Pattern-Based Context-Free Grammars for Machine Translation

Pattern-Based Context-Free Grammars for Machine Translation Koichi Takeda Tokyo Research Laboratory, IBM Research 1623-14 Shimotsuruma, Yamato, Kanagawa 242, Japan Phone: 81-462-73-4569, 81-462-73-7413 (FAX) takeda@trl, vnet. ibm. com Abstract This paper proposes the use of "pattern- based" context-free grammars as a basis for building machine translation (MT) sys- tems, which are now being adopted as per- sonal tools by a broad range of users in the cyberspace society. We discuss ma- jor requirements for such tools, including easy customization for diverse domains, the efficiency of the translation algorithm, and scalability (incremental improvement in translation quality through user interac- tion), and describe how our approach meets these requirements. 1 Introduction With the explosive growth of the World-Wide Web (WWW) as information source, it has become rou- tine for Internet users to access textual data written in foreign languages. In Japan, for example, a dozen or so inexpensive MT tools have recently been put on the market to help PC users understand English text in WWW home pages. The MT techniques em- ployed in the tools, however, are fairly conventional. For reasons of affordability, their designers appear to have made no attempt to tackle the well-known problems in MT, such as how to ensure the learnabil- ity of correct translations and facilitate customiza- tion. As a result, users are forced to see the same kinds of translation errors over and over again, ex- cept they in cases where they involve merely adding a missing word or compound to a user dictionary, or specifying one of several word-to-word translations as a correct choice. There are several alternative approaches that might eventually liberate us from this limitation on the usability of MT systems: Unification-based grammar for- malisms and lexical-semantics formalisms (see LFG (Kaplan and Bresnan, 1982), HPSG (Pollard and Sag, 1987), and Generative Lexicon (Pustejovsky, 1991), for example) have been proposed to facili- tate computationally precise description of natural- language syntax and semantics. It is possible that, with the descriptive power of these grammars and lexicons, individual usages of words and phrases may be defined specifically enough to give correct trans- lations. Practical implementation of MT systems based on these formalisms, on the other hand, would not be possible without much more efficient parsing and disambiguation algorithms for these formalisms and a method for building a lexicon that is easy even for novices to use. Corpus-based or example-based MT (Sato and Nagao, 1990; Sumita and Iida, 1991) and statisti- cal MT (Brown et al., 1993) systems provide the easiest customizability, since users have only to sup- ply a collection of source and target sentence pairs (a bilingual corpus). Two open questions, however, have yet to be satisfactorily answered before we can confidently build commercial MT systems based on these approaches: • Can the system be used for various domains without showing severe degradation of transla- tion accuracy? • What is the minimum number of examples (or training data) required to achieve reasonable MT quality for a new domain? TAG-based MT (Abeill~, Schabes, and Joshi, 1990) …

Content truncated to 500 words