Grammatical Machine Translation

Proceedings of the Human Language Technology Conference of the North American Chapter of the ACL, pages 248–255, New York, June 2006. c⃝2006 Association for Computational Linguistics Grammatical Machine Translation Stefan Riezler and John T. Maxwell III Palo Alto Research Center 3333 Coyote Hill Road, Palo Alto, CA 94304 Abstract We present an approach to statistical machine translation that combines ideas from phrase-based SMT and traditional grammar-based MT. Our system incor- porates the concept of multi-word trans- lation units into transfer of dependency structure snippets, and models and trains statistical components according to state- of-the-art SMT systems. Compliant with classical transfer-based MT, target depen- dency structure snippets are input to a grammar-based generator. An experimen- tal evaluation shows that the incorpora- tion of a grammar-based generator into an SMT framework provides improved gram- maticality while achieving state-of-the-art quality on in-coverage examples, suggest- ing a possible hybrid framework. 1 Introduction Recent approaches to statistical machine translation (SMT) piggyback on the central concepts of phrase- based SMT (Och et al., 1999; Koehn et al., 2003) and at the same time attempt to improve some of its shortcomings by incorporating syntactic knowledge in the translation process. Phrase-based translation with multi-word units excels at modeling local or- dering and short idiomatic expressions, however, it lacks a mechanism to learn long-distance dependen- cies and is unable to generalize to unseen phrases that share non-overt linguistic information. Publicly available statistical parsers can provide the syntactic information that is necessary for linguistic general- izations and for the resolution of non-local depen- dencies. This information source is deployed in re- cent work either for pre-ordering source sentences before they are input to to a phrase-based system (Xia and McCord, 2004; Collins et al., 2005), or for re-ordering the output of translation models by statistical ordering models that access linguistic in- formation on dependencies and part-of-speech (Lin, 2004; Ding and Palmer, 2005; Quirk et al., 2005)1. While these approaches deploy dependency-style grammars for parsing source and/or target text, a uti- lization of grammar-based generation on the output of translation models has not yet been attempted in dependency-based SMT. Instead, simple target lan- guage realization models that can easily be trained to reflect the ordering of the reference translations in the training corpus are preferred. The advantage of such models over grammar-based generation seems to be supported, for example, by Quirk et al.’s (2005) improvements over phrase-based SMT as well as over an SMT system that deploys a grammar-based generator (Menezes and Richardson, 2001) on n- gram based automatic evaluation scores (Papineni et al., 2001; Doddington, 2002). Another data point, however, is given by Charniak et al. (2003) who show that parsing-based language modeling can im- prove grammaticality of translations, even if these improvements are not recorded under n-gram based evaluation measures. 1A notable exception to this kind of approach is Chiang (2005) who introduces syntactic information into phrase-based SMT via hierarchical phrases rather than by external parsing. 248 In this paper we would like to step away from n-gram based …

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