neural Machine Translation | 2004.06748 | Table 3: Average BLEU of the best baseline and three MultiDDS-S settings for the Diverse group. MultiDDS-S always outperform the baseline. | ['[BOLD] Setting', '[BOLD] Baseline', '[BOLD] MultiDDS-S [BOLD] Regular', '[BOLD] MultiDDS-S [BOLD] Low', '[BOLD] MultiDDS-S [BOLD] High'] | [['M2O', '26.68', '27.00', '26.97', '27.08'], ['O2M', '17.94', '18.24', '17.95', '18.55']] | We performed experiments with these aggregation methods on the Diverse group, mainly because there is more performance trade-off among these languages. The languages are ordered on the x-axis from left to right in decreasing perplexity. Low generally performs better on the low-performing languages on the left, while Hi... |
Inducing Multilingual Text Analysis Tools Using Bidirectional Recurrent Neural Networks | 1609.09382 | Table 2: Super Sense Tagging (SST) accuracy for Simple Projection, RNN and their combination. | ['[BOLD] Model Baseline', '[BOLD] Model', '[BOLD] Italian [BOLD] MSC-IT-1', '[BOLD] Italian [BOLD] MSC-IT-2', '[BOLD] French [BOLD] MSC-FR-1', '[BOLD] French [BOLD] MSC-FR-2'] | [['Baseline', '[EMPTY]', '[BOLD] trans man.', '[BOLD] trans. auto', '[BOLD] trans. auto', '[BOLD] trans auto.'], ['Baseline', 'Simple Projection', '61.3', '45.6', '42.6', '44.5'], ['SST Based RNN', 'SRNN', '59.4', '46.2', '46.2', '47.0'], ['SST Based RNN', 'BRNN', '59.7', '46.2', '46.0', '47.2'], ['SST Based RNN', 'SRN... | SRNN-POS-X and BRNN-POS-X refer to our RNN variants: In means input layer, H1 means first hidden layer and H2 means second hidden layer. We achieve the best performance on Italian using MSC-IT-1 clean corpus while noisy training corpus degrades SST performance. The best results are obtained with combination of simple p... |
Inducing Multilingual Text Analysis Tools Using Bidirectional Recurrent Neural Networks | 1609.09382 | Table 1: Token-level POS tagging accuracy for Simple Projection, SRNN using MultiVec bilingual word embeddings as input, RNN444For RNN models, only one (same) system is used to tag German, Greek and Spanish, Projection+RNN and methods of Das & Petrov (2011), Duong et al (2013) and Gouws & Søgaard (2015). | ['[25mm] [BOLD] ModelLang.', '[BOLD] French All words', '[BOLD] French OOV', '[BOLD] German All words', '[BOLD] German OOV', '[BOLD] Greek All words', '[BOLD] Greek OOV', '[BOLD] Spanish All words', '[BOLD] Spanish OOV'] | [['Simple Projection', '80.3', '77.1', '78.9', '73.0', '77.5', '72.8', '80.0', '79.7'], ['SRNN MultiVec', '75.0', '65.4', '70.3', '68.8', '71.1', '65.4', '73.4', '62.4'], ['SRNN', '78.5', '70.0', '76.1', '76.4', '75.7', '70.7', '78.8', '72.6'], ['BRNN', '80.6', '70.9', '77.5', '76.6', '77.2', '71.0', '80.5', '73.1'], [... | We note that the POS tagger based on bidirectional RNN (BRNN) has better performance than simple RNN (SRNN), which means that both past and future contexts help select the correct tag. It is shown that after replacing OOVs by the closest words using CBOW, the tagging accuracy significantly increases. |
Piecewise Latent Variables for Neural Variational Text Processing | 1612.00377 | Table 1: Test perplexities on three document modeling tasks: 20-NewGroup (20-NG), Reuters corpus (RCV1) and CADE12 (CADE). Perplexities were calculated using 10 samples to estimate the variational lower-bound. The H-NVDM models perform best across all three datasets. | ['[BOLD] Model', '[BOLD] 20-NG', '[BOLD] RCV1', '[BOLD] CADE'] | [['[ITALIC] LDA', '1058', '−−', '−−'], ['[ITALIC] docNADE', '896', '−−', '−−'], ['[ITALIC] NVDM', '836', '−−', '−−'], ['[ITALIC] G-NVDM', '651', '905', '339'], ['[ITALIC] H-NVDM-3', '607', '865', '[BOLD] 258'], ['[ITALIC] H-NVDM-5', '[BOLD] 566', '[BOLD] 833', '294']] | Next, we observe that integrating our proposed piecewise variables yields even better results in our document modeling experiments, substantially improving over the baselines. More importantly, in the 20-NG and Reuters datasets, increasing the number of pieces from 3 to 5 further reduces perplexity. Thus, we have achie... |
Piecewise Latent Variables for Neural Variational Text Processing | 1612.00377 | Table 3: Ubuntu evaluation using F1 metrics w.r.t. activities and entities. G-VHRED, P-VHRED and H-VHRED all outperform the baseline HRED. G-VHRED performs best w.r.t. activities and H-VHRED performs best w.r.t. entities. | ['[BOLD] Model', '[BOLD] Activity', '[BOLD] Entity'] | [['[ITALIC] HRED', '4.77', '2.43'], ['[ITALIC] G-VHRED', '[BOLD] 9.24', '2.49'], ['[ITALIC] P-VHRED', '5', '2.49'], ['[ITALIC] H-VHRED', '8.41', '[BOLD] 3.72']] | All latent variable models outperform HRED w.r.t. both activities and entities. This strongly suggests that the high-level concepts represented by the latent variables help generate meaningful, goal-directed responses. Furthermore, each type of latent variable appears to help with a different aspects of the generation ... |
Piecewise Latent Variables for Neural Variational Text Processing | 1612.00377 | Table 6: Approximate posterior word encodings (20-NG). For P-KL, we bold every case where piecewise variables showed greater word sensitivity than Gaussian variables w/in the same hybrid model. | ['[BOLD] Word', '[BOLD] G-NVDM', '[BOLD] H-NVDM-5', '[EMPTY]'] | [['[BOLD] Time-related', '[BOLD] G-KL', '[BOLD] G-KL', '[BOLD] P-KL'], ['months', '23', '33', '[BOLD] 40'], ['day', '28', '32', '[BOLD] 35'], ['time', '[BOLD] 55', '22', '[BOLD] 40'], ['century', '[BOLD] 28', '13', '[BOLD] 19'], ['past', '[BOLD] 30', '18', '[BOLD] 28'], ['days', '[BOLD] 37', '14', '[BOLD] 19'], ['ahead... | The Gaussian variables were originally were sensitive to some of the words in the table. However, in the hybrid model, nearly all of the temporal words that the Gaussian variables were once more sensitive to now more strongly affect the piecewise variables, which themselves also capture all of the words that were origi... |
