Learning Bilingual Lexicons from Monolingual Corpora

Proceedings of ACL-08: HLT, pages 771–779, Columbus, Ohio, USA, June 2008. c⃝2008 Association for Computational Linguistics Learning Bilingual Lexicons from Monolingual Corpora Aria Haghighi, Percy Liang, Taylor Berg-Kirkpatrick and Dan Klein Computer Science Division, University of California at Berkeley { aria42,pliang,tberg,klein }@cs.berkeley.edu Abstract We present a method for learning bilingual translation lexicons from monolingual cor- pora. Word types in each language are charac- terized by purely monolingual features, such as context counts and orthographic substrings. Translations are induced using a generative model based on canonical correlation analy- sis, which explains the monolingual lexicons in terms of latent matchings. We show that high-precision lexicons can be learned in a va- riety of language pairs and from a range of corpus types. 1 Introduction Current statistical machine translation systems use parallel corpora to induce translation correspon- dences, whether those correspondences be at the level of phrases (Koehn, 2004), treelets (Galley et al., 2006), or simply single words (Brown et al., 1994). Although parallel text is plentiful for some language pairs such as English-Chinese or English- Arabic, it is scarce or even non-existent for most others, such as English-Hindi or French-Japanese. Moreover, parallel text could be scarce for a lan- guage pair even if monolingual data is readily avail- able for both languages. In this paper, we consider the problem of learning translations from monolingual sources alone. This task, though clearly more difficult than the standard parallel text approach, can operate on language pairs and in domains where standard approaches cannot. We take as input two monolingual corpora and per- haps some seed translations, and we produce as out- put a bilingual lexicon, defined as a list of word pairs deemed to be word-level translations. Preci- sion and recall are then measured over these bilin- gual lexicons. This setting has been considered be- fore, most notably in Koehn and Knight (2002) and Fung (1995), but the current paper is the first to use a probabilistic model and present results across a va- riety of language pairs and data conditions. In our method, we represent each language as a monolingual lexicon (see figure 2): a list of word types characterized by monolingual feature vectors, such as context counts, orthographic substrings, and so on (section 5). We define a generative model over (1) a source lexicon, (2) a target lexicon, and (3) a matching between them (section 2). Our model is based on canonical correlation analysis (CCA)1 and explains matched word pairs via vectors in a com- mon latent space. Inference in the model is done using an EM-style algorithm (section 3). Somewhat surprisingly, we show that it is pos- sible to learn or extend a translation lexicon us- ing monolingual corpora alone, in a variety of lan- guages and using a variety of corpora, even in the absence of orthographic features. As might be ex- pected, the task is harder when no seed lexicon is provided, when the languages are strongly diver- gent, or when the monolingual corpora are from dif- ferent domains. Nonetheless, …

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