Syntactic Phrase Reordering for English-to-Arabic Statistical Machine Translation

Proceedings of the 12th Conference of the European Chapter of the ACL, pages 86–93, Athens, Greece, 30 March – 3 April 2009. c⃝2009 Association for Computational Linguistics Syntactic Phrase Reordering for English-to-Arabic Statistical Machine Translation Ibrahim Badr Rabih Zbib Computer Science and Artificial Intelligence Lab Massachusetts Institute of Technology Cambridge, MA 02139, USA {iab02, rabih, glass}@csail.mit.edu James Glass Abstract Syntactic Reordering of the source lan- guage to better match the phrase struc- ture of the target language has been shown to improve the performance of phrase-based Statistical Machine Transla- tion. This paper applies syntactic reorder- ing to English-to-Arabic translation. It in- troduces reordering rules, and motivates them linguistically. It also studies the ef- fect of combining reordering with Ara- bic morphological segmentation, a pre- processing technique that has been shown to improve Arabic-English and English- Arabic translation. We report on results in the news text domain, the UN text domain and in the spoken travel domain. 1 Introduction Phrase-based Statistical Machine Translation has proven to be a robust and effective approach to machine translation, providing good performance without the need for explicit linguistic informa- tion. Phrase-based SMT systems, however, have limited capabilities in dealing with long distance phenomena, since they rely on local alignments. Automatically learned reordering models, which can be conditioned on lexical items from both the source and the target, provide some limited re- ordering capability when added to SMT systems. One approach that explicitly deals with long distance reordering is to reorder the source side to better match the target side, using predefined rules. The reordered source is then used as input to the phrase-based SMT system. This approach indirectly incorporates structure information since the reordering rules are applied on the parse trees of the source sentence. Obviously, the same re- ordering has to be applied to both training data and test data. Despite the added complexity of parsing the data, this technique has shown improvements, especially when good parses of the source side ex- ist. It has been successfully applied to German-to- English and Chinese-to-English SMT (Collins et al., 2005; Wang et al., 2007). In this paper, we propose the use of a similar approach for English-to-Arabic SMT. Unlike most other work on Arabic translation, our work is in the direction of the more morphologically com- plex language, which poses unique challenges. We propose a set of syntactic reordering rules on the English source to align it better to the Arabic tar- get. The reordering rules exploit systematic differ- ences between the syntax of Arabic and the syntax of English; they specifically address two syntac- tic constructs. The first is the Subject-Verb order in independent sentences, where the preferred or- der in written Arabic is Verb-Subject. The sec- ond is the noun phrase structure, where many dif- ferences exist between the two languages, among them the order of adjectives, compound nouns and genitive constructs, as well as the way defi- niteness is marked. The implementation of these rules is fairly straightforward since they are ap- plied …

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