  In multilingual question answering, either the question needs to be
translated into the document language, or vice versa. In addition to direction,
there are multiple methods to perform the translation, four of which we explore
in this paper: word-based, 10-best, context-based, and grammar-based. We build
a feature for each combination of translation direction and method, and train a
model that learns optimal feature weights. On a large forum dataset consisting
of posts in English, Arabic, and Chinese, our novel learn-to-translate approach
was more effective than a strong baseline (p<0.05): translating all text into
English, then training a classifier based only on English (original or
translated) text.