Piecewise Latent Variables for Neural Variational Text Processing | 1612.00377 | Table 6: Approximate posterior word encodings (20-NG). For P-KL, we bold every case where piecewise variables showed greater word sensitivity than Gaussian variables w/in the same hybrid model. | ['[BOLD] Word', '[BOLD] G-NVDM', '[BOLD] H-NVDM-5', '[EMPTY]'] | [['[BOLD] Names', '[BOLD] G-KL', '[BOLD] G-KL', '[BOLD] P-KL'], ['henry', '33', '[BOLD] 47', '39'], ['tim', '[BOLD] 32', '27', '11'], ['mary', '26', '[BOLD] 51', '30'], ['james', '40', '[BOLD] 72', '30'], ['jesus', '28', '[BOLD] 87', '39'], ['george', '26', '[BOLD] 56', '29'], ['keith', '65', '[BOLD] 94', '61'], ['kent... | The Gaussian variables were originally were sensitive to some of the words in the table. However, in the hybrid model, nearly all of the temporal words that the Gaussian variables were once more sensitive to now more strongly affect the piecewise variables, which themselves also capture all of the words that were origi... |
MultiFiT: Efficient Multi-lingual Language Model Fine-tuning | 1909.04761 | Table 6: Bootstrapping results on MLDoc with and without pretraining, trained on 1k/10k LASER labels. | ['[EMPTY]', 'de', 'es', 'fr', 'it', 'ja', 'ru', 'zh'] | [['LASER, code', '87.65', '75.48', '84.00', '71.18', '64.58', '66.58', '76.65'], ['Random init. (1k)', '77.80', '70.50', '75.65', '68.52', '68.50', '61.37', '79.19'], ['Random init. (10k)', '90.53', '69.75', '87.40', '72.72', '67.55', '63.67', '81.44'], ['MultiFiT, pseudo (1k)', '[BOLD] 91.34', '[BOLD] 78.92', '[BOLD] ... | S6SS0SSS0Px3 Robustness to noise We suspect that MultiFiT is able to outperform its teacher as the information from pretraining makes it robust to label noise. To test this hypothesis, we train MultiFiT and a randomly initialized model with the same architecture on 1k and 10k examples of the Spanish MLDoc. The pretrain... |
MultiFiT: Efficient Multi-lingual Language Model Fine-tuning | 1909.04761 | Table 2: Comparison of zero-shot and supervised methods on MLDoc. | ['[EMPTY]', 'de', 'es', 'fr', 'it', 'ja', 'ru', 'zh'] | [['[ITALIC] Zero-shot (1,000 source language examples)', '[ITALIC] Zero-shot (1,000 source language examples)', '[ITALIC] Zero-shot (1,000 source language examples)', '[ITALIC] Zero-shot (1,000 source language examples)', '[ITALIC] Zero-shot (1,000 source language examples)', '[ITALIC] Zero-shot (1,000 source language ... | In the zero-shot setting, MultiBERT underperforms the comparison methods as the shared embedding space between many languages is overly restrictive. Our monolingual LMs outperform their cross-lingual teacher LASER in almost every setting. When fine-tuned with only 100 target language examples, they are able to outperfo... |
MultiFiT: Efficient Multi-lingual Language Model Fine-tuning | 1909.04761 | Table 3: Comparison of zero-shot, translation-based and supervised methods (with 2k training examples) on all domains of CLS. MT-BOW and CL-SCL results are from Zhou et al. (2016). | ['[EMPTY]', '[EMPTY]', 'de Books', 'de DVD', 'de Music', 'fr Books', 'fr DVD', 'fr Music', 'ja Books', 'ja DVD', 'ja Music'] | [['[ITALIC] Zero-shot', 'LASER, code', '84.15', '78.00', '79.15', '83.90', '83.40', '80.75', '74.99', '74.55', '76.30'], ['[ITALIC] Zero-shot', 'MultiBERT', '72.15', '70.05', '73.80', '75.50', '74.70', '76.05', '65.41', '64.90', '70.33'], ['[ITALIC] Zero-shot', 'MultiFiT, pseudo', '[BOLD] 89.60', '[BOLD] 81.80', '[BOLD... | Cls MultiFiT is able to outperform its zero-shot teacher LASER across all domains. Importantly, the bootstrapped monolingual model also outperforms more sophisticated models that are trained on translations across almost all domains. In the supervised setting, MultiFiT similarly outperforms multilingual BERT. For both ... |
MultiFiT: Efficient Multi-lingual Language Model Fine-tuning | 1909.04761 | Table 5: Comparison of MultiFiT results with different pretraining corpora and ULMFiT, fine-tuned with 1k labels on MLDoc. | ['[EMPTY]', 'de', 'es', 'zh'] | [['ULMFiT', '94.19', '95.23', '66.82'], ['MultiFiT, no wiki', '95.23', '95.07', '90.03'], ['MultiFiT, small Wiki', '95.37', '95.30', '89.80'], ['MultiFiT', '[BOLD] 95.90', '[BOLD] 96.07', '[BOLD] 92.52']] | Pretraining on more data generally helps. MultiFiT outperforms ULMFiT significantly; the performance improvement is particularly pronounced in Chinese where ULMFiT’s word-based tokenization underperformed. |
MultiFiT: Efficient Multi-lingual Language Model Fine-tuning | 1909.04761 | Table 7: Comparison of different tokenization strategies for different languages on MLDoc. | ['[EMPTY]', 'de', 'es', 'fr', 'it', 'ru'] | [['Word-based', '95.28', '95.97', '94.72', '89.97', '[BOLD] 88.02'], ['Subword', '[BOLD] 96.10', '[BOLD] 96.07', '[BOLD] 94.75', '[BOLD] 94.75', '87.65']] | Tokenization Subword tokenization has been found useful for language modeling with morphologically rich languages Czapla et al. We train models with the best performing vocabulary sizes for subword (15k) and regular word-based tokenization (60k) with the Moses tokenizer Koehn et al. Subword tokenization outperforms wor... |
