Interactive MT as support for non-native language authoring

MT Summit VII Sept. 1999 Interactive MT As Support For Non-Native Language Authoring Svetlana Sheremetyeva and Sergei Nirenburg Computing Research Laboratory New Mexico State University Abstract The paper describes an approach to developing an interactive MT system for translating technical texts on the example of translating patent claims between Russian and English. The approach conforms to the human-aided machine translation paradigm. The sys- tem is meant for a source language (SL) speaker who does not know the target language (TL). It consists of i) an analysis module which includes a submodule of interactive syntactic analysis of SL text and a sub- module of fully automated morphological analysis, ii) an automatic module for transferring the lexical and partially syntactic content of SL text into a simi- lar content of the TL text and iii) a fully automated TL text generation module which relies on knowl- edge about the legal format of TL patent claims. An interactive analysis module guides the user through a sequence of SL analysis procedures, as a result of which the system produces a set of internal knowl- edge structures which serve as input to the TL text generation. Both analysis and generation rely heavily on the analysis of the sublanguage of patent claims. The model has been developed for English and Rus- sian as both SLs and TLs but is readily extensible to other languages. 1 Introduction Translating patents is an important task for international trade and industry. In a patent text (or "disclosure." using official terminology), the crucial part is the patent claim which is the actual subject of legal protection. Analysis synthesis and translation of claims are time-consuming tasks even for experts. The initial functionality of our patent specialist's workstation (Sheremetyeva and Nirenburg 1996b) was centered on claim text composition. The project we describe here extends that functionality to translation of patent claims. General purpose MT systems have the advantage of being potentially reusable; this reusability is not. however, guar- anteed. A study by Bourbeau and Kittredge (1988) demonstrated that no existing NLP system could process patent texts adequately. It is generally recognized, how- ever, (see, e.g.. Cowie and Lehnert 1996) that an MT system providing adequate performance even for a single type of text should be considered useful. If an MT system uses a restricted sublanguage—and. thus, can operate with smaller-scale static knowledge sources—the scope of acqui- sition and development effort will decrease correspondingly. Indeed, practically all MT systems for special domains are usually (see, e.g.. Kukich 1983; Kit- tredge et al., 1986) built to conform to the constraints of a sublanguage. Massive attempts have been made in the past ten years or so to make MT systems fully automatic (e.g.. P. Brown et al., 1988). In practice, the state of the art in NLP suggests a mixture of automatic and manual methods for any realistic comprehensive application (e.g.. Paris et al., 1995: Niren- burg et al., 1996). Several modes of human-computer cooperation have been used in practice over the years. Our approach conforms …

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