Ambiguity Preserving Machine Translation using Packed Representations

Ambiguity Preserving Machine Translation using Packed Representations* Martin C. Emele and Michael Dorna IMS, Institut fiir Maschinelle Sprachverarbeitung Universit~it Stuttgart Azenbergstrai~e 12 D-70174 Stuttgart ~emele, dorna}@ims, tmi-stuttgart, de Abstract In this paper we present an ambiguity preserv- ing translation approach which transfers am- biguous LFG f-structure representations. It is based on packed f-structure representations which are the result of potentially ambiguous utterances. If the ambiguities between source and target language can be preserved, no un- packing during transfer is necessary and the generator may produce utterances which max- imally cover the underlying ambiguities. We convert the packed f-structure descriptions into a flat set of prolog terms which consist of pred- icates, their predicate argument structure and additional attribute-value information. Ambi- guity is expressed via local disjunctions. The flat representations facilitate the application of a Shake-and-Bake like transfer approach extended to deal with packed ambiguities. 1 Introduction It is a central problem for any practical NLP system and specifically for any machine trans- lation (MT) system to deal with ambiguity of natural language utterances. This is especially true for systems with large coverage grammars, where the number of potentially ambiguous de- scriptions grows drammatically as the number of acceptable syntactic constructions and the number of lexical readings increases. In gen- eral, it is not possible to resolve all potentially ambiguous descriptions without incorporating world knowledge of unlimited size. This funda- mental problem has been discussed in the litera- * We would like to thank our colleagues at Xerox PARC and Xerox RCE for fruitful discussions and the anony- mous reviewers for valuable feedback. This work was funded by the German Federal Ministry of Education, Science, Research and Technology (BMBF) in the frame- work of the Verbmobil project under grant 01 IV 701 N3. ture as the AI completeness problem (cf. Kay et al. (1994)). Nevertheless, it has been observed that many ambiguous utterances in the source language (SL) text can be translated by equiva- lently ambiguous phrases in the target language (TL) text. We call such an ambiguity a preserv- able ambiguity and the corresponding architec- ture for translation an ambiguity preserving MT approach. In order to achieve this goal of ambiguity pre- serving translations there exist a number of dif- ferent solutions we can apply. A naive solu- tion would enumerate all possible ambiguous de- scriptions, translate them and generate the cor- responding target utterances which would then be intersected to find a common string which covers all meanings. This strategy is obvi- ously not feasible because the number of poten- tial readings might grow exponentially with the length of the sentence. Another solution to overcome this problem is not to resolve ambiguities at all by using un- derspecified representations. This strategy has been successfully applied for a number of se- mantic ambiguities like quantifier and operator scope ambiguities. Therefore it is not surpris- ing that the usage of underspecified semantic representations have gained much popularity in recent years. Work in the literature include the QLF representations (Alshawi, 1992), the …

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