SpeedRead: A Fast Named Entity Recognition Pipeline | 1301.2857 | Table 7: Confusion Matrix of the POS tags assigned by SpeedRead over the words of sections 22-24 of PTB. O represents all the other not mentioned tags. | ['[0pt][l]RefTest', 'DT', 'IN', 'JJ', 'NN', 'NNP', 'NNPS', 'NNS', 'RB', 'VBD', 'VBG', 'O'] | [['DT', '11094', '62', '3', '7', '3', '0', '0', '1', '0', '0', '13'], ['IN', '15', '13329', '9', '1', '0', '0', '0', '88', '0', '0', '50'], ['JJ', '1', '11', '7461', '[BOLD] 257', '130', '2', '10', '65', '38', '81', '159'], ['NN', '1', '5', '[BOLD] 288', '17196', '111', '0', '18', '11', '2', '109', '93'], ['NNP', '8', ... | Proper nouns are the second source of errors as most of the capitalized words will be mistakenly tagged as proper nouns while they are either adjectives or nouns. Such errors are the result of the weak logic implemented in the backoff tagger in SpeedRead, where regular expressions are applied in sequence returning the ... |
SpeedRead: A Fast Named Entity Recognition Pipeline | 1301.2857 | Table 8: F1 scores of the chunking phase using different POS tags. F1 score is calculated over tokens and not entities. | ['[100pt][l]PhaseDataset', 'Train', 'Dev', 'Test'] | [['SR+SR POS', '94.24', '94.49', '[BOLD] 93.12'], ['SR+Stanford POS L3W', '92.98', '93.37', '92.05'], ['SR+CONLL POS', '90.88', '90.82', '89.43'], ['SR+SENNA POS', '94.73', '95.07', '[BOLD] 93.80']] | This score is calculated over the chunking tags of the words. I and B tags are considered as one class while O is left as it is. that using better POS taggers does not necessarily produce better results. The quality of SpeedRead POS tagging is sufficient for the chunking stage. SENNA and SpeedRead POS taggers work bett... |
SpeedRead: A Fast Named Entity Recognition Pipeline | 1301.2857 | Table 10: F1 scores calculated using conlleval.pl script for NER taggers. The table shows that SpeedRead F1 score is 10% below the sate-of-art achieved by SENNA. | ['[80pt][l]PhaseDataset', 'Training', 'Dev', 'Test'] | [['SR+Gold Chunks', '90.80', '91.98', '87.87'], ['SpeeRead', '82.05', '83.35', '78.28'], ['Stanford', '99.28', '92.98', '89.03'], ['SENNA', '96.75', '97.24', '89.58']] | First row shows that, given chunked input, the classification phase is able to achieve close scores to the state-of- art classifiers. However, given the chunks generated by SpeedRead, the scores drop around 9.5% in F1 scores. |
SpeedRead: A Fast Named Entity Recognition Pipeline | 1301.2857 | Table 12: Speed of different NER taggers. SpeedRead is faster by 13.9 times using half the memory consumed by Stanford. | ['[BOLD] NER Tagger', '[BOLD] Token/Sec', '[BOLD] Relative', '[BOLD] Memory'] | [['[EMPTY]', '[EMPTY]', '[BOLD] Speed', 'MiB'], ['Stanford', '11,612', '1.00', '1900'], ['SENNA', '18,579', '2.13', '150'], ['SpeedRead', '153,194', '[BOLD] 13.9', '950']] | SENNA achieves close accuracy with twice the speed and less memory usage. SpeedRead takes another approach by focusing on speed. We are able to speed up the pipeline to the factor of 13. SpeedRead’s memory footprint is half the memory consumed by the Stanford pipeline. Even though SpeedRead’s accuracy is not close to t... |
Deep Speech Inpainting of Time-frequency Masks | 1910.09058 | Table 3: Blind speech inpainting (section 3.2). STOI & PESQ were computed between the recovered and the actual speech segments from validation set and averaged. The best scores were denoted in bold. PC - informed (partial conv.), FC - blind (full conv.) inpainting. | ['Intrusion', 'Size', 'Gaps STOI', 'Gaps PESQ', 'Noise STOI', 'Noise PESQ', 'PC - gaps STOI', 'PC - gaps PESQ', 'FC - gaps STOI', 'FC - gaps PESQ', 'FC - noise STOI', 'FC - noise PESQ', 'FC - additive STOI', 'FC - additive PESQ'] | [['Time', '10%', '0.893', '2.561', '0.901', '2.802', '[BOLD] 0.938', '[BOLD] 3.240', '0.930', '3.191', '0.919', '3.066', '0.933', '3.216'], ['Time', '20%', '0.772', '1.872', '0.800', '2.260', '0.887', '2.809', '0.875', '2.725', '0.863', '2.677', '[BOLD] 0.906', '[BOLD] 2.971'], ['Time', '30%', '0.641', '1.476', '0.688'... | All of the considered framework configurations successfully recovered missing or degraded parts of the input speech resulting in improved STOI and PESQ scores. The framework for the informed inpainting (PC-gaps) yielded better results, as compared to its blind counterpart, when the masked parts of the input were set to... |
Experiments with Universal CEFR Classification | 1804.06636 | Table 1: Composition of MERLIN Corpus | ['[BOLD] CEFR level', '[BOLD] DE', '[BOLD] IT', '[BOLD] CZ'] | [['A1', '57', '29', '0'], ['A2', '306', '381', '188'], ['B1', '331', '393', '165'], ['B2', '293', '0', '81'], ['C1', '42', '0', '0'], ['Total', '1029', '803', '434']] | To test our hypotheses, we need corpora graded with CEFR scale for multiple languages. One such multi-lingual corpus is the freely available MERLIN Boyd et al. This corpus consists of 2286 manually graded texts written by second language learners of German (DE), Italian (IT), and Czech (CZ) as a part of written examina... |
NRC-Canada at SMM4H Shared Task: Classifying Tweets Mentioning Adverse Drug Reactions and Medication Intake | 1805.04558 | Table 7: Task 2: Results of our best system (submission 1) on the test set when one of the feature groups is removed. | ['[BOLD] Submission', '[ITALIC] Pclass1+ [ITALIC] class2', '[ITALIC] Rclass1+ [ITALIC] class2', '[ITALIC] Fclass1+ [ITALIC] class2'] | [['a. submission 1 (all features)', '0.708', '0.642', '0.673'], ['b. all − general textual features', '0.697', '0.603', '0.647'], ['b.1. all − general [ITALIC] n-grams', '0.676', '0.673', '0.674'], ['b.2. all − general embeddings', '0.709', '0.638', '0.671'], ['b.3. all − general clusters', '0.685', '0.671', '0.678'],... | In this task, the general textual features (row b) played a bigger role in the overall performance than the domain-specific (row c) or sentiment lexicon (row d) features. Removing this group of features results in more than 2.5 percentage points drop in the F-measure affecting both precision and recall (row b). However... |
