Kaori Abe


2023

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An Investigation of Warning Erroneous Chat Translations in Cross-lingual Communication
Yunmeng Li | Jun Suzuki | Makoto Morishita | Kaori Abe | Kentaro Inui
Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics: Student Research Workshop

2022

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Why is sentence similarity benchmark not predictive of application-oriented task performance?
Kaori Abe | Sho Yokoi | Tomoyuki Kajiwara | Kentaro Inui
Proceedings of the 3rd Workshop on Evaluation and Comparison of NLP Systems

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Chat Translation Error Detection for Assisting Cross-lingual Communications
Yunmeng Li | Jun Suzuki | Makoto Morishita | Kaori Abe | Ryoko Tokuhisa | Ana Brassard | Kentaro Inui
Proceedings of the 3rd Workshop on Evaluation and Comparison of NLP Systems

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Overview of the 9th Workshop on Asian Translation
Toshiaki Nakazawa | Hideya Mino | Isao Goto | Raj Dabre | Shohei Higashiyama | Shantipriya Parida | Anoop Kunchukuttan | Makoto Morishita | Ondřej Bojar | Chenhui Chu | Akiko Eriguchi | Kaori Abe | Yusuke Oda | Sadao Kurohashi
Proceedings of the 9th Workshop on Asian Translation

This paper presents the results of the shared tasks from the 9th workshop on Asian translation (WAT2022). For the WAT2022, 8 teams submitted their translation results for the human evaluation. We also accepted 4 research papers. About 300 translation results were submitted to the automatic evaluation server, and selected submissions were manually evaluated.

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Topicalization in Language Models: A Case Study on Japanese
Riki Fujihara | Tatsuki Kuribayashi | Kaori Abe | Ryoko Tokuhisa | Kentaro Inui
Proceedings of the 29th International Conference on Computational Linguistics

Humans use different wordings depending on the context to facilitate efficient communication. For example, instead of completely new information, information related to the preceding context is typically placed at the sentence-initial position. In this study, we analyze whether neural language models (LMs) can capture such discourse-level preferences in text generation. Specifically, we focus on a particular aspect of discourse, namely the topic-comment structure. To analyze the linguistic knowledge of LMs separately, we chose the Japanese language, a topic-prominent language, for designing probing tasks, and we created human topicalization judgment data by crowdsourcing. Our experimental results suggest that LMs have different generalizations from humans; LMs exhibited less context-dependent behaviors toward topicalization judgment. These results highlight the need for the additional inductive biases to guide LMs to achieve successful discourse-level generalization.

2021

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Proceedings of the 8th Workshop on Asian Translation (WAT2021)
Toshiaki Nakazawa | Hideki Nakayama | Isao Goto | Hideya Mino | Chenchen Ding | Raj Dabre | Anoop Kunchukuttan | Shohei Higashiyama | Hiroshi Manabe | Win Pa Pa | Shantipriya Parida | Ondřej Bojar | Chenhui Chu | Akiko Eriguchi | Kaori Abe | Yusuke Oda | Katsuhito Sudoh | Sadao Kurohashi | Pushpak Bhattacharyya
Proceedings of the 8th Workshop on Asian Translation (WAT2021)

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Overview of the 8th Workshop on Asian Translation
Toshiaki Nakazawa | Hideki Nakayama | Chenchen Ding | Raj Dabre | Shohei Higashiyama | Hideya Mino | Isao Goto | Win Pa Pa | Anoop Kunchukuttan | Shantipriya Parida | Ondřej Bojar | Chenhui Chu | Akiko Eriguchi | Kaori Abe | Yusuke Oda | Sadao Kurohashi
Proceedings of the 8th Workshop on Asian Translation (WAT2021)

This paper presents the results of the shared tasks from the 8th workshop on Asian translation (WAT2021). For the WAT2021, 28 teams participated in the shared tasks and 24 teams submitted their translation results for the human evaluation. We also accepted 5 research papers. About 2,100 translation results were submitted to the automatic evaluation server, and selected submissions were manually evaluated.

2020

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Embeddings of Label Components for Sequence Labeling: A Case Study of Fine-grained Named Entity Recognition
Takuma Kato | Kaori Abe | Hiroki Ouchi | Shumpei Miyawaki | Jun Suzuki | Kentaro Inui
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

In general, the labels used in sequence labeling consist of different types of elements. For example, IOB-format entity labels, such as B-Person and I-Person, can be decomposed into span (B and I) and type information (Person). However, while most sequence labeling models do not consider such label components, the shared components across labels, such as Person, can be beneficial for label prediction. In this work, we propose to integrate label component information as embeddings into models. Through experiments on English and Japanese fine-grained named entity recognition, we demonstrate that the proposed method improves performance, especially for instances with low-frequency labels.

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PheMT: A Phenomenon-wise Dataset for Machine Translation Robustness on User-Generated Contents
Ryo Fujii | Masato Mita | Kaori Abe | Kazuaki Hanawa | Makoto Morishita | Jun Suzuki | Kentaro Inui
Proceedings of the 28th International Conference on Computational Linguistics

Neural Machine Translation (NMT) has shown drastic improvement in its quality when translating clean input, such as text from the news domain. However, existing studies suggest that NMT still struggles with certain kinds of input with considerable noise, such as User-Generated Contents (UGC) on the Internet. To make better use of NMT for cross-cultural communication, one of the most promising directions is to develop a model that correctly handles these expressions. Though its importance has been recognized, it is still not clear as to what creates the great gap in performance between the translation of clean input and that of UGC. To answer the question, we present a new dataset, PheMT, for evaluating the robustness of MT systems against specific linguistic phenomena in Japanese-English translation. Our experiments with the created dataset revealed that not only our in-house models but even widely used off-the-shelf systems are greatly disturbed by the presence of certain phenomena.

2018

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Multi-dialect Neural Machine Translation and Dialectometry
Kaori Abe | Yuichiroh Matsubayashi | Naoaki Okazaki | Kentaro Inui
Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation