Investigation of Statistical Machine Translation Applied to Answer Generation for a Speech-Oriented Guidance System
An example-based question answering (QA) is a robust and practical approach for a real-environment information guidance system. However, it cannot appropriately respond to unexpected user’s utterances if a similar example of a questionanswer pair does not exist in the QA database; in addition, the answer sentences cannot reflect differences in nuance, because the set of answer sentences are fixed beforehand. To deal with these problems, we propose a new method, which introduces statistical machine translation training to answer sentence generation. In the proposed method, we treat questions and answer sentences as different languages. In this paper, we investigate a feasibility of translation from question into answer using real user utterances for Takemaru-kun.
Kazuma Nishimura Hiromichi Kawanami Hiroshi Saruwatari Kiyohiro Shikano
Graduate School of Information Science, Nara Institute of science and Technology, Japan
国际会议
2011亚太信号与信息处理协会年度峰会(APSIPAASC 2011)
西安
英文
1-4
2011-10-18（万方平台首次上网日期，不代表论文的发表时间）