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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 Page: 1 2 Hits 1 – 20 of 38 1 SyGNS: A Systematic Generalization Testbed Based on Natural Language Semantics ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Inui, Kentaro; Mineshima, Koji. - : Underline Science Inc., 2021 BASE Show details 2 Summarize-then-Answer: Generating Concise Explanations for Multi-hop Reading Comprehension ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Harsh; Balasubramanian, Niranjan; Inoue, Naoya; Inui, Kentaro; Sinha, Steven. - : Underline Science Inc., 2021 Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.490/ Abstract: How can we generate concise explanations for multi-hop Reading Comprehension (RC)? The current strategies of identifying supporting sentences can be seen as an extractive question-focused summarization of the input text. However, these extractive explanations are not necessarily concise i.e. not minimally sufficient for answering a question. Instead, we advocate for an abstractive approach, where we propose to generate a question-focused, abstractive summary of input paragraphs and then feed it to an RC system. Given a limited amount of human-annotated abstractive explanations, we train the abstractive explainer in a semi-supervised manner, where we start from the supervised model and then train it further through trial and error maximizing a conciseness-promoted reward function. Our experiments demonstrate that the proposed abstractive explainer can generate more compact explanations than an extractive explainer with limited ... Keyword: Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing; Question-Answering Systems URL: https://underline.io/lecture/37463-summarize-then-answer-generating-concise-explanations-for-multi-hop-reading-comprehension https://dx.doi.org/10.48448/g81n-2n36 BASE Hide details 3 SHAPE: Shifted Absolute Position Embedding for Transformers ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Inui, Kentaro; Kiyono, Shun. - : Underline Science Inc., 2021 BASE Show details 4 Incorporating Residual and Normalization Layers into Analysis of Masked Language Models ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Inui, Kentaro; Kobayashi, Goro. - : Underline Science Inc., 2021 BASE Show details 5 Pseudo Zero Pronoun Resolution Improves Zero Anaphora Resolution ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Inui, Kentaro; Kiyono, Shun. - : Underline Science Inc., 2021 BASE Show details 6 Exploring Methods for Generating Feedback Comments for Writing Learning ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Hanawa, Kazuaki; Inui, Kentaro. - : Underline Science Inc., 2021 BASE Show details 7 Transformer-based Lexically Constrained Headline Generation ... Yamada, Kosuke; Hitomi, Yuta; Tamori, Hideaki. - : arXiv, 2021 BASE Show details 8 Transformer-based Lexically Constrained Headline Generation ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Hitomi, Yuta; Inui, Kentaro. - : Underline Science Inc., 2021 BASE Show details 9 Topicalization in Language Models: A Case Study on Japanese ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Abe, Kaori; Fujihara, Riki. - : Underline Science Inc., 2021 BASE Show details 10 Lower Perplexity is Not Always Human-Like ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Asahara, Masayuki; Inui, Kentaro. - : Underline Science Inc., 2021 BASE Show details 11 Lower Perplexity is Not Always Human-Like ... Kuribayashi, Tatsuki; Oseki, Yohei; Ito, Takumi. - : arXiv, 2021 BASE Show details 12 An Empirical Study of Contextual Data Augmentation for Japanese Zero Anaphora Resolution ... Konno, Ryuto; Matsubayashi, Yuichiroh; Kiyono, Shun. - : arXiv, 2020 BASE Show details 13 PheMT: A Phenomenon-wise Dataset for Machine Translation Robustness on User-Generated Contents ... Fujii, Ryo; Mita, Masato; Abe, Kaori. - : arXiv, 2020 BASE Show details 14 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 15 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 16 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 17 Attention is Not Only a Weight: Analyzing Transformers with Vector Norms ... Kobayashi, Goro; Kuribayashi, Tatsuki; Yokoi, Sho. - : arXiv, 2020 BASE Show details 18 Filtering Noisy Dialogue Corpora by Connectivity and Content Relatedness ... Akama, Reina; Yokoi, Sho; Suzuki, Jun. - : arXiv, 2020 BASE Show details 19 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 20 Do Neural Models Learn Systematicity of Monotonicity Inference in Natural Language? ... Yanaka, Hitomi; Mineshima, Koji; Bekki, Daisuke. - : arXiv, 2020 BASE Show details Page: 1 2

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