Acquiring Chinese paraphrases based on random walk of $N$ steps

Abstract

The conventional “pivot” approach of acquiring paraphrases from bilingual corpus has certain limitations where only candidate paraphrases within two steps are considered. In this paper, we propose a graph-based model of acquiring paraphrases from a phrase translation table. First, we describe a graph-based model representing Chinese-English phrase translation relations, a random walk algorithm based on $N$ number of steps and a confidence metric for the obtained paraphrases. Furthermore, with the aim of finding more potential for Chinese paraphrases, we augment the model so that it is able to integrate other language pairs, such as English-Japanese phrase translation relations. We performed experiments on NTCIR Chinese-English and English-Japanese bilingual corpus and compared the results to those of conventional methods. The experimental results show that the proposed approach acquires more paraphrases. In addition, the performance was improved further after the English-Japanese phrase translations were added to the graph-based model.