Japanese-English Cross-Language Information Retrieval Using Machine Translation and Pseudo-Relevance Feedback

Abstract

This paper evaluates the effectiveness of cross-language information retrieval (CLIR) based on machine-translation (MT) where the search requests are in Japanese and the documents are in English [24]. Our experiments use a subcollection of the TREC test collections and two bilingual researchers for separately translating the TREC requests into Japanese. Our main findings are as follows: (1) With the aid of pseudo-relevance feedback, MT-based Japanese-English CLIR can be as effective as best-case monolingual retrieval. In particular, although poor MT quality often leads to poor initial CLIR performance, pseudo-relevance feedback can alleviate the harm in many cases; (2) The manual request translation process that is inherent in conventional CLIR performance evaluation can be a more dominant factor than query length and overall MT quality.