Osamu Imaichi


2024

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JFLD: A Japanese Benchmark for Deductive Reasoning Based on Formal Logic
Terufumi Morishita | Atsuki Yamaguchi | Gaku Morio | Hikaru Tomonari | Osamu Imaichi | Yasuhiro Sogawa
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Large language models (LLMs) have proficiently solved a broad range of tasks with their rich knowledge but often struggle with logical reasoning. To foster the research on logical reasoning, many benchmarks have been proposed so far. However, most of these benchmarks are limited to English, hindering the evaluation of LLMs specialized for each language. To address this, we propose **JFLD** (**J**apanese **F**ormal **L**ogic **D**eduction), a deductive reasoning benchmark for Japanese. JFLD assess whether LLMs can generate logical steps to (dis-)prove a given hypothesis based on a given set of facts. Its key features are assessing pure logical reasoning abilities isolated from knowledge and assessing various reasoning rules. We evaluate various Japanese LLMs and see that they are still poor at logical reasoning, thus highlighting a substantial need for future research.

2023

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Controlling keywords and their positions in text generation
Yuichi Sasazawa | Terufumi Morishita | Hiroaki Ozaki | Osamu Imaichi | Yasuhiro Sogawa
Proceedings of the 16th International Natural Language Generation Conference

One of the challenges in text generation is to control text generation as intended by the user. Previous studies proposed specifying the keywords that should be included in the generated text. However, this approach is insufficient to generate text that reflect the user’s intent. For example, placing an important keyword at the beginning of the text would help attract the reader’s attention; however, existing methods do not enable such flexible control. In this paper, we tackle a novel task of controlling not only keywords but also the position of each keyword in the text generation. To this end, we propose a task-independent method that uses special tokens to control the relative position of keywords. Experimental results on summarization and story generation tasks show that the proposed method can control keywords and their positions. The experimental results also demonstrate that controlling the keyword positions can generate summary texts that are closer to the user’s intent than baseline.

2013

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A Comparison of Rule-Based and Machine Learning Methods for Medical Information Extraction
Osamu Imaichi | Toshihiko Yanase | Yoshiki Niwa
The First Workshop on Natural Language Processing for Medical and Healthcare Fields

2004

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Constructing Word-Sense Association Networks from Bilingual Dictionary and Comparable Corpora
Hiroyuki Kaji | Osamu Imaichi
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

A novel thesaurus named a gword-sense association networkh is proposed for the first time. It consists of nodes representing word senses, each of which is defined as a set consisting of a word and its translation equivalents, and edges connecting topically associated word senses. This word-sense association network is produced from a bilingual dictionary and comparable corpora by means of a newly developed fully automatic method. The feasibility and effectiveness of the method were demonstrated experimentally by using the EDR English-Japanese dictionary together with Wall Street Journal and Nihon Keizai Shimbun corpora. The word-sense association networks were applied to word-sense disambiguation as well as to a query interface for information retrieval.