NRC-Canada at SMM4H Shared Task: Classifying Tweets Mentioning Adverse Drug Reactions and Medication Intake | 1805.04558 | Table 4: Task 1: Results for our three official submissions, baselines, and top three teams. Evaluation measures for Task 1 are precision (P), recall (R), and F1-measure (F) for class 1 (ADR). | ['[BOLD] Submission', '[ITALIC] Pclass1', '[ITALIC] Rclass1', '[ITALIC] Fclass1'] | [['[ITALIC] a. Baselines', '[EMPTY]', '[EMPTY]', '[EMPTY]'], ['a.1. Assigning class 1 (ADR) to all instances', '0.077', '1.000', '0.143'], ['a.2. SVM-unigrams', '0.391', '0.298', '0.339'], ['[ITALIC] b. Top 3 teams in the shared task', '[EMPTY]', '[EMPTY]', '[EMPTY]'], ['b.1. NRC-Canada', '0.392', '0.488', '0.435'], ['... | The best results in Fclass1 were obtained with submission 1 (row c.1). The results for submission 2 are the lowest, with F-measure being 3.5 percentage points lower than the result for submission 1 (row c.2). The ensemble classifier (submission 3) shows a slightly worse performance than the best result. However, in the... |
NRC-Canada at SMM4H Shared Task: Classifying Tweets Mentioning Adverse Drug Reactions and Medication Intake | 1805.04558 | Table 5: Task 1: Results of our best system (submission 1) on the test set when one of the feature groups is removed. | ['[BOLD] Submission', '[ITALIC] Pclass1', '[ITALIC] Rclass1', '[ITALIC] Fclass1'] | [['a. submission 1 (all features)', '0.392', '0.488', '0.435'], ['b. all − general textual features', '0.390', '0.444', '0.415'], ['b.1. all − general [ITALIC] n-grams', '0.397', '0.484', '0.436'], ['b.2. all − general embeddings', '0.365', '0.480', '0.414'], ['b.3. all − general clusters', '0.383', '0.498', '0.433'],... | To investigate the impact of each feature group on the overall performance, we conduct ablation experiments where we repeat the same classification process but remove one feature group at a time. Comparing the two major groups of features, general textual features (row b) and domain-specific features (row c), we observ... |
NRC-Canada at SMM4H Shared Task: Classifying Tweets Mentioning Adverse Drug Reactions and Medication Intake | 1805.04558 | Table 6: Task 2: Results for our three official submissions, baselines, and top three teams. Evaluation measures for Task 2 are micro-averaged P, R, and F1-score for class 1 (intake) and class 2 (possible intake). | ['[BOLD] Submission', '[ITALIC] Pclass1+ [ITALIC] class2', '[ITALIC] Rclass1+ [ITALIC] class2', '[ITALIC] Fclass1+ [ITALIC] class2'] | [['[ITALIC] a. Baselines', '[EMPTY]', '[EMPTY]', '[EMPTY]'], ['a.1. Assigning class 2 to all instances', '0.359', '0.609', '0.452'], ['a.2. SVM-unigrams', '0.680', '0.616', '0.646'], ['[ITALIC] b. Top 3 teams in the shared task', '[EMPTY]', '[EMPTY]', '[EMPTY]'], ['b.1. InfyNLP', '0.725', '0.664', '0.693'], ['b.2. UKNL... | The best results in Fclass1+class2 are achieved with submission 1 (row c.1). The results for the other two submissions, submission 2 and submission 3, are quite similar to the results of submission 1 in both precision and recall (rows c.2–c.3). Adding the features from the ADR lexicon and the Pronoun lexicon did not re... |
Evaluating Semantic Parsing against a Simple Web-based Question Answering Model | 1707.04412 | Table 4: Feature ablation results. The five features that lead to largest drop in performance are displayed. | ['[BOLD] Feature Template', '[BOLD] F1', 'Δ'] | [['WebQA', '53.6', '[EMPTY]'], ['- Max-NE', '51.8', '-1.8'], ['- Ne+Common', '51.8', '-1.8'], ['- Google Rank', '51.4', '-2.2'], ['- In Quest', '50.1', '-3.5'], ['- TF-IDF', '41.5', '-12']] | Note that TF-IDF is by far the most impactful feature, leading to a large drop of 12 points in performance. This shows the importance of using the redundancy of the web for our QA system. |
Evaluating Semantic Parsing against a Simple Web-based Question Answering Model | 1707.04412 | Table 3: Results on development (average over random splits) and test set. Middle: results on all examples. Bottom: results on the subset where candidate extraction succeeded. | ['[BOLD] System', '[BOLD] Dev [BOLD] F1', '[BOLD] Dev [BOLD] p@1', '[BOLD] Test [BOLD] F1', '[BOLD] Test [BOLD] p@1', '[BOLD] Test [BOLD] MRR'] | [['STAGG', '-', '-', '37.7', '-', '-'], ['CompQ', '-', '-', '[BOLD] 40.9', '-', '-'], ['WebQA', '35.3', '36.4', '32.6', '33.5', '42.4'], ['WebQA-extrapol', '-', '-', '34.4', '-', '-'], ['CompQ-Subset', '-', '-', '48.5', '-', '-'], ['WebQA-Subset', '53.6', '55.1', '51.9', '53.4', '67.5']] | WebQA obtained 32.6 F1 (33.5 p@1, 42.4 MRR) compared to 40.9 F1 of CompQ. Our candidate extraction step finds the correct answer in the top-K candidates in 65.9% of development examples and 62.7% of test examples. Thus, our test F1 on examples for which candidate extraction succeeded (WebQA-Subset) is 51.9 (53.4 p@1, 6... |
CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases | 1909.05378 | Table 5: Performance of various methods over all questions (question match) and all interactions (interaction match). | ['Model', 'Question Match', 'Question Match', 'Interaction Match', 'Interaction Match'] | [['[EMPTY]', 'Dev', 'Test', 'Dev', 'Test'], ['CD-Seq2Seq', '13.8', '13.9', '2.1', '2.6'], ['SyntaxSQL-con', '15.1', '14.1', '2.7', '2.2']] | We use the same evaluation metrics used by the SParC dataset Yu et al. The two models achieve less than 16% question-level accuracy and less than 3% on interaction-level accuracy. Since the two models have been benchmarked on both CoSQL and SParC, we cross-compare their performance on these two datasets. Both models pe... |
CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases | 1909.05378 | Table 6: BLEU scores on the development and test sets, and human evaluations of logic correctness rate (LCR) and grammar check on the 100 examples randomly sampled from the test set. | ['Model', 'BLEU', 'BLEU', 'LCR (%)', 'Grammar'] | [['[EMPTY]', 'Dev', 'Test', 'Test', 'Test'], ['Template', '9.5', '9.3', '41.0', '4.0'], ['Seq2Seq', '15.3', '14.1', '27.0', '3.5'], ['Pointer-generator', '16.4', '15.1', '35.0', '3.6']] | et al. To compute LCR and grammar score, we randomly sampled 100 descriptions generated by each model. Three students proficient in English participated in the evaluation, They were asked to choose a score 0 or 1 for LCR, and 1 to 5 for grammar check (the larger, the better). For LCR, the final score was decided by maj... |
CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases | 1909.05378 | Table 7: Accuracy of user dialog act prediction on the development and test sets. | ['Model', 'Dev', 'Test'] | [['Majority', '63.3', '62.8'], ['TBCNN-pair', '84.2', '83.9']] | The result of Majority indicates that about 40% of user questions cannot be directly converted into SQL queries. This confirms the necessity of considering a larger set of dialogue actions for building a practical NLIDB system. Even though TBCNN can predict around 85% of user intents correctly, most of the correct pred... |
End-to-End Abstractive Summarization for Meetings | 2004.02016 | Table 3: Ablation results of HMNet on AMI’s test set. “+role text” means the role vector is not used, but the role name is prepended to each turn’s transcript. | ['Model', 'ROUGE-1', 'R-2', 'R-SU4'] | [['HMNet', '[BOLD] 52.1', '[BOLD] 19.7', '[BOLD] 24.1'], ['−POS&ENT', '49.3', '18.8', '23.5'], ['−role vector', '47.8', '17.2', '21.7'], ['+role text', '47.4', '18.8', '23.7'], ['−hierarchy', '45.1', '15.9', '20.5']] | Ablation Study. We conduct an ablation study of HMNet to verify the effectiveness of its various components. When the role vector is removed, the ROUGE-1 score drops 4.3 points. The “+role text” setting removes the role vector. Instead, it prepends the role name to each turn’s transcript. Its performance is higher than... |
End-to-End Abstractive Summarization for Meetings | 2004.02016 | Table 2: ROUGE-1, ROUGE-2, ROUGE-SU4 scores of generated summary in AMI and ICSI datasets. Numbers in bold are the overall best result. Numbers with underscore are the best result from previous literature. ∗ The two baseline MM models require additional human annotations of topic segmentation and visual signals from ca... | ['Model', 'AMI ROUGE-1', 'AMI R-2', 'AMI R-SU4', 'ICSI ROUGE-1', 'ICSI R-2', 'ICSI R-SU4'] | [['Random', '35.13', '6.26', '13.17', '29.28', '3.78', '10.29'], ['Longest Greedy', '33.35', '5.11', '12.15', '30.23', '4.27', '10.90'], ['Template', '31.50', '6.80', '11.40', '/', '/', '/'], ['CoreRank Submodular', '36.13', '7.33', '14.18', '29.82', '4.00', '10.61'], ['PageRank Submodular', '36.1', '7.42', '14.32', '3... | As shown, except for ROUGE-1 in AMI, HMNet outperforms all baseline models in all metrics, and the result is statistically significant at level 0.05, under paired t-test with the best baseline results. On ICSI dataset, HMNet achieves 7.51, 3.06 and 5.44 higher ROUGE points than previous best results. |
Uncertainty in Neural Network Word Embedding Exploration of Threshold for Similarity | 1606.06086 | Table 3: In each cell, the top value shows the result of the potential threshold and the bottom reports the optimal value (shown as - when equal to our threshold value). † indicates a significant difference to the baseline. There is no significance difference between the results of the optimal value and our threshold. | ['[BOLD] Collection', '[BOLD] 100 (0.81) [BOLD] MAP', '[BOLD] 100 (0.81) [BOLD] NDCG', '[BOLD] 200 (0.74) [BOLD] MAP', '[BOLD] 200 (0.74) [BOLD] NDCG', '[BOLD] 300 (0.69) [BOLD] MAP', '[BOLD] 300 (0.69) [BOLD] NDCG', '[BOLD] 400 (0.65) [BOLD] MAP', '[BOLD] 400 (0.65) [BOLD] NDCG'] | [['TREC-6', '0.273†', '0.432', '0.274†', '0.441', '0.277†', '0.439', '0.275†', '0.442'], ['TREC-6', '-', '0.436', '-', '-', '-', '0.442', '0.278†', '0.447'], ['TREC-7', '0.211', '0.377', '0.217†', '0.390', '0.214', '0.386', '0.215', '0.386'], ['TREC-7', '0.212', '0.395', '-', '0.399', '-', '0.395', '-', '0.400'], ['TRE... | For each dimension our threshold and its confidence interval are shown with vertical lines. Significant differences of the results to the baseline are marked on the plots using the † symbol. |
Uncertainty in Neural Network Word Embedding Exploration of Threshold for Similarity | 1606.06086 | Table 1: Potential thresholds | ['Dimensionality', 'Threshold Boundaries Lower', 'Threshold Boundaries [BOLD] Main', 'Threshold Boundaries Upper'] | [['100', '0.802', '[BOLD] 0.818', '0.829'], ['200', '0.737', '[BOLD] 0.756', '0.767'], ['300', '0.692', '[BOLD] 0.708', '0.726'], ['400', '0.655', '[BOLD] 0.675', '0.693']] | We also consider an upper and lower bound for this threshold based on the points that the confident intervals cross the approximated mean. |
