Reversible Machine Translation: What to Do When the Languages Don’t Line Up

REVERSIBLE MACHINE TRANSLATION: WHAT TO DO WHEN THE LANGUAGES DON'T LINE UP James Barnett, Inderjeet Mani, Paul Martin, and Elaine Rich MCC 3500 West Balcones Center Dr. Austin, TX 78759 'Abstract In this paper ,we deal with issues that face an interlingua-based, reversible machine translation system when the~ literal meaning of the source text is not identical to the literal meaning of the nat- ural target translation. We present an algorithm for lexical choice that handles such cases and that relies exclusively, on reversible, monolingual lin- guistic descriptions and a language-independent 1 domain knowledge base. Introduction Machine translation is an obvious application for reversible natural language systems, since both understanding and generation are important parts of the process. There are several arguments for this view (for e:kample, [Isabelle, 89]), including reducing the total cost of adding a new language and making it easier to maintain and validate the resulting system'. Reversible MT systems, just like the broader class of MT systems as a whole, fall into two roughly defined families: transfer systems and in- terlingua (or pivot systems). Reversible trans- fer systems (e.g., [van Noord, 90], [Zajac, 90], [Dymetman, 8811 and [Strzalkowski, 90]) exploit three reversible subsystems: one to analyze the source text, one! to perform the transfer, and a third to generate the target text. Interlingua- based systems (e.g., Ultra [Farwell, 90]), on the other hand, require only two reversible compo- nents: one to analyze the source text into the in- terlingua representation, and one to generate the target text from that representation. In this pa- per, we will focus on issues that arise in the design of interlingua-based MT systems. The simplest model of a reversible, interlingua- based system contains two components: one ana- lyzes the source text to create the interlingua rep- resentation and the other maps from that to the target text. Unfortunately, the real situation is 61 not that simple, for several reasons, including two that we will focus on here: • This model assumes that the same infor- mation is present in the target text as in the source. But in some eases, which have been called translation mismatches [Kameyama, 91], information is either added to or deleted from the source in creating the target. We will show some examples of this below in Section 2. In these eases, the sim- ple reversible system we outlined above would produce unacceptable translations. • Although the notion of a reversible system that describes the set of legal translations is reasonably clearcut, the notion of pre- ferred translation is more difficult to de- fine [van Noord, 90], [Barnett, 91d]. In some cases, which have been called translation di- vergences [Dorr, 90], the most natural trans- lation differs from the source in some signifi- cant way (e.g., its focus). Of course, in many cases, both of these issues occur together and interact. In this paper, we present some techniques for dealing with these problems. These techniques have three impor- tant properties: They require purely declarative, reversible descriptions of …

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