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<Paper uid="P99-1030">
  <Title>Analysis System of Speech Acts and Discourse Structures Using Maximum Entropy Model*</Title>
  <Section position="1" start_page="0" end_page="230" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> We propose a statistical dialogue analysis model to determine discourse structures as well as speech acts using maximum entropy model. The model can automatically acquire probabilistic discourse knowledge from a discourse tagged corpus to resolve ambiguities. We propose the idea of tagging discourse segment boundaries to represent the structural information of discourse.</Paragraph>
    <Paragraph position="1"> Using this representation we can effectively combine speech act analysis and discourse structure analysis in one framework.</Paragraph>
    <Paragraph position="2"> Introduction To understand a natural language dialogue, a computer system must be sensitive to the speaker's intentions indicated through utterances. Since identifying the speech acts of utterances is very important to identify speaker's intentions, it is an essential part of a dialogue analysis system. It is difficult, however, to infer the speech act from a surface utterance since an utterance may represent more than one speech act according to the context. Most works done in the past on the dialogue analysis has analyzed speech acts based on knowledge such as recipes for plan inference and domain specific knowledge (Litman (1987), Caberry (1989), Hinkelman (1990), Lambert (1991), Lambert (1993), Lee (1998)). Since these knowledge-based models depend on costly hand-crafted knowledge, these models are difficult to be scaled up and expanded to other domains.</Paragraph>
    <Paragraph position="3"> Recently, machine learning models using a discourse tagged corpus are utilized to analyze speech acts in order to overcome such problems (Nagata (1994a), Nagata (1994b), Reithinger (1997), Lee (1997), Samuel (1998)). Machine learning offers promise as a means of associating features of utterances with particular speech acts, since computers can automatically analyze large quantities of data and consider many different feature interactions. These models are based on the features such as cue phrases, change of speaker, short utterances, utterance length, speech acts tag n-grams, and word n-grams, etc. Especially, in many cases, the speech act of an utterance influenced by the context of the utterance, i.e., previous utterances. So it is very important to reflect the information about the context to the model.</Paragraph>
    <Paragraph position="4"> Discourse structures of dialogues are usually represented as hierarchical structures, which reflect embedding sub-dialogues (Grosz (1986)) and provide very useful context for speech act analysis. For example, utterance 7 in Figure 1 has several surface speech acts such as acknowledge, inform, and response. Such an ambiguity can be solved by analyzing the context. If we consider the n utterances linearly adjacent to utterance 7, i.e., utterances 6, 5, etc., as context, we will get acknowledge or inform with high probabilities as the speech act of utterance 7. However, as shown in Figure 1, utterance 7 is a response utterance to utterance 2 that is hierarchically recent to utterance 7 according to the discourse structure of the dialogue. If we know the discourse structure of the dialogue, we can determine the speech act of  Some researchers have used the structural information of discourse to the speech act analysis (Lee (1997), Lee (1998)). It is not, however, enough to cover various dialogues since they used a restricted rule-based model such as RDTN (Recursive Dialogue Transition Networks) for discourse structure analysis. Most of the previous related works, to our knowledge, tried to determine the speech act of an utterance, but did not mention about statistical models to determine the discourse structure of a dialogue.  I )User : I would like Io reserve a room.</Paragraph>
    <Paragraph position="5"> 2) Agent : What kind of room do you want? 3) User : What kind of room do you have'? 4) Agent : We have single mid double rooms.</Paragraph>
    <Paragraph position="6"> 5) User : How much are those rooms? 6) Agent : Single costs 30,000 won and double ~SlS 40,000 WOll.  In this paper, we propose a dialogue analysis model to determine both the speech acts of utterances and the discourse structure of a dialogue using maximum entropy model. In the proposed model, the speech act analysis and the discourse structure analysis are combined in one framework so that they can easily provide feedback to each other. For the discourse structure analysis, we suggest a statistical model with discourse segment boundaries (DSBs) similar to the idea of gaps suggested for a statistical parsing (Collins (1996)). For training, we use a corpus tagged with various discourse knowledge. To overcome the problem of data sparseness, which is common for corpus-based works, we use split partial context as well as whole context.</Paragraph>
    <Paragraph position="7"> After explaining the tagged dialogue corpus we used in section 1, we discuss the statistical models in detail in section 2. In section 3, we explain experimental results. Finally, we conclude in section 4.</Paragraph>
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