Translating into Morphologically Rich Languages with Synthetic Phrases

Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pages 1677–1687, Seattle, Washington, USA, 18-21 October 2013. c⃝2013 Association for Computational Linguistics Translating into Morphologically Rich Languages with Synthetic Phrases Victor Chahuneau Eva Schlinger Noah A. Smith Chris Dyer Language Technologies Institute Carnegie Mellon University Pittsburgh, PA 15213, USA {vchahune,eschling,nasmith,cdyer}@cs.cmu.edu Abstract Translation into morphologically rich lan- guages is an important but recalcitrant prob- lem in MT. We present a simple and effec- tive approach that deals with the problem in two phases. First, a discriminative model is learned to predict inflections of target words from rich source-side annotations. Then, this model is used to create additional sentence- specific word- and phrase-level translations that are added to a standard translation model as “synthetic” phrases. Our approach re- lies on morphological analysis of the target language, but we show that an unsupervised Bayesian model of morphology can success- fully be used in place of a supervised analyzer. We report significant improvements in transla- tion quality when translating from English to Russian, Hebrew and Swahili. 1 Introduction Machine translation into morphologically rich lan- guages is challenging, due to lexical sparsity and the large variety of grammatical features expressed with morphology. In this paper, we introduce a method that uses target language morphological grammars (either hand-crafted or learned unsupervisedly) to address this challenge and demonstrate its effective- ness at improving translation from English into sev- eral morphologically rich target languages. Our approach decomposes the process of produc- ing a translation for a word (or phrase) into two steps. First, a meaning-bearing stem is chosen and then an appropriate inflection is selected using a feature-rich discriminative model that conditions on the source context of the word being translated. Rather than attempting to directly produce full- sentence translations using such an elementary pro- cess, we use our model to generate translations of individual words and short phrases that augment— on a sentence-by-sentence basis—the inventory of translation rules obtained using standard translation rule extraction techniques (Chiang, 2007). We call these synthetic phrases. The major advantages of our approach are: (i) synthesized forms are targeted to a specific transla- tion context; (ii) multiple, alternative phrases may be generated with the final choice among rules left to the global translation model; (iii) virtually no language-specific engineering is necessary; (iv) any phrase- or syntax-based decoder can be used with- out modification; and (v) we can generate forms that were not attested in the bilingual training data. The paper is structured as follows. We first present our “translate-and-inflect” model for pre- dicting lexical translations into morphologically rich languages given a source word and its context (§2). Our approach requires a morphological grammar to relate surface forms to underlying ⟨stem, inflection⟩ pairs; we discuss how either a standard morpholog- ical analyzer or a simple Bayesian unsupervised an- alyzer can be used (§3). After describing an ef- ficient parameter estimation procedure for the in- flection model (§4), we employ the translate-and- inflect model in an MT system. We describe how …

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