主催: 人工知能学会
会議名: 第72回 言語・音声理解と対話処理研究会
回次: 72
開催地: 東京工業大学すずかけ台キャンパス 中会議室およびG3棟1Fエントランス
開催日: 2014/12/15 - 2014/12/16
p. 02-
In text and speech, there are various features that express the individuality of the writer or speaker. We proposed a method for transforming individuality using a technique inspired by statistical machine translation (SMT), and showed the effectiveness. In previous work, we proposed a method for paraphrasing for characteristic words using n-gram clustering. However, the method can be improved, because it considers only short context. In this paper, we propose a model of transforming individuality that considers longer contexts. To achieve this, we suggest adaptation of the language models and expansion of paraphrasing for characteristic words.