Modelling Lexical Redundancy for Machine Translation

Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, pages 969–976, Sydney, July 2006. c⃝2006 Association for Computational Linguistics Modelling lexical redundancy for machine translation David Talbot and Miles Osborne School of Informatics, University of Edinburgh 2 Buccleuch Place, Edinburgh, EH8 9LW, UK d.r.talbot@sms.ed.ac.uk, miles@inf.ed.ac.uk Abstract Certain distinctions made in the lexicon of one language may be redundant when translating into another language. We quantify redundancy among source types by the similarity of their distributions over target types. We propose a language- independent framework for minimising lexical redundancy that can be optimised directly from parallel text. Optimisation of the source lexicon for a given target lan- guage is viewed as model selection over a set of cluster-based translation models. Redundant distinctions between types may exhibit monolingual regularities, for ex- ample, inflexion patterns. We define a prior over model structure using a Markov random field and learn features over sets of monolingual types that are predictive of bilingual redundancy. The prior makes model selection more robust without the need for language-specific assumptions re- garding redundancy. Using these mod- els in a phrase-based SMT system, we show significant improvements in transla- tion quality for certain language pairs. 1 Introduction Data-driven machine translation (MT) relies on models that can be efficiently estimated from par- allel text. Token-level independence assumptions based on word-alignments can be used to decom- pose parallel corpora into manageable units for pa- rameter estimation. However, if training data is scarce or language pairs encode significantly dif- ferent information in the lexicon, such as Czech and English, additional independence assumptions may assist the model estimation process. Standard statistical translation models use sep- arate parameters for each pair of source and target types. In these models, distinctions in either lex- icon that are redundant to the translation process will result in unwarranted model complexity and make parameter estimation from limited parallel data more difficult. A natural way to eliminate such lexical redundancy is to group types into ho- mogeneous clusters that do not differ significantly in their distributions over types in the other lan- guage. Cluster-based translation models capture the corresponding independence assumptions. Previous work on bilingual clustering has fo- cused on coarse partitions of the lexicon that resemble automatically induced part-of-speech classes. These were used to model generic word-alignment patterns such as noun-adjective re-ordering between English and French (Och, 1998). In contrast, we induce fine-grained parti- tions of the lexicon, conceptually closer to auto- matic lemmatisation, optimised specifically to as- sign translation probabilities. Unlike lemmatisa- tion or stemming, our method specifically quanti- fies lexical redundancy in a bilingual setting and does not make language-specific assumptions. We tackle the problem of redundancy in the translation lexicon via Bayesian model selection over a set of cluster-based translation models. We search for the model, defined by a clustering of the source lexicon, that maximises the marginal likelihood of target tokens in parallel data. In this optimisation, source types are combined into clus- ters if their distributions over …

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