<?xml version="1.0" standalone="yes"?>
<Paper uid="P95-1016">
  <Title>Utilizing Statistical Dialogue Act Processing in Verbmobil</Title>
  <Section position="2" start_page="0" end_page="116" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Extracting and processing communicative intentions behind natural language utterances plays an important role in natural language systems (see e.g.</Paragraph>
    <Paragraph position="1"> (Cohen et al., 1990; Hinkelman and Spackman, 1994)). Within the speech-to-speech translation system VERBMOBIL (Wahlster, 1993; Kay et al., 1994), dialogue acts are used as the basis for the treatment of intentions in dialogues. The representation of intentions in the VERBMOBIL system serves two main purposes: * Utilizing the dialogue act of an utterance as an important knowledge source for translation yields a faster and often qualitative better translation than a method that depends on surface expressions only. This is the case especially in the first application of VV.RBMOBIL, the on-demand translation of appointment scheduling dialogues.</Paragraph>
    <Paragraph position="2"> * Another use of dialogue act processing in VERBMOBIL is the prediction of follow-up dialogue acts to narrow down the search space on the analysis side. For example, dialogue act predictions are employed to allow for dynamically adaptable language models in word recognition.</Paragraph>
    <Paragraph position="3"> *This work was funded by the German Federal Ministry 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 Alexandersson for valuable comments and suggestions on earlier drafts of this paper.</Paragraph>
    <Paragraph position="4"> 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.</Paragraph>
    <Paragraph position="5"> 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 VERBMOBIL. null In the other processing mode the diMogue component 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 every utterance the dialogue component supplies the keyword spotter with a prediction of the most likely follow-up dialogue act and the situationdependent keywords.</Paragraph>
    <Paragraph position="6"> The dialogue component uses a combination of statistical and knowledge based approaches to process dialogue acts and to maintain and to provide contextual information for the other modules of VERBMOBIL (Maier and McGlashan, 1994). It includes a robust dialogue plan recognizing module, which uses repair techniques to treat unexpected dialogue steps. The information acquired during dialogue processing is stored in a dialogue memory.</Paragraph>
    <Paragraph position="7"> This contextual information is decomposed into the intentional structure, the referential structure, and the temporal structure which refers to the dates mentioned in the dialogue.</Paragraph>
    <Paragraph position="8">  An overview of the dialogue component is given in (Alexandersson et al., 1995). In this paper main emphasis is on statistical dialogue act prediction in VEFtBMOBIL, with an evaluation of the method, and an example of the interaction between plan recognition and statistical dialogue act prediction.</Paragraph>
  </Section>
class="xml-element"></Paper>