Language Model Adaptation and Analysis for Individuality Transforming

Abstract

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.