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Hitomi Yanaka
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2020 – today
- 2024
- [c24]Nobuyuki Iokawa, Hitomi Yanaka:
Visual-Textual Entailment with Quantities Using Model Checking and Knowledge Injection. LREC/COLING 2024: 17397-17408 - [c23]Asa Tomita, Hitomi Yanaka, Daisuke Bekki:
Reforging : A Method for Constructing a Linguistically Valid Japanese CCG Treebank. EACL (Student Research Workshop) 2024: 196-207 - [c22]Aman Jain, Teruhisa Misu, Kentaro Yamada, Hitomi Yanaka:
GesNavi: Gesture-guided Outdoor Vision-and-Language Navigation. EACL (Student Research Workshop) 2024: 290-295 - [i25]Takeshi Kojima, Itsuki Okimura, Yusuke Iwasawa, Hitomi Yanaka, Yutaka Matsuo:
On the Multilingual Ability of Decoder-based Pre-trained Language Models: Finding and Controlling Language-Specific Neurons. CoRR abs/2404.02431 (2024) - [i24]Tomoki Doi, Masaru Isonuma, Hitomi Yanaka:
Comprehensive Evaluation of Large Language Models for Topic Modeling. CoRR abs/2406.00697 (2024) - [i23]Hitomi Yanaka, Namgi Han, Ryoma Kumon, Jie Lu, Masashi Takeshita, Ryo Sekizawa, Taisei Kato, Hiromi Arai:
Analyzing Social Biases in Japanese Large Language Models. CoRR abs/2406.02050 (2024) - [i22]Rongzhi Li, Takeru Matsuda, Hitomi Yanaka:
Exploring Intra and Inter-language Consistency in Embeddings with ICA. CoRR abs/2406.12474 (2024) - [i21]Ryoma Kumon, Daiki Matsuoka, Hitomi Yanaka:
Evaluating Structural Generalization in Neural Machine Translation. CoRR abs/2406.13363 (2024) - [i20]Akiko Aizawa, Eiji Aramaki, Bowen Chen, Fei Cheng, Hiroyuki Deguchi, Rintaro Enomoto, Kazuki Fujii, Kensuke Fukumoto, Takuya Fukushima, Namgi Han, Yuto Harada, Chikara Hashimoto, Tatsuya Hiraoka, Shohei Hisada, Sosuke Hosokawa, Lu Jie, Keisuke Kamata, Teruhito Kanazawa, Hiroki Kanezashi, Hiroshi Kataoka, Satoru Katsumata, Daisuke Kawahara, Seiya Kawano, Atsushi Keyaki, Keisuke Kiryu, Hirokazu Kiyomaru, Takashi Kodama, Takahiro Kubo, Yohei Kuga, Ryoma Kumon, Shuhei Kurita, Sadao Kurohashi, Conglong Li, Taiki Maekawa, Hiroshi Matsuda, Yusuke Miyao, Kentaro Mizuki, Sakae Mizuki, Yugo Murawaki, Ryo Nakamura, Taishi Nakamura, Kouta Nakayama, Tomoka Nakazato, Takuro Niitsuma, Jiro Nishitoba, Yusuke Oda, Hayato Ogawa, Takumi Okamoto, Naoaki Okazaki, Yohei Oseki, Shintaro Ozaki, Koki Ryu, Rafal Rzepka, Keisuke Sakaguchi, Shota Sasaki, Satoshi Sekine, Kohei Suda, Saku Sugawara, Issa Sugiura, Hiroaki Sugiyama, Hisami Suzuki, Jun Suzuki, Toyotaro Suzumura, Kensuke Tachibana, Yu Takagi, Kyosuke Takami, Koichi Takeda, Masashi Takeshita, Masahiro Tanaka, Kenjiro Taura, Arseny Tolmachev, Nobuhiro Ueda, Zhen Wan, Shuntaro Yada, Sakiko Yahata, Yuya Yamamoto, Yusuke Yamauchi, Hitomi Yanaka, Rio Yokota, Koichiro Yoshino:
LLM-jp: A Cross-organizational Project for the Research and Development of Fully Open Japanese LLMs. CoRR abs/2407.03963 (2024) - 2023
- [j2]Akiyoshi Tomihari, Hitomi Yanaka
:
Logic-Based Inference With Phrase Abduction Using Vision-and-Language Models. IEEE Access 11: 45645-45656 (2023) - [c21]Tomoki Sugimoto, Yasumasa Onoe, Hitomi Yanaka:
Jamp: Controlled Japanese Temporal Inference Dataset for Evaluating Generalization Capacity of Language Models. ACL (student) 2023: 57-68 - [c20]Ryo Sekizawa, Nan Duan, Shuai Lu, Hitomi Yanaka:
Constructing Multilingual Code Search Dataset Using Neural Machine Translation. ACL (student) 2023: 69-75 - [c19]Hitomi Yanaka, Yuta Nakamura
