What's New

We will present the following papers at Interspeech 2022 (2022/9)

  • Zhengdong Yang, Wangjin Zhou, Chenhui Chu, Sheng Li, Raj Dabre, Raphael Rubino and Yi Zhao:
    Fusion of Self-supervised Learned Models for MOS Prediction
  • Kak Soky, Sheng Li, Masato Mimura, Chenhui Chu and Tatsuya Kawahara:
    Leveraging Simultaneous Translation for Enhancing Transcription of Low-resource Language via Cross Attention Mechanism

We will present the following papers at NAACL 2022 Student Research Workshop (2022/7)

  • Takumi Yoshikoshi, Takashi Kodama, Sadao Kurohashi:
    Explicit Use of Topicality in Dialogue Response Generation
  • Prakhar Saxena, Yin Jou Huang and Sadao Kurohashi:
    Static and Dynamic Speaker Modeling based on Graph Neural Network for Emotion Recognition in Conversation

We will present a paper at NAACL 2022 (2022/7)

  • Zhuoyuan Mao, Chenhui Chu, Raj Dabre, Haiyue Song, Zhen Wan, Sadao Kurohashi:
    When do Contrastive Word Alignments Improve Many-to-many Neural Machine Translation? (Findings)

We will present the following papers at LREC2022 (2022/6)

  • Fei Cheng, Shuntaro Yada, Ribeka Tanaka, Eiji ARAMAKI and Sadao Kurohashi:
    JaMIE: A Pipeline Japanese Medical Information Extraction System with Novel Relation Annotation
  • Felix Giovanni Virgo, Fei Cheng, Sadao Kurohashi:
    Improving Event Duration Question Answering by Leveraging Existing Temporal Information Extraction Data
  • Taro Okahisa, Ribeka Tanaka, Takashi Kodama, Yin Jou Huang and Sadao Kurohashi:
    Constructing a Culinary Interview Dialogue Corpus with Video Conferencing Tool
  • Yihang Li, Shuichiro Shimizu, Weiqi Gu, Chenhui Chu and Sadao Kurohashi:
    VISA: An Ambiguous Subtitles Dataset for Visual Scene-aware Machine Translation

Associate Professor Chu received a Google Research Scholar Award for his Visual Scene-Aware Machine Translation research proposal. (2022/4)

Research Overview

Language is the most reliable medium of human intellectual activities. Our objective is to establish the technology and academic discipline for handling and understanding language, in a manner that is as close as possible to that of humans, using computers. These include syntactic language analysis, semantic analysis, context analysis, text comprehension, text generation and dictionary systems to develop various application systems for machine translation and information retrieval.

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.

Fundamental Studies on Text Understanding

To make computers understand language, it is essential to give computers world knowledge. This was a very hard problem ten years ago, but it has become possible to acquire knowledge from a massive amount of text in virtue of the drastic progress of computing power and network. We have successfully acquired linguistic patterns of predicate-argument structures from automatic parses of 7 billion Japanese sentences crawled from the Web using grid computing machines. By utilizing such knowledge, we study text understanding, i.e., recognizing the relationships between words and phrases in text.

Policy Regarding Acceptance of Students from Outside

Master course

PhD course