Discriminative Methods for Transliteration

Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing (EMNLP 2006), pages 612–617, Sydney, July 2006. c⃝2006 Association for Computational Linguistics Discriminative Methods for Transliteration Dmitry Zelenko SRA International 4300 Fair Lakes Ct. Fairfax VA 22033 dmitry_zelenko@sra.com Chinatsu Aone SRA International 4300 Fair Lakes Ct. Fairfax VA 22033 chinatsu_aone@sra.com Abstract We present two discriminative methods for name transliteration. The methods correspond to local and global modeling approaches in modeling structured output spaces. Both methods do not require alignment of names in different lan- guages – their features are computed di- rectly from the names themselves. We perform an experimental evaluation of the methods for name transliteration from three languages (Arabic, Korean, and Russian) into English, and compare the methods experimentally to a state-of-the- art joint probabilistic modeling approach. We find that the discriminative methods outperform probabilistic modeling, with the global discriminative modeling ap- proach achieving the best performance in all languages. 1 Introduction Name transliteration is an important task of tran- scribing a name from alphabet to another. For example, an Arabic “مﺎﻴﻟو”, Korean “윌리엄”, and Russian “Вильям” all correspond to English “William”. We address the problem of translit- eration in the general setting: it involves trying to recover original English names from their tran- scription in a foreign language, as well as finding an acceptable spelling of a foreign name in Eng- lish. We apply name transliteration in the context of cross-lingual information extraction. Name extractors are currently available in multiple lan- guages. Our goal is to make the extracted names understandable to monolingual English speakers by transliterating the names into English. The extraction context of the transliteration application imposes additional complexity con- straints on the task. In particular, we aim for the transliteration speed to be comparable to that of extraction speed. Since most current extraction systems are fairly fast (>1 Gb of text per hour), the complexity requirement reduces the range of techniques applicable to the transliteration. More precisely, we cannot use WWW and the web count information to hone in on the right translit- eration candidate. Instead, all relevant translitera- tion information has to be represented within a compact and self-contained transliteration model. We present two methods for creating and ap- plying transliteration models. In contrast to most previous transliteration approaches, our models are discriminative. Using an existing translitera- tion dictionary D (a set of name pairs {(f,e)}), we learn a function that directly maps a name f from one language into a name e in another language. We do not estimate either direct conditional p(e|f) or reverse conditional p(f|e) or joint p(e,f) probability models. Furthermore, we do away with the notion of alignment: our transliteration model does not require and is not defined of in terms of aligned e and f. Instead, all features used by the model are computed directly from the names f and e without any need for their alignment. The two discriminative methods that we pre- sent correspond to local and global modeling paradigms for solving complex learning …

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