Improved Statistical Machine Translation Using Paraphrases

Proceedings of the Human Language Technology Conference of the North American Chapter of the ACL, pages 17–24, New York, June 2006. c⃝2006 Association for Computational Linguistics Improved Statistical Machine Translation Using Paraphrases Chris Callison-Burch Philipp Koehn Miles Osborne School of Informatics University of Edinburgh 2 Buccleuch Place Edinburgh, EH8 9LW callison-burch@ed.ac.uk Abstract Parallel corpora are crucial for training SMT systems. However, for many lan- guage pairs they are available only in very limited quantities. For these lan- guage pairs a huge portion of phrases en- countered at run-time will be unknown. We show how techniques from paraphras- ing can be used to deal with these oth- erwise unknown source language phrases. Our results show that augmenting a state- of-the-art SMT system with paraphrases leads to significantly improved coverage and translation quality. For a training corpus with 10,000 sentence pairs we in- crease the coverage of unique test set un- igrams from 48% to 90%, with more than half of the newly covered items accurately translated, as opposed to none in current approaches. 1 Introduction As with many other statistical natural language pro- cessing tasks, statistical machine translation (Brown et al., 1993) produces high quality results when am- ple training data is available. This is problematic for so called “low density” language pairs which do not have very large parallel corpora. For example, when words occur infrequently in a parallel corpus param- eter estimates for word-level alignments can be in- accurate, which can in turn lead to inaccurate phrase translations. Limited amounts of training data can further lead to a problem of low coverage in that many phrases encountered at run-time are not ob- served in the training data and therefore their trans- lations will not be learned. Here we address the problem of unknown phrases. Specifically we show that upon encountering an un- known source phrase, we can substitute a paraphrase for it and then proceed using the translation of that paraphrase. We derive these paraphrases from re- sources that are external to the parallel corpus that the translation model is trained from, and we are able to exploit (potentially more abundant) parallel corpora from other language pairs to do so. In this paper we: • Define a method for incorporating paraphrases of unseen source phrases into the statistical ma- chine translation process. • Show that by translating paraphrases we achieve a marked improvement in coverage and translation quality, especially in the case of un- known words which to date have been left un- translated. • Argue that while we observe an improvement in Bleu score, this metric is particularly poorly suited to measuring the sort of improvements that we achieve. • Present an alternative methodology for targeted manual evaluation that may be useful in other research projects. 2 The Problem of Coverage in SMT Statistical machine translation made considerable advances in translation quality with the introduc- tion of phrase-based translation (Marcu and Wong, 2002; Koehn et al., 2003; Och and Ney, 2004). By 17 0 10 20 30 40 …

Content truncated to 500 words