A Comparison of Head Transducers and Transfer for a Limited Domain Translation Application

A Comparison of Head Transducers and Transfer for a Limited Domain Translation Application Hiyan Alshawi and Adam L. Buchsbaum AT&T Labs 180 Park Avenue Florham Park. NJ 079:32-0971. USA {hiyan,alb}.~research.at t.com Abstract We compare the effectiveness of two related • machine translation models applied to the same limited-domain task. One is a trans- fer model with monolingual head automata for analysis and generation; the other is a direct transduction model based on bilin- gual head transducers. We conclude that the head transducer model is more effective according to measures of accuracy, compu- tational requirements, model size, and de- velopment effort. I Introduction In this paper we describe an experimental ma- chine translation system based on head transducer models and compare it to a related transfer sys- tem, described in Alshawi 1996a, based on mono- lingual head automata. Head transducer models consist of collections of finite state machines that are associated with pairs of lexical items in a bilin- gual lexicon. The transfer system follows the fa- miliar analysis-transfer-generation architecture (Is- abelle and Macklovitch 1986). with mapping of dependency representations (Hudson 1984)in the transfer phase. In contrast, the head transducer approach is more closely aligned with earlier di- rect translation methods: no explicit representa- tions of the source language (interlingua or other- wise) are created in the process of deriving the target string. Despite ~he simple direct architecture, the head transducer model does embody modern prin- ciples of lexicalized recursive grammars and statis- tical language processing. The context for evaluat- ing both the transducer and transfer models was the development of experimental prototypes for speech- to-speech translation. In the case of text translation for publishing, it is reasonable to adopt economic measures of the Fei Xia Department of Computer and Information Science University of Pennsylvania Philadelphia, PA 19104. USA fxia@cis.upenn.edu effectiveness of translation systems. This involves assessing the total cost .,f employing a '~ransiation system, including, for example, the cost of manual post-editing. Post-editing "s not an option in speech translation systems for person-to-person communi- cation, and real-time operation is important in this context, so in comparing the two translation models we looked at a variety of other measures, including translation accuracy, speed, and system complexity. Both models underlying the translation systems can be characterized as statistical translation mod- els, but unlike the models proposed by Brown et al. (1990, 1993), these models have non-uniform lin- guistically motivated structure, at present coded by hand. In fact, the original motivation for the head transducer models was that they are simpler and more amenable to automatic model structure acqui- sition, while the transfer component of the tradi- tional system was designed with regard to allowing maximum flexibility in mapping between source and target representations to overcome translation diver- gences (Lindop and Tsujii 1991: Dorr 1994). In prac- tice, it turned out that adopting the simpler trans- ducer models did not invoive sacrificing accuracy, at least for our limited domain application. We first describe the transfer and head transducer approaches in Sections 2 and …

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