TAILIEUCHUNG - Báo cáo khoa học: "Ambiguity Preserving Machine Translation using Packed Representations*"

In this paper we present an ambiguity preserving translation approach which transfers ambiguous 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 unpacking during transfer is necessary and the generator may produce utterances which maximally cover the underlying ambiguities. We convert the packed f-structure descriptions into a flat set of prolog terms which consist of predicates, their predicate argument structure and additional attribute-value information. . | Ambiguity Preserving Machine Translation using Packed Representations Martin c. Emele and Michael Dorna IMS Institut fur Maschinelle Sprachverarbeitung Universitat Stuttgart AzenbergstraEe 12 D-70174 Stuttgart emele dornajfflims. uni-stuttgart. de Abstract In this paper we present an ambiguity preserving translation approach which transfers ambiguous 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 unpacking during transfer is necessary and the generator may produce utterances which maximally cover the underlying ambiguities. We convert the packed f-structure descriptions into a flat set of prolog terms which consist of predicates their predicate argument structure and additional attribute-value information. Ambiguity 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 translation 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 descriptions grows drammatically as the number of acceptable syntactic constructions and the number of lexical readings increases. In general it is not possible to resolve all potentially ambiguous descriptions without incorporating world knowledge of unlimited size. This fundamental 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 anonymous reviewers for valuable feedback. This work was funded by the German Federal Ministry of Education Science Research and Technology BMBF in the framework of the Verbmobil project under grant 01 IV 701 N3. ture as

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• Ambiguity Preserving Machine Translation
• Packed Representations
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