Generative Pre-training for Speech with Autoregressive Predictive Coding | 1910.12607 | Table 2: ASR WER results with varying amounts of training data randomly sampled from si284. Feature extractors pre-trained with just 10 hours of LibriSpeech audio are denoted with a subscript 10. | ['Features', 'Proportion of si284 1', 'Proportion of si284 1/2', 'Proportion of si284 1/4', 'Proportion of si284 1/8', 'Proportion of si284 1/16', 'Proportion of si284 1/32'] | [['log Mel', '18.3', '24.1', '33.4', '44.6', '66.4', '87.7'], ['CPC', '20.7', '28.3', '38.8', '50.9', '69.7', '88.1'], ['R-APC', '15.2', '18.3', '24.6', '35.8', '49.0', '66.8'], ['T-APC', '13.7', '16.4', '21.3', '31.4', '43.0', '63.2'], ['PASE10', '20.8', '26.6', '32.8', '42.1', '58.8', '78.6'], ['CPC10', '23.4', '30.0... | For example, 1/16 means that we take only 72×1/16=4.5 hours from si284 for training. We find that for all input features, there is a significant increase in WER whenever the training size is reduced by half. When comparing R-APC and T-APC with log Mel, we see the former two always outperform the latter across all propo... |
Generative Pre-training for Speech with Autoregressive Predictive Coding | 1910.12607 | Table 1: ASR results (WER ↓) of APC with varying n during pre-training and different transfer learning approaches (Frozen vs. Finetuned). log Mel is the baseline that uses log Mel spectrograms as input features. The best transfer learning result is marked in bold. | ['Features', '[ITALIC] n 1', '[ITALIC] n 2', '[ITALIC] n 3', '[ITALIC] n 5', '[ITALIC] n 10', '[ITALIC] n 20'] | [['log Mel', '18.3', '18.3', '18.3', '18.3', '18.3', '18.3'], ['R-APC Scratch', '23.2', '23.2', '23.2', '23.2', '23.2', '23.2'], ['R-APC Frozen', '17.2', '15.8', '15.2', '16.3', '17.8', '20.9'], ['R-APC Finetuned', '18.2', '17.6', '16.9', '18.2', '19.7', '21.7'], ['T-APC Scratch', '25.0', '25.0', '25.0', '25.0', '25.0'... | We also include the case where APC is randomly initialized and trained from scratch along with the seq2seq model. We think this is because for a small n, APC can exploit local smoothness in the spectrograms for predicting the target future frame (since xk can be very similar to xk+n when n is small) and thus does not n... |
Generative Pre-training for Speech with Autoregressive Predictive Coding | 1910.12607 | Table 3: ASR WER results using different numbers of GRU layers for the encoder in the ASR seq2seq model. | ['Features', 'Number of encoder layers 1', 'Number of encoder layers 2', 'Number of encoder layers 3', 'Number of encoder layers 4'] | [['log Mel', '28.8', '23.5', '20.8', '18.3'], ['CPC', '34.3', '29.8', '25.2', '23.7'], ['R-APC', '26.2', '20.3', '17.6', '15.2'], ['T-APC', '25.2', '18.6', '15.8', '13.7'], ['PASE10', '29.4', '25.7', '22.5', '20.8'], ['CPC10', '35.8', '31.3', '26.0', '24.4'], ['R-APC10', '27.6', '22.3', '19.6', '17.6'], ['T-APC10', '28... | The next aspect we examine is to what extent can we reduce the downstream model size with transfer learning. We see that when using the same number of layers, * -APC and *-APC10 always outperform other features. It is noteworthy that T-APC with just 2 layers performs similar to log Mel using 4 layers (18.6 vs. 18.3), w... |
Generative Pre-training for Speech with Autoregressive Predictive Coding | 1910.12607 | Table 4: Speech translation results. BLEU scores (↑) are reported. We also include the results of the cascaded system (ASR + MT) reported in [28] and the S-Transformer model reported in [29]. Only the results on the test set are available for these two approaches. | ['Methods', 'dev', 'test'] | [['Cascaded', '-', '14.6'], ['S-Transformer', '-', '13.8'], ['log Mel', '12.5', '12.9'], ['CPC', '12.1', '12.5'], ['R-APC', '13.5', '13.8'], ['T-APC', '13.7', '14.3'], ['PASE10', '12.0', '12.4'], ['CPC10', '11.8', '12.3'], ['R-APC10', '13.2', '13.7'], ['T-APC10', '12.8', '13.4']] | Besides, our RNN-based model with T-APC features (14.3) outperforms S-Transformer (13.8), and is comparable with the cascaded system (14.6). |
This before That:Causal Precedence in the Biomedical Domain | 1606.08089 | Table 4: Results of all proposed causal models, using stratified 10-fold cross-validation. The combined system is a sieve-based architecture that applies the models in decreasing order of their precision. The combined system significantly outperforms the best single model, SVM with L1 regularization, according to a boo... | ['[ITALIC] Model', '[ITALIC] p', '[ITALIC] r', '[ITALIC] f1'] | [['Intra-sentence', '0.5', '0.01', '0.01'], ['Inter-sentence', '0.5', '0.01', '0.01'], ['Reichenbach', '0', '0', '0'], ['LR+L1', '0.58', '0.32', '0.41'], ['LR+L2', '0.65', '0.26', '0.37'], ['SVM+L1', '0.54', '0.35', '[BOLD] 0.43'], ['SVM+L2', '0.54', '0.29', '0.38'], ['RF', '0.62', '0.25', '0.36'], ['LSTM', '0.40', '0.... | We report results using micro precision, recall, and F1 scores for each model. With fewer than 200 instances of causal precedence occurring in 1000 annotations, training and testing for both the feature-based classifiers and latent feature models was performed using stratified 10-fold cross validation. Weight updates w... |
Sentiment Tagging with Partial Labels using Modular Architectures | 1906.00534 | Table 5: Comparing our models with several state-of-the-art systems on the CoNLL 2003 English NER dataset. | ['Model', 'English'] | [['LSTM-CRF\xa0Lample et\xa0al. ( 2016 )', '90.94'], ['LSTM-CNN-CRF\xa0Ma and Hovy ( 2016 )', '91.21'], ['LM-LSTM-CRF\xa0Liu et\xa0al. ( 2018 )', '91.06'], ['LSTM-CRF-T', '90.8'], ['LSTM-CRF-TI', '91.16'], ['LSTM-CRF-TI(g)', '[BOLD] 91.68']] | A3SS0SSS0Px2 Results on NER For Dutch and Spanish, we used cross-lingual embedding as a way to exploit lexical information. The results are shown in Tab. Our best-performing model outperform all the competing systems. |
