What's New

We will conduct the following tutorial at IJCNLP2017 (2017/11/27-12/1) (8/10)

  • Fabien Cromieres, Toshiaki Nakazawa and Raj Dabre:
    Neural Machine Translation: Basics, Practical Aspects and Recent Trends

We will present following paper at PACLIC2017 (2017/11/16-18) (8/10)

  • Raj Dabre, Tetsuji Nakagawa and Hideto Kazawa:
    An Empirical Study of Language Relatedness for Transfer Learning in Neural Machine Translation (short; work done at Google)

We will present following paper at MT SUMMIT 2017 (2017/9/18-22) (8/10)

  • Raj Dabre, Fabien Cromieres, Sadao Kurohashi:
    Enabling Multi-Source Neural Machine Translation By Concatenating Source Sentences In Multiple Languages (long)

The following paper has been selected as Outstanding Paper of ACL2017 . (8/2)

  • Shuhei Kurita, Daisuke Kawahara and Sadao Kurohashi:
    Neural Joint Model for Transition-based Chinese Syntactic Analysis

John Richardson(Google) received 4th AAMT Nagao Student Award with his PhD thesis (2016/9) entitled "Improving Statistical Machine Translation with Target-Side Dependency Syntax." (6/14)

Prof. Kurohashi and Prof. Kawahara received "The
Commendation for Science and Technology by the
Minister of Education, Culture, Sports, Science and
Technology" (Prizes for Science and Technology;
Research Category)
. (4/12)

We will present following papers at ACL2017 (2017/7/30-8/4) (4/1)

  • Shuhei Kurita, Daisuke Kawahara and Sadao Kurohashi:
    Neural Joint Model for Transition-based Chinese Syntactic Analysis
  • Chenhui Chu, Raj Dabre and Sadao Kurohashi:
    An Empirical Comparison of Domain Adaptation Methods for Neural Machine Translation (short)

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.