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SyGNS: A Systematic Generalization Testbed Based on Natural Language Semantics ...
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2
Summarize-then-Answer: Generating Concise Explanations for Multi-hop Reading Comprehension ...
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3
SHAPE: Shifted Absolute Position Embedding for Transformers ...
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4
Incorporating Residual and Normalization Layers into Analysis of Masked Language Models ...
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5
Pseudo Zero Pronoun Resolution Improves Zero Anaphora Resolution ...
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6
Exploring Methods for Generating Feedback Comments for Writing Learning ...
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7
Transformer-based Lexically Constrained Headline Generation ...
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8
Transformer-based Lexically Constrained Headline Generation ...
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9
Topicalization in Language Models: A Case Study on Japanese ...
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10
Lower Perplexity is Not Always Human-Like ...
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11
Lower Perplexity is Not Always Human-Like ...
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12
An Empirical Study of Contextual Data Augmentation for Japanese Zero Anaphora Resolution ...
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13
PheMT: A Phenomenon-wise Dataset for Machine Translation Robustness on User-Generated Contents ...
Fujii, Ryo; Mita, Masato; Abe, Kaori. - : arXiv, 2020
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14
Seeing the world through text: Evaluating image descriptions for commonsense reasoning in machine reading comprehension ...
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15
Language Models as an Alternative Evaluator of Word Order Hypotheses: A Case Study in Japanese ...
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16
Encoder-Decoder Models Can Benefit from Pre-trained Masked Language Models in Grammatical Error Correction ...
Abstract: This paper investigates how to effectively incorporate a pre-trained masked language model (MLM), such as BERT, into an encoder-decoder (EncDec) model for grammatical error correction (GEC). The answer to this question is not as straightforward as one might expect because the previous common methods for incorporating a MLM into an EncDec model have potential drawbacks when applied to GEC. For example, the distribution of the inputs to a GEC model can be considerably different (erroneous, clumsy, etc.) from that of the corpora used for pre-training MLMs; however, this issue is not addressed in the previous methods. Our experiments show that our proposed method, where we first fine-tune a MLM with a given GEC corpus and then use the output of the fine-tuned MLM as additional features in the GEC model, maximizes the benefit of the MLM. The best-performing model achieves state-of-the-art performances on the BEA-2019 and CoNLL-2014 benchmarks. Our code is publicly available at: ... : Accepted as a short paper to the 58th Annual Conference of the Association for Computational Linguistics (ACL-2020) ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://arxiv.org/abs/2005.00987
https://dx.doi.org/10.48550/arxiv.2005.00987
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17
Attention is Not Only a Weight: Analyzing Transformers with Vector Norms ...
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18
Filtering Noisy Dialogue Corpora by Connectivity and Content Relatedness ...
Akama, Reina; Yokoi, Sho; Suzuki, Jun. - : arXiv, 2020
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19
Modeling Event Salience in Narratives via Barthes' Cardinal Functions ...
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20
Do Neural Models Learn Systematicity of Monotonicity Inference in Natural Language? ...
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