Sentiment Tagging with Partial Labels using Modular Architectures | 1906.00534 | Table 2: Comparing our models with recent results on the Aspect Sentiment datasets. | ['Models', 'English E+A', 'English E+A+S', 'Spanish E+A', 'Spanish E+A+S', 'Dutch E+A', 'Dutch E+A+S', 'Russian E+A', 'Russian E+A+S'] | [['LSTM-CNN-CRFMa and Hovy ( 2016 )', '58.73', '44.20', '64.32', '50.34', '51.62', '36.88', '58.88', '38.13'], ['LSTM-CRF-LMLiu et\xa0al. ( 2018 )', '62.27', '45.04', '63.63', '50.15', '51.78', '34.77', '62.18', '38.80'], ['LSTM-CRF', '59.11', '48.67', '62.98', '52.10', '51.35', '37.30', '63.41', '42.47'], ['LSTM-CRF-T... | S6SS2SSS0Px2 Aspect Based Sentiment We evaluated our models on two tasks: The first uses two modules, for identifying the position of the aspect in the text (i.e., chunking) and the aspect category prediction (denoted E+A). The second adds a third module that predicts the sentiment polarity associated with the aspect (... |
Sentiment Tagging with Partial Labels using Modular Architectures | 1906.00534 | Table 4: Performance on the target sentiment task | ['System', 'Architecture', 'English Pre', 'English Rec', 'English F1', 'Spanish Pre', 'Spanish Rec', 'Spanish F1'] | [['Zhang, Zhang and Vo (2015)', 'Pipeline', '43.71', '37.12', '40.06', '45.99', '40.57', '43.04'], ['Zhang, Zhang and Vo (2015)', 'Joint', '44.62', '35.84', '39.67', '46.67', '39.99', '43.02'], ['Zhang, Zhang and Vo (2015)', 'Collapsed', '46.32', '32.84', '38.36', '47.69', '34.53', '40.00'], ['Li and Lu (2017)', 'SS', ... | The complete results of our experiments on the target sentiment task are summarized in Tab. Our LSTM-CRF-TI(g) model outperforms all the other competing models in Precision, Recall and the F1 score. |
Sentiment Tagging with Partial Labels using Modular Architectures | 1906.00534 | Table 6: Comparing our models with recent results on the 2002 CoNLL Dutch and Spanish NER datasets. | ['Model', 'Dutch', 'Spanish'] | [['Carreras et\xa0al. Carreras et\xa0al. ( 2002 )', '77.05', '81.39'], ['Nothman et\xa0al. Nothman et\xa0al. ( 2013 )', '78.60', '[EMPTY]'], ['dos Santos and Guimarães dos Santos and Guimarães ( 2015 )', '[EMPTY]', '82.21'], ['Gillick et\xa0al. Gillick et\xa0al. ( 2015 )', '82.84', '82.95'], ['Lample et\xa0al. Lam... | A3SS0SSS0Px2 Results on NER For Dutch and Spanish, we used cross-lingual embedding as a way to exploit lexical information. The results are shown in Tab. Our best-performing model outperform all the competing systems. |
Argumentation Mining in User-Generated Web Discourse | 1601.02403 | Table 11: Results of classification of argument components in the cross-domain scenario. Macro-F1 scores reported, bold numbers denote the best results. HS – homeschooling, MS – mainstreaming, PIS – prayer in schools, PPS – private vs. public schools, RS – redshirting, SSE – single sex education. Results in the aggrega... | ['Domain', '[BOLD] Feature set combinations 0', '[BOLD] Feature set combinations 01', '[BOLD] Feature set combinations 012', '[BOLD] Feature set combinations 0123', '[BOLD] Feature set combinations 01234', '[BOLD] Feature set combinations 1234', '[BOLD] Feature set combinations 234', '[BOLD] Feature set combinations 34... | [['[BOLD] HS', '0.087', '0.063', '0.044', '0.106', '0.072', '0.075', '0.065', '0.063', '[BOLD] 0.197'], ['[BOLD] MS', '0.072', '0.060', '0.070', '0.058', '0.038', '0.062', '0.045', '0.060', '[BOLD] 0.188'], ['[BOLD] PIS', '0.078', '0.073', '0.083', '0.074', '0.086', '0.073', '0.096', '0.081', '[BOLD] 0.166'], ['[BOLD] ... | However, using only feature set 4 (embeddings), the system performance increases rapidly, so it is even comparable to numbers achieved in the in-domain scenario. These results indicate that embedding features generalize well across domains in our task of argument component identification. |
Argumentation Mining in User-Generated Web Discourse | 1601.02403 | Table 5: Correlations between αU and various measures on different data sub-sets. SC – full sentence coverage; DL – document length; APL – average paragraph length; ASL = average sentence length; ARI, C-L (Coleman-Liau), Flesch, LIX – readability measures. Bold numbers denote statistically significant correlation (p<0.... | ['[EMPTY]', '[BOLD] SC', '[BOLD] DL', '[BOLD] APL', '[BOLD] ASL', '[BOLD] ARI', '[BOLD] C-L', '[BOLD] Flesch', '[BOLD] LIX'] | [['all data', '-0.14', '-0.14', '0.01', '0.04', '0.07', '0.08', '-0.11', '0.07'], ['comments', '-0.17', '[BOLD] -0.64', '0.13', '0.01', '0.01', '0.01', '-0.11', '0.01'], ['forum posts', '-0.08', '-0.03', '-0.08', '-0.03', '0.08', '0.24', '-0.17', '0.20'], ['blog posts', '-0.50', '0.21', '[BOLD] -0.81', '-0.61', '-0.39'... | We observed the following statistically significant (p<0.05) correlations. First, document length negatively correlates with agreement in comments. The longer the comment was the lower the agreement was. Second, average paragraph length negatively correlates with agreement in blog posts. The longer the paragraphs in bl... |
Argumentation Mining in User-Generated Web Discourse | 1601.02403 | Table 8: Class distribution of the gold data Toulmin corpus approximated to the sentence level boundaries. | ['[BOLD] Class', '[BOLD] Sentences in data [BOLD] Relative (%)', '[BOLD] Sentences in data [BOLD] Absolute', '[BOLD] Class', '[BOLD] Sentences in data [BOLD] Relative (%)', '[BOLD] Sentences in data [BOLD] Absolute'] | [['Backing-B', '5.6', '220', 'Premise-I', '8.6', '336'], ['Backing-I', '7.2', '281', 'Rebuttal-B', '1.6', '61'], ['Claim-B', '4.4', '171', 'Rebuttal-I', '0.9', '37'], ['Claim-I', '0.4', '16', 'Refutation-B', '0.5', '18'], ['O', '56.8', '2214', 'Refutation-I', '0.4', '15'], ['Premise-B', '13.6', '530', '[BOLD] Total', '... | The little presence of rebuttal and refutation (4 classes account only for 3.4% of the data) makes this dataset very unbalanced. The overall best performance (Macro-F1 0.251) was achieved using the rich feature sets (01234 and 234) and significantly outperformed the baseline as well as other feature sets. Classificatio... |
