We will present the following papers at ACL 2024 (2024/8) . †

• Sirou Chen, Sakiko Yahata, Shuichiro Shimizu, Zhengdong Yang, Yihang Li, Chenhui Chu, Sadao Kurohashi:
  MELD-ST: An Emotion-aware Speech Translation Dataset (Findings)
• Yahan Yu, Duzhen Zhang, Xiuyi Chen, Chenhui Chu:
  Flexible Weight Tuning and Weight Fusion Strategies for Continual Named Entity Recognition (Findings)
• Duzhen Zhang, Yahan Yu, Chenxing Li, Jiahua Dong, Dan Su, Chenhui Chu, Dong Yu:
  MM-LLMs: Recent Advances in MultiModal Large Language Models (Findings)
• Zhen Wan, Yating Zhang, Yexiang Wang, Fei Cheng, Sadao Kurohashi:
  Reformulating Domain Adaptation of Large Language Models as Adapt-Retrieve-Revise: A Case Study on Chinese Legal Domain (Findings)

We will hold a briefing session (2024/5/11) †

• As part of the admission orientation of the Intelligence Science and Technology Course held on 11 May, 2024, our lab will have an onsite briefing session after the course-wide session. We plan to hold two briefing sessions from 15:00 and 16:00, respectively.

The following paper received the FY2023 best paper award of the Journal of Natural Language Processing (2024/3) †

• Kazumasa Omura, Daisuke Kawahara, and Sadao Kurohashi:
  Building a Commonsense Inference Dataset based on Basic Events and its Application

We will present the following papers at LREC-COLING 2024 (2024/5). †

• Taishi Chika, Taro Okahisa, Takashi Kodama, Yin Jou Huang, Yugo Murawaki and Sadao Kurohashi:
  Domain Transferable Semantic Frames for Expert Interview Dialogues
• Yikun Sun, Zhen Wan, Nobuhiro Ueda, Sakiko Yahata, Fei Cheng, Chenhui Chu and Sadao Kurohashi:
  Rapidly Developing High-quality Instruction Data and Evaluation Benchmark for Large Language Models with Minimal Human Effort: A Case Study on Japanese
• Norizo Sakaguchi, Yugo Murawaki, Chenhui Chu and Sadao Kurohashi:
  Identifying Source Language Expressions for Pre-editing in Machine Translation
• Nobuhiro Ueda, Hideko Habe, Yoko Matsui, Akishige Yuguchi, Seiya Kawano, Yasutomo Kawanishi, Sadao Kurohashi and Koichiro Yoshino: J-CRe3:
  A Japanese Conversation Dataset for Real-world Reference Resolution
• Hao Wang, Tang Li, Chenhui Chu, Rui Wang and Pinpin Zhu:
  Towards Human-Like Machine Comprehension: Few-Shot Relational Learning in Visually-Rich Documents
• Rikito Takahashi, Hirokazu Kiyomaru, Chenhui Chu and Sadao Kurohashi:
  Abstractive Multi-Video Captioning: Benchmark Dataset Construction and Extensive Evaluation
• Yugo Murawaki:
  Principal Component Analysis as a Sanity Check for Bayesian Phylolinguistic Reconstruction
• Kazumasa Omura, Fei Cheng and Sadao Kurohashi:
  An Empirical Study of Synthetic Data Generation for Implicit Discourse Relation Recognition
• Xiaotian Lu, Jiyi Li, Zhen Wan, Xiaofeng Lin, Koh Takeuchi and Hisashi Kashima:
  Evaluating Saliency Explanations in NLP by Crowdsourcing
• Francois Meyer, Haiyue Song, Abhisek Chakrabarty, Jan Buys, Raj Dabre and Hideki Tanaka:
  NGLUEni: Benchmarking and Adapting Pretrained Language Models for Nguni Languages

Search Engine Infrastructure based on Deep Natural Language Processing †

The essential purpose of information retrieval is not to retrieve just a relevant document but to acquire the information or knowledge in the document. We have been developing a next-generation infrastructure of information retrieval on the basis of the following techniques of deep natural language processing: precise processing based not on words but on predicate-argument structures, identifying the variety of linguistic expressions and providing a bird's-eye view of search results via clustering and interaction.

Machine Translation †

To bring automatic translation by computers to the level of human translation, we have been studying next-generation methodology of machine translation on the basis of text understanding and a large collection of translation examples. We have already accomplished practical translation on the domain of travel conversation, and constructed a translation-aid system that can be used by experts of patent translation.