Tracking Initiative in Collaborative Dialogue Interactions

Tracking Initiative in Collaborative Dialogue Interactions Jennifer Chu-Carroll and Michael K. Brown Bell Laboratories Lucent Technologies 600 Mountain Avenue Murray Hill, NJ 07974, U.S.A. E-mail: {jencc,mkb} @ bell-labs.corn Abstract In this paper, we argue for the need to dis- tinguish between task and dialogue initiatives, and present a model for tracking shifts in both types of initiatives in dialogue interactions. Our model predicts the initiative holders in the next dialogue turn based on the current initia- tive holders and the effect that observed cues have on changing them. Our evaluation across various corpora shows that the use of cues con- sistently improves the accuracy in the system' s prediction of task and dialogue initiative hold- ers by 2-4 and 8-13 percentage points, respec- tively, thus illustrating the generality of our model. 1 Introduction Naturally-occurring collaborative dialogues are very rarely, if ever, one-sided. Instead, initiative of the in- teraction shifts among participants in a primarily princi- pled fashion, signaled by features such as linguistic cues, prosodic cues and, in face-to-face interactions, eye gaze aad gestures. Thus, for a dialogue system to interact with its user in a natural and coherent manner, it must recog- nize the user's cues for initiative shifts and provide ap- propriate cues in its responses to user utterances. Previous work on mixed-initiative dialogues focused on tracking a single thread of control among participants. We argue that this view of initiative fails to distinguish between task initiative and dialogue initiative, which to- gether determine when and how an agent will address an issue. Although physical cues, such as gestures and eye gaze, play an important role in coordinating initia- tive shifts in face-to-face interactions, a great deal of information regarding initiative shifts can be extracted from utterances based on linguistic and domain knowl- edge alone. By taking into account such cues during dia- logue interactions, the system is better able to determine the task and dialogue initiative holders for each turn and to tailor its response to user utterances accordingly. In this paper, we show how distinguishing between task and dialogue initiatives accounts for phenomena in collaborative dialogues that previous models were unable to explain. We show that a set of cues, which can be recognized based on linguistic and domain knowledge alone, can be utilized by a model for tracking initiative to predict the task and dialogue initiative holders with 99.1% and 87.8% accuracies, respectively, in collabo- rative planning dialogues. Furthermore, application of our model to dialogues in various other collaborative en- vironments consistently increases the accuracies in the prediction of task and dialogue initiative holders by 2-4 and 8-13 percentage points, respectively, compared to a simple prediction method without the use of cues, thus illustrating the generality of our model. 2 Task Initiative vs. Dialogue Initiative 2.1 Motivation Previous work on mixed-initiative dialogues focused on tracking and allocating a single thread of control, the conversational lead, among participants. Novick (1988) developed a computational model that utilizes meta- locutionary acts, such as repeat and give-turn, to cap- …

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