MT Error Detection for Cross-Lingual Question Answering
نویسندگان
چکیده
We present a novel algorithm for detecting errors in MT, specifically focusing on content words that are deleted during MT. We evaluate it in the context of cross-lingual question answering (CLQA), where we try to correct the detected errors by using a better (but slower) MT system to retranslate a limited number of sentences at query time. Using a query-dependent ranking heuristic enabled the system to direct scarce MT resources towards retranslating the sentences that were most likely to benefit CLQA. The error detection algorithm identified spuriously deleted content words with high precision. However, retranslation was not an effective approach for correcting them, which indicates the need for a more targeted approach to error correction in the future.
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