What's New

We will present the following papers at NAACL 2022 Student Research Workshop

  • 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 DialDoc2022 (2022/5)

  • Takashi Kodama, Ribeka Tanaka and Sadao Kurohashi:
    Construction of Hierarchical Structured Knowledge-based Recommendation Dialogue Dataset and Dialogue System

We will hold a briefing session (2022/5/14)

As part of the admission orientation of the Department of Intelligence Science and Technology held on 14 May, 2022, our lab will have an onsite & online hybrid briefing session. If you will join online. please proceed to a breakout room that will be set up after the department-wide session. We plan to hold two briefing sessions from 15:00 and 16:00, respectively.

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 a paper at ACL 2022 Student Research Workshop (2022/5)

  • Yongmin Kim, Chenhui Chu, Sadao Kurohashi:
    Flexible Visual Grounding

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

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

The following paper received the Special Committee Award of the 28-th annual meeting of the association for natural language processing (2022/3)

  • Rikito Takahashi, Chenhui Chu and Sadao Kurohashi:
    Abstractive Captioning from Multiple Videos
  • Yihang Li, Shuichirou Shimizu, Chenhui Chu and Sadao Kurohashi:
    Towards the Construction of Multimodal Machine Translation Dataset Focusing on Ambiguity of Translation

We have released the app for Japanese composition study "Let's get started with Ichimaru! Kotobamusubi" developed in the research project with the Japan Kanji Aptitude Testing Foundation.

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

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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

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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.

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