A Paraphrase-Based Approach to Machine Translation Evaluation
Grazia Russo-Lassner, Jimmy Lin, Philip Resnik · 2005
We propose a novel approach to automatic machine translation evaluation based on paraphrase identification. The quality of machine-generated output can be viewed as the extent to which the conveyed meaning matches the semantics of reference translations, independent of lexical and syntactic divergences. This idea is implemented in linear regression models that attempt to capture human judgments of adequacy and fluency, based on features that have previously been shown to be effective for paraphrase identification. We evaluated our model using the output of three different MT systems from the 2004 NIST Arabic-to-English MT evaluation. Results show that models employing paraphrase-based features correlate better with human judgments than models based purely on existing automatic MT metrics. Report Documentation Page Form ApprovedOMB No. 0704-0188 Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington