A Word-to-Word Model of Translational Equivalence

A Word-to-Word Model of Translational Equivalence I. Dan Melamed Dept. of Computer and Information Science University of Pennsylvania Philadelphia, PA, 19104, U.S.A. raelamed~unagi, c is. upenn, edu Abstract Many multilingual NLP applications need to translate words between different lan- guages, but cannot afford the computa- tional expense of inducing or applying a full translation model. For these applications, we have designed a fast algorithm for esti- mating a partial translation model, which accounts for translational equivalence only at the word level . The model's preci- sion/recall trade-off can be directly con- trolled via one threshold parameter. This feature makes the model more suitable for applications that are not fully statistical. The model's hidden parameters can be eas- ily conditioned on information extrinsic to the model, providing an easy way to inte- grate pre-existing knowledge such as part- of-speech, dictionaries, word order, etc.. Our model can link word tokens in paral- lel texts as well as other translation mod- els in the literature. Unlike other transla- tion models, it can automatically produce dictionary-sized translation lexicons, and it can do so with over 99% accuracy. 1 Introduction Over the past decade, researchers at IBM have devel- oped a series of increasingly sophisticated statistical models for machine translation (Brown et al., 1988; Brown et al., 1990; Brown et al., 1993a). However, the IBM models, which attempt to capture a broad range of translation phenomena, are computation- ally expensive to apply. Table look-up using an ex- plicit translation lexicon is sufficient and preferable for many multilingual NLP applications, including "crummy" MT on the World Wide Web (Church & I-Iovy, 1993), certain machine-assisted translation tools (e.g. (Macklovitch, 1994; Melamed, 1996b)), concordancing for bilingual lexicography (Catizone et al., 1993; Gale & Church, 1991), computer- assisted language learning, corpus linguistics (Melby. 1981), and cross-lingual information retrieval (Oard &Dorr, 1996). In this paper, we present a fast method for in- ducing accurate translation lexicons. The method assumes that words are translated one-to-one. This assumption reduces the explanatory power of our model in comparison to the IBM models, but, as shown in Section 3.1, it helps us to avoid what we call indirect associations, a major source of errors in other models. Section 3.1 also shows how the one- to-one assumption enables us to use a new greedy competitive linking algorithm for re-estimating the model's parameters, instead of more expensive algo- rithms that consider a much larger set of word cor- respondence possibilities. The model uses two hid- den parameters to estimate the confidence of its own predictions. The confidence estimates enable direct control of the balance between the model's preci- sion and recall via a simple threshold. The hidden parameters can be conditioned on prior knowledge about the bitext to improve the model's accuracy. 2 Co-occurrence With the exception of (Fung, 1998b), previous methods for automatically constructing statistical translation models begin by looking at word co- occurrence frequencies in bitexts (Gale & Church, 1991; Kumano & Hirakawa, 1994; Fung, 1998a; Melamed, 1995). A bitext comprises a pair of …

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