Context-based Arabic Morphological Analysis for Machine Translation

CoNLL 2008: Proceedings of the 12th Conference on Computational Natural Language Learning, pages 135–142 Manchester, August 2008 Context-based Arabic Morphological Analysis for Machine Translation ThuyLinh Nguyen Language Technologies Institute School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213, USA thuylinh@cs.cmu.edu Stephan Vogel Language Technologies Institute School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213, USA vogel@cs.cmu.edu Abstract In this paper, we present a novel morphol- ogy preprocessing technique for Arabic- English translation. We exploit the Arabic morphology-English alignment to learn a model removing nonaligned Arabic mor- phemes. The model is an instance of the Conditional Random Field (Lafferty et al., 2001) model; it deletes a morpheme based on the morpheme’s context. We achieved around two BLEU points im- provement over the original Arabic trans- lation for both a travel-domain system trained on 20K sentence pairs and a news domain system trained on 177K sentence pairs, and showed a potential improvement for a large-scale SMT system trained on 5 million sentence pairs. 1 Introduction Statistical machine translation (SMT) relies heav- ily on the word alignment model of the source and the target language. However, there is a mismatch between a rich morphology language (e.g Arabic, Czech) and a poor morphology lan- guage (e.g English). An Arabic source word of- ten corresponds to several English words. Pre- vious research has focused on attempting to ap- ply morphological analysis to machine translation in order to reduce unknown words of highly in- flected languages. Nießen and Ney (2004) rep- resented a word as a vector of morphemes and gained improvement over word-based system for c⃝ 2008. Licensed under the Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported li- cense (http://creativecommons.org/licenses/by-nc-sa/3.0/). Some rights reserved. German-English translation. Goldwater and Mc- closky (2005) improved Czech-English translation by applying different heuristics to increase the equivalence of Czech and English text. Specially for Arabic-English translation, Lee (2004) used the Arabic part of speech and English parts of speech (POS) alignment probabilities to retain an Arabic affix, drop it from the corpus or merge it back to a stem. The resulting system outperformed the original Arabic system trained on 3.3 million sentence pairs corpora when using monotone decoding. However, an improvement in monotone decoding is no guarantee for an im- provement over the best baseline achievable with full word forms. Our experiments showed that an SMT phrase-based translation using 4 words dis- tance reordering could gain four BLEU points over monotone decoding. Sadat and Habash (2006) ex- plored a wide range of Arabic word-level prepro- cessing and produced better translation results for a system trained on 5 million Arabic words. What all the above methodologies do not pro- vide is a means to disambiguate morphologi- cal analysis for machine translation based on the words’ contexts. That is, for an Arabic word anal- ysis of the form prefix*-stem-suffix* a morpheme only is either always retained, always dropped off or always merged to the stem regardless of its surrounding text. In the example in Figure (1), the Arabic word “AlnAfi*h”(“window” in English) …

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