Utilizing Statistical Dialogue Act Processing in Verbrnobil

Utilizing Statistical Dialogue Act Processing in Verbmobil Norbert Reithinger and Elisabeth Maier* DFKI GmbH Stuhlsatzenhausweg 3 D-66123 Saarbriicken Germany {re±thinger, maier}@dfki, uni- sb. de Abstract In this paper, we present a statistical ap- proach for dialogue act processing in the di- alogue component of the speech-to-speech translation system VERBMOBIL. Statistics in dialogue processing is used to predict follow-up dialogue acts. As an application example we show how it supports repair when unexpected dialogue states occur. 1 Introduction Extracting and processing communicative intentions behind natural language utterances plays an im- portant role in natural language systems (see e.g. (Cohen et al., 1990; Hinkelman and Spackman, 1994)). Within the speech-to-speech translation sys- tem VERBMOBIL (Wahlster, 1993; Kay et al., 1994), dialogue acts are used as the basis for the treatment of intentions in dialogues. The representation of in- tentions in the VERBMOBIL system serves two main purposes: • Utilizing the dialogue act of an utterance as an important knowledge source for transla- tion yields a faster and often qualitative better translation than a method that depends on sur- face expressions only. This is the case especially in the first application of VV.RBMOBIL, the on- demand translation of appointment scheduling dialogues. • Another use of dialogue act processing in VERB- MOBIL is the prediction of follow-up dialogue acts to narrow down the search space on the analysis side. For example, dialogue act pre- dictions are employed to allow for dynamically adaptable language models in word recognition. *This work was funded by the German Federal Min- istry for Education, Research and Technology (BMBF) in the framework of the Verbmohil Project under Grant 01IV101K/1. The responsibility for the contents of this study lies with the authors. Thanks to Jan Alexanders- son for valuable comments and suggestions on earlier drafts of this paper. Recent results (e.g. (Niedermair, 1992)) show a reduction of perplexity in the word recognizer between 19% and 60% when context dependent language models are used. DiMogue act determination in VERBMOBIL is done in two ways, depending on the system mode: using deep or shallow processing. These two modes depend on the fact that VERBMOBIL is only translating on demand, i.e. when the user's knowledge of English is not sufficient to participate in a dialogue. If the user of VERBMOBIL needs translation, she presses a button thereby activating deep processing. In depth processing of an utterance takes place in maximally 50% of the dialogue contributions, namely when the owner speaks German only. DiMogue act extraction from a DRS-based semantic representation (Bos et al., 1994) is only possible in this mode and is the task of the semantic evaluation component of VERB- MOBIL. In the other processing mode the diMogue com- ponent tries to process the English passages of the diMogue by using a keyword spotter that tracks the ongoing dialogue superficiMly. Since the keyword spotter only works reliably for a vocabulary of some ten words, it has to be provided with keywords which typically occur in utterances of the same diMogue act type; for …

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