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SyGNS: A Systematic Generalization Testbed Based on Natural Language Semantics ...
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Summarize-then-Answer: Generating Concise Explanations for Multi-hop Reading Comprehension ...
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SHAPE: Shifted Absolute Position Embedding for Transformers ...
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Incorporating Residual and Normalization Layers into Analysis of Masked Language Models ...
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Pseudo Zero Pronoun Resolution Improves Zero Anaphora Resolution ...
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Exploring Methods for Generating Feedback Comments for Writing Learning ...
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Transformer-based Lexically Constrained Headline Generation ...
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Transformer-based Lexically Constrained Headline Generation ...
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Topicalization in Language Models: A Case Study on Japanese ...
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An Empirical Study of Contextual Data Augmentation for Japanese Zero Anaphora Resolution ...
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PheMT: A Phenomenon-wise Dataset for Machine Translation Robustness on User-Generated Contents ...
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Seeing the world through text: Evaluating image descriptions for commonsense reasoning in machine reading comprehension ...
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Language Models as an Alternative Evaluator of Word Order Hypotheses: A Case Study in Japanese ...
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Encoder-Decoder Models Can Benefit from Pre-trained Masked Language Models in Grammatical Error Correction ...
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Attention is Not Only a Weight: Analyzing Transformers with Vector Norms ...
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Filtering Noisy Dialogue Corpora by Connectivity and Content Relatedness ...
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Abstract:
Large-scale dialogue datasets have recently become available for training neural dialogue agents. However, these datasets have been reported to contain a non-negligible number of unacceptable utterance pairs. In this paper, we propose a method for scoring the quality of utterance pairs in terms of their connectivity and relatedness. The proposed scoring method is designed based on findings widely shared in the dialogue and linguistics research communities. We demonstrate that it has a relatively good correlation with the human judgment of dialogue quality. Furthermore, the method is applied to filter out potentially unacceptable utterance pairs from a large-scale noisy dialogue corpus to ensure its quality. We experimentally confirm that training data filtered by the proposed method improves the quality of neural dialogue agents in response generation. ... : 18 pages, Accepted at The 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020) ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/2004.14008 https://dx.doi.org/10.48550/arxiv.2004.14008
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Modeling Event Salience in Narratives via Barthes' Cardinal Functions ...
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Do Neural Models Learn Systematicity of Monotonicity Inference in Natural Language? ...
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