, Yuki Chida, Tomoya Kurosawa:
Medical Visual Textual Entailment for Numerical Understanding of Vision-and-Language Models. ClinicalNLP@ACL 2023: 8-18 - [c18]Natsuki Murakami, Mana Ishida, Yuta Takahashi, Hitomi Yanaka, Daisuke Bekki:
Knowledge Injection for Disease Names in Logical Inference between Japanese Clinical Texts. ClinicalNLP@ACL 2023: 108-117 - [c17]Daiki Matsuoka
, Daisuke Bekki
, Hitomi Yanaka
:
Appositive Projection as Implicit Context Extension in Dependent Type Semantics. LENLS 2023: 224-243 - [c16]Ryo Sekizawa, Hitomi Yanaka:
Analyzing Syntactic Generalization Capacity of Pre-trained Language Models on Japanese Honorific Conversion. *SEM@ACL 2023: 40-47 - [c15]Tomoya Kurosawa, Hitomi Yanaka:
Does Character-level Information Always Improve DRS-based Semantic Parsing? *SEM@ACL 2023: 249-258 - [i19]Daisuke Bekki, Hitomi Yanaka:
Is Japanese CCGBank empirically correct? A case study of passive and causative constructions. CoRR abs/2302.14708 (2023) - [i18]Tomoya Kurosawa, Hitomi Yanaka:
Does Character-level Information Always Improve DRS-based Semantic Parsing? CoRR abs/2306.02302 (2023) - [i17]Ryo Sekizawa, Hitomi Yanaka:
Analyzing Syntactic Generalization Capacity of Pre-trained Language Models on Japanese Honorific Conversion. CoRR abs/2306.03055 (2023) - [i16]Aman Jain, Anirudh Reddy Kondapally, Kentaro Yamada, Hitomi Yanaka:
A neuro-symbolic approach for multimodal reference expression comprehension. CoRR abs/2306.10717 (2023) - [i15]Tomoki Sugimoto, Yasumasa Onoe, Hitomi Yanaka:
Jamp: Controlled Japanese Temporal Inference Dataset for Evaluating Generalization Capacity of Language Models. CoRR abs/2306.10727 (2023) - [i14]Ryo Sekizawa, Nan Duan, Shuai Lu, Hitomi Yanaka:
Constructing Multilingual Code Search Dataset Using Neural Machine Translation. CoRR abs/2306.15604 (2023) - 2022
- [j1]Hitomi Yanaka, Koji Mineshima:
Compositional Evaluation on Japanese Textual Entailment and Similarity. Trans. Assoc. Comput. Linguistics 10: 1266-1284 (2022) - [c14]Tomoya Kurosawa, Hitomi Yanaka:
Logical Inference for Counting on Semi-structured Tables. ACL (student) 2022: 84-96 - [c13]Tomoki Sugimoto, Hitomi Yanaka:
Compositional Semantics and Inference System for Temporal Order based on Japanese CCG. ACL (student) 2022: 104-114 - [i13]Tomoya Kurosawa, Hitomi Yanaka:
Logical Inference for Counting on Semi-structured Tables. CoRR abs/2204.07803 (2022) - [i12]Tomoki Sugimoto, Hitomi Yanaka:
Compositional Semantics and Inference System for Temporal Order based on Japanese CCG. CoRR abs/2204.09245 (2022) - [i11]Hitomi Yanaka, Koji Mineshima:
Compositional Evaluation on Japanese Textual Entailment and Similarity. CoRR abs/2208.04826 (2022) - 2021
- [c12]Hitomi Yanaka, Koji Mineshima, Kentaro Inui:
SyGNS: A Systematic Generalization Testbed Based on Natural Language Semantics. ACL/IJCNLP (Findings) 2021: 103-119 - [c11]Masato Mita, Hitomi Yanaka:
Do Grammatical Error Correction Models Realize Grammatical Generalization? ACL/IJCNLP (Findings) 2021: 4554-4561 - [c10]Hitomi Yanaka, Koji Mineshima:
Assessing the Generalization Capacity of Pre-trained Language Models through Japanese Adversarial Natural Language Inference. BlackboxNLP@EMNLP 2021: 337-349 - [c9]Hitomi Yanaka, Koji Mineshima, Kentaro Inui:
Exploring Transitivity in Neural NLI Models through Veridicality. EACL 2021: 920-934 - [i10]Hitomi Yanaka, Koji Mineshima, Kentaro Inui:
