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Hits 1 – 20 of 30
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
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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
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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
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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
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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
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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
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7
Transformer-based Lexically Constrained Headline Generation ...
Yamada, Kosuke
;
Hitomi, Yuta
;
Tamori, Hideaki
. - : arXiv, 2021
BASE
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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
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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
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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
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11
Lower Perplexity is Not Always Human-Like ...
Kuribayashi, Tatsuki
;
Oseki, Yohei
;
Ito, Takumi
. - : arXiv, 2021
BASE
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12
An Empirical Study of Contextual Data Augmentation for Japanese Zero Anaphora Resolution ...
Konno, Ryuto
;
Matsubayashi, Yuichiroh
;
Kiyono, Shun
. - : arXiv, 2020
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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
BASE
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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
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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
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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
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17
Attention is Not Only a Weight: Analyzing Transformers with Vector Norms ...
Kobayashi, Goro
;
Kuribayashi, Tatsuki
;
Yokoi, Sho
. - : arXiv, 2020
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18
Filtering Noisy Dialogue Corpora by Connectivity and Content Relatedness ...
Akama, Reina
;
Yokoi, Sho
;
Suzuki, Jun
;
Inui, Kentaro
. - : arXiv, 2020
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) ...
Keyword:
Computation and Language cs.CL
;
FOS Computer and information sciences
URL:
https://arxiv.org/abs/2004.14008
https://dx.doi.org/10.48550/arxiv.2004.14008
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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
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20
Do Neural Models Learn Systematicity of Monotonicity Inference in Natural Language? ...
Yanaka, Hitomi
;
Mineshima, Koji
;
Bekki, Daisuke
. - : arXiv, 2020
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