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 .

Challenge: 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.
Approach: They present two discriminative methods that learn a map function from one language into another using a dictionary without the notion of alignment.
Outcome: 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.