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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 9 of 9 1 An Empirical Study of Contextual Data Augmentation for Japanese Zero Anaphora Resolution ... Konno, Ryuto; Matsubayashi, Yuichiroh; Kiyono, Shun. - : arXiv, 2020 BASE Show details 2 PheMT: A Phenomenon-wise Dataset for Machine Translation Robustness on User-Generated Contents ... Fujii, Ryo; Mita, Masato; Abe, Kaori. - : arXiv, 2020 BASE Show details 3 Seeing the world through text: Evaluating image descriptions for commonsense reasoning in machine reading comprehension ... The 28th International Conference on Computational Linguistics 2020; Galvan-Sosa, Diana; Inui, Kentaro. - : Underline Science Inc., 2020 BASE Show details 4 Language Models as an Alternative Evaluator of Word Order Hypotheses: A Case Study in Japanese ... Kuribayashi, Tatsuki; Ito, Takumi; Suzuki, Jun. - : arXiv, 2020 BASE Show details 5 Encoder-Decoder Models Can Benefit from Pre-trained Masked Language Models in Grammatical Error Correction ... Kaneko, Masahiro; Mita, Masato; Kiyono, Shun. - : arXiv, 2020 BASE Show details 6 Attention is Not Only a Weight: Analyzing Transformers with Vector Norms ... Kobayashi, Goro; Kuribayashi, Tatsuki; Yokoi, Sho. - : arXiv, 2020 BASE Show details 7 Filtering Noisy Dialogue Corpora by Connectivity and Content Relatedness ... Akama, Reina; Yokoi, Sho; Suzuki, Jun. - : arXiv, 2020 BASE Show details 8 Modeling Event Salience in Narratives via Barthes' Cardinal Functions ... The 28th International Conference on Computational Linguistics 2020; Inoue, Naoya; Inui, Kentaro. - : Underline Science Inc., 2020 BASE Show details 9 Do Neural Models Learn Systematicity of Monotonicity Inference in Natural Language? ... Yanaka, Hitomi; Mineshima, Koji; Bekki, Daisuke; Inui, Kentaro. - : arXiv, 2020 Abstract: Despite the success of language models using neural networks, it remains unclear to what extent neural models have the generalization ability to perform inferences. In this paper, we introduce a method for evaluating whether neural models can learn systematicity of monotonicity inference in natural language, namely, the regularity for performing arbitrary inferences with generalization on composition. We consider four aspects of monotonicity inferences and test whether the models can systematically interpret lexical and logical phenomena on different training/test splits. A series of experiments show that three neural models systematically draw inferences on unseen combinations of lexical and logical phenomena when the syntactic structures of the sentences are similar between the training and test sets. However, the performance of the models significantly decreases when the structures are slightly changed in the test set while retaining all vocabularies and constituents already appearing in the training set. ... : accepted by ACL2020 as a long paper ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences; Logic in Computer Science cs.LO URL: https://arxiv.org/abs/2004.14839 https://dx.doi.org/10.48550/arxiv.2004.14839 BASE Hide details

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