Yuki Arase

Associate Professor (Japanese page)

Big Data Engineering Laboratory,
Graduate school of information science and technology,
Osaka University, Japan

Email: arase[at]ist.osaka-u.ac.jp
Twitter: @Yuki_arase

Yuki Arase is an associate professor at the graduate school of information science and technology, Osaka University, Japan. She was previously an associate researcher at the natural language computing group of Microsoft Research Asia. Her primary research interest is in paraphrasing, conversation systems, and educational applications for language learners. She earned her Ph. D. of Information Science at Osaka University in 2010 for research on presenting a large amount of information on small screens.

Apart from research, Yuki enjoys yoga practice, always traveling with a yoga mat in her luggage trolley.

I’m recruiting PhD students starting from Oct. 2022 or Apr. 2023. Please send me your CV with a publication list if you are interested. Note that we do not have “research student” positions.

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Publications / CV / ACL Anthology / Google Scholar / Semantic Scholar

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News

• Received the 22nd Funai Information Technology Award from FFIT.
• A paper has been accepted at SEM 2023. S. Kadotani and Y. Arase. Monolingual Phrase Alignment as Parse Forest Mapping, in Proc. of the Joint Conference on Lexical and Computational Semantics (SEM 2023), (July 2023, to appear).
• A paper has been accepted at ACL 2023. Yuki Arase, Han Bao, and Sho Yokoi. Unbalanced Optimal Transport for Unbalanced Word Alignment, in Proc. of the Annual Meeting of the Association for Computational Linguistics (ACL 2023), (July 2023, to appear).
• Our paper, titled “CEFR-based Sentence Difficulty Annotation and Assessment,” has been accepted at EMNLP 2022.
• I have received some inquiries about our lexical substitution corpora. They are downloadable from the research page!

(Last update: 2023/05/31)

Research

For more details, please see Research page.

Paraphrase generation & recognition

Paraphrasing takes various forms of monolingual text transformations, such as text simplification, rewriting, and style transfer. We work on both recognition and generation. The core technologies are intelligent phrase alignment and controllable paraphrase generation.

Related papers: DIRECT, phrase alignment, Round-trip translation for paraphrasing, SAPPHIRE

Representation learning

Vector representation of words, phrases, and sentences are the very basis for NLP research. We study

1. sophisticated representations for word meaning in context and multilingual sentences,
2. efficient pre-trained models for words and phrases, and
3. representations for few-shot learning.

Related papers: WiC representation, disentangling sentence meaning, transfer fine-tuning, label representation for few-shot learning, tiny word embedding

NLP for language education & learning

As a central application of our research outcomes, we develop technologies for language learning and education supports. Our technology covers from fine-grained lexical-level transformations to coarse-grained text-level processing.

Related papers: definition generation, controllable text simplification, CEFR-based lexical simplification, fill-in-the-blank quiz generation

Selected Recent Publications

• S. Kadotani and Y. Arase. Monolingual Phrase Alignment as Parse Forest Mapping, in Proc. of the Joint Conference on Lexical and Computational Semantics (*SEM 2023), (July 2023, to appear).
• Y. Arase, H. Bao, and S. Yokoi. Unbalanced Optimal Transport for Unbalanced Word Alignment, in Proc. of the Annual Meeting of the Association for Computational Linguistics (ACL 2023), (July 2023, to appear).
• Y. Arase, S. Uchida, and T. Kajiwara. CEFR-based Sentence Difficulty Annotation and Assessment, in Proc. of Conference on Empirical Methods in Natural Language Processing (EMNLP2022), pp. 6206-6219 (Dec. 2022).
• Y. Kuroda, T. Kajiwara, Y. Arase, and T. Ninomiya. Adversarial Training on Disentangling Meaning and Language Representations for Unsupervised Quality Estimation, in Proc. of International Conference on Computational Linguistics (COLING 2022), pp. 5240-5245 (Oct. 2022).
• H. Huang, T. Kajiwara, and Y. Arase. Definition Modelling for Appropriate Specificity, in Proc. of Conference on Empirical Methods in Natural Language Processing (EMNLP2021), pp. 2499–2509 (Nov. 2021).
• N. Tiyajamorn, T. Kajiwara, Y. Arase, and M. Onizuka. Language-agnostic Representation from Multilingual Sentence Encoders for Cross-lingual Similarity Estimation, in Proc. of Conference on Empirical Methods in Natural Language Processing (EMNLP2021), pp. 7764–7774 (Nov. 2021).
• Y. Arase and T. Kajiwara. Distilling Word Meaning in Context from Pre-trained Language Model, in Findings of the Association for Computational Linguistics: EMNLP 2021, pp. 534–546 (Nov. 2021). code
• J. Takayama, T. Kajiwara, and Y. Arase. DIRECT: Direct and Indirect Responses in Conversational Text Corpus, in Findings of the Association for Computational Linguistics: EMNLP 2021, pp. 1980–1989 (Nov. 2021). data
• S. Ohashi, J. Takayama, T. Kajiwara, and Y. Arase. Distinct Label Representations for Few-Shot Text Classification, in Proc. of the Annual Meeting of the Association for Computational Linguistics and International Joint Conference on Natural Language Processing (ACL-IJCNLP2021), pp. 831-836 (Aug. 2021).
• Y. Arase and J. Tsujii: Transfer Fine-Tuning of BERT with Phrasal Paraphrases, in Computer Speech & Language, Vol. 66 (Mar. 2021). paper 
• S. Ohashi, M. Isogawa, T. Kajiwara and Y. Arase: Tiny Word Embeddings Using Globally Informed Reconstruction, in Proc. of International Conference on Computational Linguistics (COLING2020), pp. 1199–1203 (Dec. 2020).
• Y. Arase and J. Tsujii: Compositional Phrase Alignment and Beyond, in Proc. of Conference on Empirical Methods in Natural Language Processing (EMNLP2020), pp. 1611–1623 (Nov. 2020).
• J. Takayama and Y. Arase: Consistent Response Generation with Controlled Specificity. in Proc. of Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 4418–4427 (Nov. 2020).
• S. Ohashi, J. Takayama, T. Kajiwara, C. Chu, Y. Arase. Text Classification with Negative Supervision, in Proc. of Annual Meeting of the Association for Computational Linguistics (ACL 2020), pp. 351–357 (July 2020).
• T. Kajiwara, B. Miura, and Y. Arase. Monolingual Transfer Learning via Bilingual Translators for Style-Sensitive Paraphrase Generation, in Proc. of the AAAI Conference on Artificial Intelligence (AAAI 2020), pp. 8042-8049, (Feb. 2020).
• Y. Arase and J. Tsujii: Transfer Fine-Tuning: A BERT Case Study, Proc. of Conference on Empirical Methods in Natural Language Processing (EMNLP2019), pp. 5396–5407 (Nov. 2019).
• J. Takayama, E. Nomoto, and Y. Arase: Dialogue Breakdown Detection Robust to Variations in Annotators and Dialogue Systems, Computer Speech & Language, Vol. 54, pp. 31-43 (Mar. 2019).
• Y. Arase and J. Tsujii: SPADE: Evaluation Dataset for Monolingual Phrase Alignment, in Proc. of Language Resources and Evaluation Conference (LREC 2018), (May 2018).

Academic Service

• PC Chair: IJCNLP-AACL2023, SNL2021
• Senior Area Chair: ACL2023
• Area Chair: ARR, ACL2020, 2021, EMNLP2021
• Publication Chair for ACL2020
• Reviewer: ACL, EMNLP, NAACL, EACL, TACL, Computational Linguistics, etc.