Phrase-Based Backoff Models for Machine Translation of Highly Inflected Languages

Phrase-Based Backoff Models for Machine Translation of Highly Inflected Languages Mei Yang Department of Electrical Engineering University of Washington Seattle, WA, USA yangmei@ee.washington.edu Katrin Kirchhoff Department of Electrical Engineering University of Washington Seattle, WA, USA katrin@ee.washington.edu Abstract We propose a backoff model for phrase- based machine translation that translates unseen word forms in foreign-language text by hierarchical morphological ab- stractions at the word and the phrase level. The model is evaluated on the Europarl corpus for German-English and Finnish- English translation and shows improve- ments over state-of-the-art phrase-based models. 1 Introduction Current statistical machine translation (SMT) usu- ally works well in cases where the domain is fixed, the training and test data match, and a large amount of training data is available. Nevertheless, standard SMT models tend to perform much bet- ter on languages that are morphologically simple, whereas highly inflected languages with a large number of potential word forms are more prob- lematic, particularly when training data is sparse. SMT attempts to find a sentence ˆe in the desired output language given the corresponding sentence f in the source language, according to ˆe = argmaxeP(f|e)P(e) (1) Most state-of-the-art SMT adopt a phrase-based approach such that e is chunked into I phrases ¯e1, ..., ¯eI and the translation model is defined over mappings between phrases in e and in f. i.e. P( ¯f|¯e). Typically, phrases are extracted from a word-aligned training corpus. Different inflected forms of the same lemma are treated as different words, and there is no provision for unseen forms, i.e. unknown words encountered in the test data are not translated at all but appear verbatim in the output. Although the percentage of such unseen word forms may be negligible when the training set is large and matches the test set well, it may rise drastically when training data is limited or from a different domain. Many current and future ap- plications of machine translation require the rapid porting of existing systems to new languages and domains without being able to collect appropri- ate training data; this problem can therefore be expected to become increasingly more important. Furthermore, untranslated words can be one of the main factors contributing to low user satisfaction in practical applications. Several previous studies (see Section 2 below) have addressed issues of morphology in SMT, but most of these have focused on the problem of word alignment and vocabulary size reduction. Princi- pled ways of incorporating different levels of mor- phological abstraction into phrase-based models have mostly been ignored so far. In this paper we propose a hierarchical backoff model for phrase- based translation that integrates several layers of morphological operations, such that more specific models are preferred over more general models. We experimentally evaluate the model on transla- tion from two highly-inflected languages, German and Finnish, into English and present improve- ments over a state-of-the-art system. The rest of the paper is structured as follows: The following section discusses related background work. Sec- tion 4 describes the proposed model; Sections 5 and 6 …

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