Automated postediting of documents
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
A syntax-based statistical translation model
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Simultaneous multilingual search for translingual information retrieval
Proceedings of the 17th ACM conference on Information and knowledge management
Choosing the right translation: a syntactically informed classification approach
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Fine-tuning in Brazilian Portuguese--English statistical transfer machine translation: verbal tenses
HLT-SRWS '10 Proceedings of the NAACL HLT 2010 Student Research Workshop
LDA based similarity modeling for question answering
SS '10 Proceedings of the NAACL HLT 2010 Workshop on Semantic Search
MT error detection for cross-lingual question answering
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
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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 the main verb and on average, 79% of the modified sentences are judged to be more comprehensible. The QA performance also benefits from the improved MT: 7% of irrelevant response sentences become relevant.