On the Use of Comparable Corpora to Improve SMT performance

Proceedings of the 12th Conference of the European Chapter of the ACL, pages 16–23, Athens, Greece, 30 March – 3 April 2009. c⃝2009 Association for Computational Linguistics On the use of Comparable Corpora to improve SMT performance Sadaf Abdul-Rauf and Holger Schwenk LIUM, University of Le Mans, FRANCE Sadaf.Abdul-Rauf@lium.univ-lemans.fr Abstract We present a simple and effective method for extracting parallel sentences from comparable corpora. We employ a sta- tistical machine translation (SMT) system built from small amounts of parallel texts to translate the source side of the non- parallel corpus. The target side texts are used, along with other corpora, in the lan- guage model of this SMT system. We then use information retrieval techniques and simple filters to create French/English parallel data from a comparable news cor- pora. We evaluate the quality of the ex- tracted data by showing that it signifi- cantly improves the performance of an SMT systems. 1 Introduction Parallel corpora have proved be an indispens- able resource in Statistical Machine Translation (SMT). A parallel corpus, also called bitext, con- sists in bilingual texts aligned at the sentence level. They have also proved to be useful in a range of natural language processing applications like au- tomatic lexical acquisition, cross language infor- mation retrieval and annotation projection. Unfortunately, parallel corpora are a limited re- source, with insufficient coverage of many lan- guage pairs and application domains of inter- est. The performance of an SMT system heav- ily depends on the parallel corpus used for train- ing. Generally, more bitexts lead to better per- formance. Current resources of parallel corpora cover few language pairs and mostly come from one domain (proceedings of the Canadian or Eu- ropean Parliament, or of the United Nations). This becomes specifically problematic when SMT sys- tems trained on such corpora are used for general translations, as the language jargon heavily used in these corpora is not appropriate for everyday life translations or translations in some other domain. One option to increase this scarce resource could be to produce more human translations, but this is a very expensive option, in terms of both time and money. In recent work less expensive but very productive methods of creating such sentence aligned bilingual corpora were proposed. These are based on generating “parallel” texts from al- ready available “almost parallel” or “not much parallel” texts. The term “comparable corpus” is often used to define such texts. A comparable corpus is a collection of texts composed independently in the respective lan- guages and combined on the basis of similarity of content (Yang and Li, 2003). The raw mate- rial for comparable documents is often easy to ob- tain but the alignment of individual documents is a challenging task (Oard, 1997). Multilingual news reporting agencies like AFP, Xinghua, Reuters, CNN, BBC etc. serve to be reliable producers of huge collections of such comparable corpora. Such texts are widely available from LDC, in par- ticular the Gigaword corpora, or over the WEB for many languages and domains, e.g. Wikipedia. …

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