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 languages -their features are computed directly 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-theart joint probabilistic modeling approach . We find that the discriminative methods outperform probabilistic modeling , with the global discriminative modeling approach achieving the best performance in all languages . Name transliteration is an important task of transcribing a name from alphabet to another . For example , an Arabic ‫,"وﻟﻴﺎم"‬ Korean " 윌리엄 " , and Russian " Вильям " all correspond to English " William " . We address the problem of transliteration in the general setting : it involves trying to recover original English names from their transcription in a foreign language , as well as finding an acceptable spelling of a foreign name in English . We apply name transliteration in the context of cross-lingual information extraction . Name extractors are currently available in multiple languages . 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 constraints 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 can not use WWW and the web count information to hone in on the right transliteration candidate . Instead , all relevant transliteration information has to be represented within a compact and self-contained transliteration model . We present two methods for creating and applying transliteration models . In contrast to most previous transliteration approaches , our models are discriminative . Using an existing transliteration 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 present correspond to local and global modeling paradigms for solving complex learning problems with structured output spaces . In the local setting , we learn linear classifiers that predict a letter e i from the previously predicted letters e 1 … e i-1 and the original name f. In the global setting , we learn a function W mapping a pair ( f , e ) into a score W(f , e)∈ R. The function W is linear in features computed from the pair ( f , e ) . We describe the pertinent feature spaces as well as pre-sent both training and decoding algorithms for the local and global settings . We perform an experimental evaluation for three language pairs ( transliteration from Arabic , Korean , and Russian into English ) comparing our methods to a joint probabilistic modeling approach to transliteration , which was shown to deliver superior performance . We show experimentally that both discriminative methods outperform the probabilistic approach , with global discriminative modeling achieving the best performance in all languages . We presented two novel discriminative approaches to name transliteration that do not employ the notion of alignment . We showed experimentally that the approaches lead to superior experimental results in all languages , with the global discriminative modeling approach achieving the best performance . The results are somewhat surprising , for the notion of alignment seems very intuitive and useful for transliteration . We will investigate whether similar alignment-free methodology can be extended to full-text translation . It will also be interesting to study the relationship between our discriminative alignment-free methods and recently proposed discriminative alignment-based methods for transliteration and translation ( Taskar et al . 2005a ; Moore 2005 ) . We also showed that for name transliteration , global discriminative modeling is superior to local classifier-based discriminative modeling . This may have resulted from poor calibration of scores and probabilities produced by individual classifiers . We plan to further investigate the relationship between the local and global approaches to complex learning problems in natural language .
The name transliteration task aims to transcribe extracted names into English, and since current extraction systems are fairly fast, applicable techniques for transliteration are limited.
They present two discriminative methods that learn a map function from one language into another using a dictionary without the notion of alignment.
The proposed methods outperform state-of-the-art probabilistic models on name transliteration from Arabic, Korean, and Russian to English, and the global discriminative modelling performs the best.