Automatic Discovery of Non-Compositional Compounds in Parallel Data

Automatic Discovery of Non-Compositional Compounds in Parallel Data * I. Dan Melamed Dept. of Computer and Information Science University of Pennsylvania Philadelphia, PA, 19104, U.S.A. melamed~unagi, cis. upenn, edu http ://www. cis. upenn, edu/"melamed Abstract Automatic segmentation of text into min- imal content-bearing units is an unsolved problem even for languages like English. Spaces between words offer an easy first ap- proximation, but this approximation is not good enough for machine translation (MT), where many word sequences are not trans- lated word-for-word. This paper presents an efficient automatic method for discover- ing sequences of words that are translated as a unit. The method proceeds by com- paring pairs of statistical translation mod- els induced from parallel texts in two lan- guages. It can discover hundreds of non- compositional compounds on each itera- tion, and constructs longer compounds out of shorter ones. Objective evaluation on a simple machine translation task has shown the method's potential to improve the qual- ity of MT output. The method makes few assumptions about the data, so it can be applied to parallel data other than parallel texts, such as word spellings and pronunci- ations. 1 Introduction The optimal way to analyze linguistic data into its primitive elements is rarely obvious but often crucial. Identifying phones and words in speech has been a major focus of research. Automati- cally finding words in text, the problem addressed here, is largely unsolved for languages such as Chi- nese and Thai, which are written without spaces * Many thanks to Mike Collins, Jason Eisner, Mitch Marcus and two anonymous reviewers for their feedback on earlier drafts of this paper. This research was sup- ported by an equipment grant from Sun MicroSystems and by ARPA Contract #N66001-94C-6043. (but see Fung & Wu, 1994; Sproat et al., 1996). Spaces in texts of languages like English offer an easy first approximation to minimal content-bearing units. However, this approximation mis-analyzes non-compositional compounds (NCCs) such as "kick the bucket" and "hot dog." NCCs are com- pound words whose meanings are a matter of con- vention and cannot be synthesized from the mean- ings of their space-delimited components. Treating NCCs as multiple words degrades the performance of machine translation (MT), information retrieval, natural language generation, and most other NLP applications. NCCs are usually not translated literally to other languages. Therefore, one way to discover NCCs is to induce and analyze a translation model between two languages. This paper is about an information- theoretic approach to this kind of ontological dis- covery. The method is based on the insight that treatment of NCCs as multiple words reduces the predictive power of translation models. Whether a given sequence of words is an NCC can be de- termined by comparing the predictive power of two translation models that differ on whether they treat the word sequence as an NCC. Searching a space of data models in this manner has been proposed be- fore, e.g. by Brown et al. (1992) and Wang et al. (1996), but their particular methods …

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