Implications for Generating Clarification Requests in Task-Oriented Dialogues

Proceedings of the 43rd Annual Meeting of the ACL, pages 239–246, Ann Arbor, June 2005. c⃝2005 Association for Computational Linguistics Implications for Generating Clarification Requests in Task-oriented Dialogues Verena Rieser Department of Computational Linguistics Saarland University Saarbr¨ucken, D-66041 vrieser@coli.uni-sb.de Johanna D. Moore School of Informatics University of Edinburgh Edinburgh, EH8 9LW, GB J.Moore@ed.ac.uk Abstract Clarification requests (CRs) in conversa- tion ensure and maintain mutual under- standing and thus play a crucial role in robust dialogue interaction. In this pa- per, we describe a corpus study of CRs in task-oriented dialogue and compare our findings to those reported in two prior studies. We find that CR behavior in task-oriented dialogue differs significantly from that in everyday conversation in a number of ways. Moreover, the dialogue type, the modality and the channel qual- ity all influence the decision of when to clarify and at which level of the ground- ing process. Finally we identify form- function correlations which can inform the generation of CRs. 1 Introduction Clarification requests in conversation ensure and maintain mutual understanding and thus play a sig- nificant role in robust and efficient dialogue interac- tion. From a theoretical perspective, the model of grounding explains how mutual understanding is es- tablished. According to Clark (1996), speakers and listeners ground mutual understanding on four lev- els of coordination in an action ladder, as shown in Table 1. Several current research dialogue systems can de- tect errors on different levels of grounding (Paek and Horvitz, 2000; Larsson, 2002; Purver, 2004; Level Speaker S Listener L Convers. S is proposing activity α L is considering pro- posal α Intention S is signalling that p L is recognizing that p Signal S is presenting signal σ L is identifying signal σ Channel S is executing behavior β L is attending to behav- ior β Table 1: Four levels of grounding Schlangen, 2004). However, only the work of Purver (2004) addresses the question of how the source of the error affects the form the CR takes. In this paper, we investigate the use of form- function mappings derived from human-human di- alogues to inform the generation of CRs. We iden- tify the factors that determine which function a CR should take and identify function-form correlations that can be used to guide the automatic generation of CRs. In Section 2, we discuss the classification schemes used in two recent corpus studies of CRs in human-human dialogue, and assess their applica- bility to the problem of generating CRs. Section 3 describes the results we obtained by applying the classification scheme of Rodriguez and Schlangen (2004) to the Communicator Corpus (Bennett and Rudnicky, 2002). Section 4 draws general conclu- sions for generating CRs by comparing our results to those of (Purver et al., 2003) and (Rodriguez and Schlangen, 2004). Section 5 describes the correla- tions between function and form features that are present in the corpus and their implications for gen- erating CRs. 239 Attr. Value Category Example form non Non-Reprise “What did you say?” wot Conventional …

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