Analysis System of Speech Acts and Discourse Structures Using Maximum Entropy Model

Analysis System of Speech Acts and Discourse Structures Using Maximum Entropy Model* Won Seug Choi, Jeong-Mi Cho and Jungyun Seo Dept. of Computer Science, Sogang University Sinsu-dong 1, Mapo-gu Seoul, Korea, 121-742 {dolhana, jmcho} @nlprep.sogang.ac.kr, seojy@ccs.sogang.ac.kr Abstract 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. Using this representation we can effectively combine speech act analysis and discourse structure analysis in one framework. 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. 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. 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 utterance 7 as response. * This …

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