A Paraphrase-Based Approach to Machine Translation Evaluation
Abstract
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 ill 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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Aug 01, 2005
- Accession Number
- ADA448032
Entities
People
- Grazia Russo-lassner
- Jimmy Lin
- Philip Resnik
Organizations
- University of Maryland