An investigation of question translation for English-Chinese cross-language question answering

Abstract

Question-answering (QA) is a next-generation search technology which aims to provide answers to a user's question from a collection of documents. Cross-Language QA (CLQA) extends this paradigm to answering questions from a collection in a different language to the question itself. The accuracy with which a CLQA system answers questions depends on the QA system and translation between the question and the information source. We report results from an evaluation of English-Chinese CLQA comparing question translation using standard machine translation systems and extended translation incorporating Web mining to enhance the translation dictionary against a baseline of monolingual Chinese QA. Results from these experiments show that our noun phrase recognition and translation techniques lead to a significant improvement in CLQA effectiveness. Moreover, the syntactic form of a question can be impaired during query translation, and thus potentially degrades the overall CLQA system performance.