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<Paper uid="P95-1016">
  <Title>Utilizing Statistical Dialogue Act Processing in Verbmobil</Title>
  <Section position="5" start_page="119" end_page="119" type="concl">
    <SectionTitle>
5 Conclusion
</SectionTitle>
    <Paragraph position="0"> This paper presents the method for statistical dialogue act prediction currently used in the dialogue component of VERBMOBIL. It presents plan repair as one example of its use.</Paragraph>
    <Paragraph position="1"> The analysis of the statistical method shows that the prediction algorithm shows satisfactory results when deviations from the main dialogue model are excluded. If dialogue acts for deviations are included, the prediction rate drops around 10%. The analysis of the hit rate shows also a large variation in the structure of the dialogues from the corpus.</Paragraph>
    <Paragraph position="2"> We currently integrate the speaker direction into the prediction process which results in a gain of up to 5 % in the prediction hit rate. Additionally, we investigate methods to cluster training dialogues in classes with a similar structure.</Paragraph>
    <Paragraph position="3"> An important application of the statistical prediction is the repair mechanism of the dialogue plan recognizer. The mechanism proposed here contributes to the robustness of the whole VERBMOBIL system insofar as it is able to recognize cases where dialogue act attribution has delivered incorrect or insufficient results. This is especially important because the input given to the dialogue component is unreliable when dialogue act information is computed via the keyword spotter. Additional dialogue act readings can be proposed and the dialogue history can be changed accordingly.</Paragraph>
    <Paragraph position="4"> Currently, the dialogue component processes more than 200 annotated dialogues from the VERBMOBIL corpus. For each of these dialogues, the plan recognizer builds a dialogue tree structure, using the method presented in section 4, even if the dialogue structure is inconsistent with the dialogue model.</Paragraph>
    <Paragraph position="5"> Therefore, our model provides robust techniques for the processing of even highly unexpected dialogue contributions.</Paragraph>
    <Paragraph position="6"> In a next version of the system it is envisaged that the semantic evaluation component and the keyword spotter are able to attribute a set of dialogue acts with their respective probabilities to an utterance.</Paragraph>
    <Paragraph position="7"> Also, the plan operators will be augmented with statistical information so that the selection of the best possible follow-up dialogue acts can be retrieved by using additional information from the plan recognizer itself.</Paragraph>
  </Section>
class="xml-element"></Paper>