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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. - : Underline Science Inc., 2021 BASE Show 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; Matsuda, Koji; Nishida, Kyosuke; Suzuki, Jun. - : Underline Science Inc., 2020 Abstract: Despite recent achievements in natural language understanding, reasoning over commonsense knowledge still represents a big challenge to AI systems. As the name suggests, common sense is related to perception and as such, humans derive it from experience rather than from literary education. Recent works in the NLP and the computer vision field have made the effort of making such knowledge explicit using written language and visual inputs, respectively. Our premise is that the latter source fits better with the characteristics of commonsense acquisition. In this work, we explore to what extent the descriptions of real-world scenes are sufficient to learn common sense about different daily situations, drawing upon visual information to answer script knowledge questions. ... Keyword: Information and Knowledge Engineering; Intelligent System; Natural Language Processing; Neural Network URL: https://dx.doi.org/10.48448/ykdt-fd88 https://underline.io/lecture/6568-seeing-the-world-through-text-evaluating-image-descriptions-for-commonsense-reasoning-in-machine-reading-comprehension BASE Hide 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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