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.