Latent Alignment and Variational Attention | 1807.03756 | Table 3: (Left) Comparison against the best prior work for NMT on the IWSLT 2014 German-English test set. (Upper Right) Comparison of inference alternatives of variational attention on IWSLT 2014. (Lower Right) Comparison of different models in terms of implied discrete entropy (lower = more certain alignment). | ['Inference Method', '#Samples', 'PPL', 'BLEU'] | [['REINFORCE', '1', '6.17', '33.30'], ['RWS', '5', '6.41', '32.96'], ['Gumbel-Softmax', '1', '6.51', '33.08']] | For NMT, on the IWSLT 2014 German-English task, variational attention with enumeration and sampling performs comparably to optimizing the log marginal likelihood, despite the fact that it is optimizing a lower bound. We believe that this is due to the use of q(z), which conditions on the entire source/target and theref... |
Latent Alignment and Variational Attention | 1807.03756 | Table 1: Evaluation on NMT and VQA for the various models. E column indicates whether the expectation is calculated via enumeration (Enum) or a single sample (Sample) during training. For NMT we evaluate intrinsically on perplexity (PPL) (lower is better) and extrinsically on BLEU (higher is better), where for BLEU we ... | ['Model', 'Objective', 'E', 'NMT PPL', 'NMT BLEU', 'VQA NLL', 'VQA Eval'] | [['Soft Attention', 'log [ITALIC] p( [ITALIC] y|E[ [ITALIC] z])', '-', '7.17', '32.77', '1.76', '58.93'], ['Marginal Likelihood', 'logE[ [ITALIC] p]', 'Enum', '6.34', '33.29', '1.69', '60.33'], ['Hard Attention', 'E [ITALIC] p[log [ITALIC] p]', 'Enum', '7.37', '31.40', '1.78', '57.60'], ['Hard Attention', 'E [ITALIC] p... | We first note that hard attention underperforms soft attention, even when its expectation is enumerated. This indicates that Jensen’s inequality alone is a poor bound. On the other hand, on both experiments, exact marginal likelihood outperforms soft attention, indicating that when possible it is better to have latent ... |
Latent Alignment and Variational Attention | 1807.03756 | Table 2: (Left) Performance change on NMT from exact decoding to K-Max decoding with K=5. (see section 5 for definition of K-max decoding). (Right) Test perplexity of different approaches while varying K to estimate Ez[p(y|x,~x)]. Dotted lines compare soft baseline and variational with full enumeration. | ['Model', 'PPL Exact', 'PPL [ITALIC] K-Max', 'BLEU Exact', 'BLEU [ITALIC] K-Max'] | [['Marginal Likelihood', '6.34', '6.90', '33.29', '33.31'], ['Hard + Enum', '7.37', '7.37', '31.40', '31.37'], ['Hard + Sample', '7.38', '7.38', '31.00', '31.04'], ['Variational + Enum', '6.08', '6.42', '33.68', '33.69'], ['Variational + Sample', '6.17', '6.51', '33.30', '33.27']] | For all methods exact enumeration is better, however K-max is a reasonable approximation. This possibly indicates that soft attention models are approximating latent alignment models. On the other hand, training with latent alignments and testing with soft attention performed badly. |
Latent Alignment and Variational Attention | 1807.03756 | Table 3: (Left) Comparison against the best prior work for NMT on the IWSLT 2014 German-English test set. (Upper Right) Comparison of inference alternatives of variational attention on IWSLT 2014. (Lower Right) Comparison of different models in terms of implied discrete entropy (lower = more certain alignment). | ['[EMPTY]', 'IWSLT'] | [['Model', 'BLEU'], ['Beam Search Optimization Wiseman2016 ', '26.36'], ['Actor-Critic Bahdanau2017 ', '28.53'], ['Neural PBMT + LM Huang2018 ', '30.08'], ['Minimum Risk Training Edunov2017 ', '32.84'], ['Soft Attention', '32.77'], ['Marginal Likelihood', '33.29'], ['Hard Attention + Enum', '31.40'], ['Hard Attenti... | For NMT, on the IWSLT 2014 German-English task, variational attention with enumeration and sampling performs comparably to optimizing the log marginal likelihood, despite the fact that it is optimizing a lower bound. We believe that this is due to the use of q(z), which conditions on the entire source/target and theref... |
Latent Alignment and Variational Attention | 1807.03756 | Table 3: (Left) Comparison against the best prior work for NMT on the IWSLT 2014 German-English test set. (Upper Right) Comparison of inference alternatives of variational attention on IWSLT 2014. (Lower Right) Comparison of different models in terms of implied discrete entropy (lower = more certain alignment). | ['Model', 'Entropy NMT', 'Entropy VQA'] | [['Soft Attention', '1.24', '2.70'], ['Marginal Likelihood', '0.82', '2.66'], ['Hard Attention + Enum', '0.05', '0.73'], ['Hard Attention + Sample', '0.07', '0.58'], ['Variational Relaxed Attention', '2.02', '-'], ['Variational Attention + Enum', '0.54', '2.07'], ['Variational Attention + Sample', '0.52', '2.44']] | For NMT, on the IWSLT 2014 German-English task, variational attention with enumeration and sampling performs comparably to optimizing the log marginal likelihood, despite the fact that it is optimizing a lower bound. We believe that this is due to the use of q(z), which conditions on the entire source/target and theref... |
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