Papers by Jun’ichi Tsujii
Compositional Phrase Alignment and Beyond (2020.emnlp-main)
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| Challenge: | Phrase alignment is the basis for sentence pair interactions, such as paraphrase identification and textual entailment recognition. |
| Approach: | They propose a phrase alignment model that embeds similarity distributions into powerful contextualized representations that can be used to model sentence pair interactions. |
| Outcome: | The proposed method significantly outperforms that used in a previous study and achieves a performance competitive with that of experienced human annotators. |
CharacterBERT: Reconciling ELMo and BERT for Word-Level Open-Vocabulary Representations From Characters (2020.coling-main)
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Hicham El Boukkouri, Olivier Ferret, Thomas Lavergne, Hiroshi Noji, Pierre Zweigenbaum, Jun’ichi Tsujii
| Challenge: | Pre-trained language representations from Transformers have become the most popular choice for building NLP systems. |
| Approach: | They propose a new variant of BERT that drops the wordpiece tokenization system altogether . they propose 'characterBERT' module to represent entire words by consulting their characters . |
| Outcome: | The proposed model improves performance on a variety of medical domain tasks while producing robust, word-level, and open-vocabulary representations. |
ELAINE-medLLM: Lightweight English Japanese Chinese Trilingual Large Language Model for Bio-medical Domain (2025.coling-main)
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Ken Yano, Zheheng Luo, Jimin Huang, Qianqian Xie, Masaki Asada, Chenhan Yuan, Kailai Yang, Makoto Miwa, Sophia Ananiadou, Jun’ichi Tsujii
| Challenge: | Existing bilingual or multilingual medical LLMs are limited in multilingual data and therefore perform poorly in non-English languages such as Japanese and Chinese. |
| Approach: | They propose to use a trilingual (English, Japanese, Chinese) large language model adapted for the bio-medical domain to harness the knowledge and abilities of the base model. |
| Outcome: | The proposed model can support English, Japanese, and Chinese and is adapted for a bio-medical domain. |
Transfer Fine-Tuning: A BERT Case Study (D19-1)
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| Challenge: | Recent advances in sentence representation learning have improved the performance of BERT models, but the computational power required is an obstacle preventing practical applications from adopting the technology. |
| Approach: | They propose to inject phrasal paraphrase relations into BERT to generate suitable representations for semantic equivalence assessment instead of increasing model size. |
| Outcome: | The proposed model improves a smaller model while maintaining the model size. |