Correcting Machine Translation During Question Answering

When a multi-lingual question-answering (QA) system provides an answer that has been incorrectly translated, it is very likely to be regarded as irrelevant. In this paper, we propose a novel method for correcting a deletion error that affects overall understanding of the sentence. Our post-editing technique uses information available at query time: examples drawn from related documents determined to be relevant to the query. Our results show that 4%-7 % of MT sentences are missing a main verb and on average, 79 % of the sentences modified by our system are judged to be more comprehensible. QA performance also benefits from the improved MT: 7 % of irrelevant response sentences become relevant