Machine Translation and World Knowledge. Many existing ap-

proaches to machine translation take for granted that the information pre-sented in the output is found somewhere in the input, and, moreover, that such information should be expressed at a single representational level, say, in terms of the parse trees or of \semantic " assertions. Languages, how-ever, not only express the equivalent information by drastically dierent linguistic means, but also often disagree in what distinctions should be ex-pressed linguistically at all. For example, in translating from Japanese to English, it is often necessary to supply determiners for noun phrases, and this in general cannot be done without deep understanding of the source text. Similarly, in translating from English to Japanese, politeness consid-erations, which in English are implicit in the social situation and explicit in very diuse ways in, for example, the heavy use of hypotheticals, must be realized grammatically in Japanese. Machine translation therefore requires that the appropriate inferences be drawn and that the text be interpreted to some depth (see Oviatt, 1988). Recently, an elegant approach to inference in discourse interpretation has been developed at a number of sites (e.g., Hobbs et al., 1988; Charniak and Goldman, 1988; Norvig, 1987), all based on the notion of abduction, and we have begun to explore its potential ap-plication to machine translation. We argue that this approach provides the possibility of deep reasoning and of mapping between the languages at a variety of levels. (See also Kaplan et al., 1988, on the latter point.) 1 Interpretation as Abduction. Abductive inference is inference to the best explanation. The easiest way to understand it is to compare it with 1 The authors have proted from discussions about this work with Mark Stickel and with the participants in the Translation Group at CSLI. The research was funded b