Shallow and Deep Paraphrasing for Improved Machine Translation Parameter Optimization

Dennis N. Mehay, Michael White


Abstract
String comparison methods such as BLEU (Papineni et al., 2002) are the de facto standard in MT evaluation (MTE) and in MT system parameter tuning (Och, 2003). It is difficult for these metrics to recognize legitimate lexical and grammatical paraphrases, which is important for MT system tuning (Madnani, 2010). We present two methods to address this: a shallow lexical substitution technique and a grammar-driven paraphrasing technique. Grammatically precise paraphrasing is novel in the context of MTE, and demonstrating its usefulness is a key contribution of this paper. We use these techniques to paraphrase a single reference, which, when used for parameter tuning, leads to superior translation performance over baselines that use only human-authored references.
Anthology ID:
2012.amta-monomt.3
Volume:
Workshop on Monolingual Machine Translation
Month:
October 28-November 1
Year:
2012
Address:
San Diego, California, USA
Venue:
AMTA
SIG:
Publisher:
Association for Machine Translation in the Americas
Note:
Pages:
Language:
URL:
https://aclanthology.org/2012.amta-monomt.3
DOI:
Bibkey:
Cite (ACL):
Dennis N. Mehay and Michael White. 2012. Shallow and Deep Paraphrasing for Improved Machine Translation Parameter Optimization. In Workshop on Monolingual Machine Translation, San Diego, California, USA. Association for Machine Translation in the Americas.
Cite (Informal):
Shallow and Deep Paraphrasing for Improved Machine Translation Parameter Optimization (Mehay & White, AMTA 2012)
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PDF:
https://preview.aclanthology.org/author-url/2012.amta-monomt.3.pdf