tailieunhanh - Báo cáo khoa học: "Predicting Success in Dialogue"

Task-solving in dialogue depends on the linguistic alignment of the interlocutors, which Pickering & Garrod (2004) have suggested to be based on mechanistic repetition effects. In this paper, we seek confirmation of this hypothesis by looking at repetition in corpora, and whether repetition is correlated with task success. We show that the relevant repetition tendency is based on slow adaptation rather than short-term priming and demonstrate that lexical and syntactic repetition is a reliable predictor of task success given the first five minutes of a taskoriented dialogue. . | Predicting Success in Dialogue David Reitter and Johanna D. Moore dreitter jmoore @ School of Informatics University of Edinburgh United Kingdom Abstract Task-solving in dialogue depends on the linguistic alignment of the interlocutors which Pickering Garrod 2004 have suggested to be based on mechanistic repetition effects. In this paper we seek confirmation of this hypothesis by looking at repetition in corpora and whether repetition is correlated with task success. We show that the relevant repetition tendency is based on slow adaptation rather than short-term priming and demonstrate that lexical and syntactic repetition is a reliable predictor of task success given the first five minutes of a task-oriented dialogue. 1 Introduction While humans are remarkably efficient flexible and reliable communicators we are far from perfect. Our dialogues differ in how successfully information is conveyed. In task-oriented dialogue where the interlocutors are communicating to solve a problem task success is a crucial indicator of the success of the communication. An automatic measure of task success would be useful for evaluating conversations among humans . for evaluating agents in a call center. In humancomputer dialogues predicting the task success after just a first few turns of the conversation could avoid disappointment if the conversation isn t going well a caller may be passed on to a human operator or the system may switch dialogue strategies. As a first step we focus on human-human dialogue since cur 808 rent spoken dialogue systems do not yet yield long syntactically complex conversations. In this paper we use syntactic and lexical features to predict task success in an environment where we assume no speaker model no semantic information and no information typical for a human-computer dialogue system . ASR confidence. The features we use are based on a psychological theory linking alignment between dialogue participants to low-level syntactic .

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• Predicting Success in Dialogue
• David Reitter and Johanna D
• Moore
• Long Papers
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