Exploring Transitivity in Neural NLI Models through Veridicality. CoRR abs/2101.10713 (2021) - [i9]Hitomi Yanaka, Koji Mineshima, Kentaro Inui:
SyGNS: A Systematic Generalization Testbed Based on Natural Language Semantics. CoRR abs/2106.01077 (2021) - [i8]Masato Mita, Hitomi Yanaka:
Do Grammatical Error Correction Models Realize Grammatical Generalization? CoRR abs/2106.03031 (2021) - [i7]Riko Suzuki, Hitomi Yanaka, Koji Mineshima, Daisuke Bekki:
Building a Video-and-Language Dataset with Human Actions for Multimodal Logical Inference. CoRR abs/2106.14137 (2021) - 2020
- [c8]Hitomi Yanaka
, Koji Mineshima, Daisuke Bekki, Kentaro Inui:
Do Neural Models Learn Systematicity of Monotonicity Inference in Natural Language? ACL 2020: 6105-6117 - [i6]Hitomi Yanaka, Koji Mineshima, Daisuke Bekki, Kentaro Inui:
Do Neural Models Learn Systematicity of Monotonicity Inference in Natural Language? CoRR abs/2004.14839 (2020)
2010 – 2019
- 2019
- [c7]Riko Suzuki, Hitomi Yanaka, Masashi Yoshikawa, Koji Mineshima, Daisuke Bekki:
Multimodal Logical Inference System for Visual-Textual Entailment. ACL (2) 2019: 386-392 - [c6]Hitomi Yanaka
, Koji Mineshima, Daisuke Bekki, Kentaro Inui, Satoshi Sekine, Lasha Abzianidze, Johan Bos:
Can Neural Networks Understand Monotonicity Reasoning? BlackboxNLP@ACL 2019: 31-40 - [c5]Hitomi Yanaka
, Koji Mineshima, Daisuke Bekki, Kentaro Inui, Satoshi Sekine, Lasha Abzianidze, Johan Bos:
HELP: A Dataset for Identifying Shortcomings of Neural Models in Monotonicity Reasoning. *SEM@NAACL-HLT 2019: 250-255 - [i5]Hitomi Yanaka, Koji Mineshima, Daisuke Bekki, Kentaro Inui, Satoshi Sekine, Lasha Abzianidze, Johan Bos:
HELP: A Dataset for Identifying Shortcomings of Neural Models in Monotonicity Reasoning. CoRR abs/1904.12166 (2019) - [i4]Riko Suzuki, Hitomi Yanaka, Masashi Yoshikawa, Koji Mineshima, Daisuke Bekki:
Multimodal Logical Inference System for Visual-Textual Entailment. CoRR abs/1906.03952 (2019) - [i3]Hitomi Yanaka, Koji Mineshima, Daisuke Bekki, Kentaro Inui, Satoshi Sekine, Lasha Abzianidze, Johan Bos:
Can neural networks understand monotonicity reasoning? CoRR abs/1906.06448 (2019) - 2018
- [c4]Kana Manome, Masashi Yoshikawa, Hitomi Yanaka, Pascual Martínez-Gómez, Koji Mineshima, Daisuke Bekki:
Neural sentence generation from formal semantics. INLG 2018: 408-414 - [c3]Hitomi Yanaka, Koji Mineshima, Pascual Martínez-Gómez, Daisuke Bekki:
Acquisition of Phrase Correspondences Using Natural Deduction Proofs. NAACL-HLT 2018: 756-766 - [i2]Hitomi Yanaka, Koji Mineshima, Pascual Martínez-Gómez, Daisuke Bekki:
Acquisition of Phrase Correspondences using Natural Deduction Proofs. CoRR abs/1804.07656 (2018) - 2017
- [c2]Hitomi Yanaka
, Koji Mineshima, Pascual Martínez-Gómez, Daisuke Bekki:
Determining Semantic Textual Similarity using Natural Deduction Proofs. EMNLP 2017: 681-691 - [i1]Hitomi Yanaka, Koji Mineshima, Pascual Martínez-Gómez, Daisuke Bekki:
Determining Semantic Textual Similarity using Natural Deduction Proofs. CoRR abs/1707.08713 (2017) - 2016
- [c1]Hitomi Yanaka
, Yukio Ohsawa:
Clustering Documents on Case Vectors Represented by Predicate-argument Structures - Applied for Eliciting Technological Problems from Patents. FedCSIS 2016: 175-180
Coauthor Index
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last updated on 2024-08-16 18:33 CEST by the dblp team
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