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Statistical Machine Translation with Scarce Resources Using Morpho-syntactic Information
Computational Linguistics
Arabic tokenization, part-of-speech tagging and morphological disambiguation in one fell swoop
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Improving statistical MT through morphological analysis
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Arabic Natural Language Processing
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Syntactic phrase reordering for English-to-Arabic statistical machine translation
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Morphological analysis for statistical machine translation
HLT-NAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers
Arabic preprocessing schemes for statistical machine translation
NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
Bridging the inflection morphology gap for Arabic statistical machine translation
NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
Joint morphological-lexical language modeling for machine translation
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Syntactic reordering for English-Arabic phrase-based machine translation
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Statistical machine translation enhancements through linguistic levels: A survey
ACM Computing Surveys (CSUR)
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Much of the work on statistical machine translation (SMT) from morphologically rich languages has shown that morphological tokenization and orthographic normalization help improve SMT quality because of the sparsity reduction they contribute. In this article, we study the effect of these processes on SMT when translating into a morphologically rich language, namely Arabic. We explore a space of tokenization schemes and normalization options. We also examine a set of six detokenization techniques and evaluate on detokenized and orthographically correct (enriched) output. Our results show that the best performing tokenization scheme is that of the Penn Arabic Treebank. Additionally, training on orthographically normalized (reduced) text then jointly enriching and detokenizing the output outperforms training on enriched text.