Modeling the clarification potential of instructions: Predicting clarification requests and other reactions. (September 2017)

• Record Type: Journal Article
• Authors: Benotti, Luciana; Blackburn, Patrick
• Is Part Of: Computer speech & language. Volume 45(2017)
• Page Start: 536
• Page End: 551
• Publication Date: 2017-09
• Subjects: Clarification requests -- Level-sensitive Gabsdil test -- Conversational implicatures -- Dialogue systems -- Classical planning -- Micro-planning -- Negotiability -- Tacit acts -- Explicatures -- Emergent structure -- Opportunistic theories of communication; Speech processing systems -- Periodicals; Automatic speech recognition -- Periodicals; Computers -- Periodicals; Linguistics -- Periodicals; Speech-Language Pathology -- Periodicals; Traitement automatique de la parole -- Périodiques; Reconnaissance automatique de la parole -- Périodiques; Automatic speech recognition; Speech processing systems; Electronic journals; Periodicals; 006.454
• DOI: 10.1016/j.csl.2017.01.008
• Languages: English
• ISSNs: 0885-2308

Abstract

Highlights: We hypothesize that implicatures are a rich source of clarification requests. We motivate the hypothesis in theoretical, practical and empirical terms. We model clarification potential by inferring conversational implicatures. Much of the inference can be handled using classical AI planning. Discourse structure emerges as task structure is exploited opportunistically. Abstract: We hypothesize that conversational implicatures are a rich source of clarification requests, and in this paper we do two things. First, we motivate the hypothesis in theoretical, practical and empirical terms and formulate it as a concrete clarification potential principle: implicatures may become explicit as fourth-level clarification requests . Second, we present a framework for generating the clarification potential of an instruction by inferring its conversational implicatures with respect to a particular context. We evaluate the framework and illustrate its performance using a human–human corpus of situated conversations. Much of the inference required can be handled using classical planning, though as we shall note, other forms of means-ends analysis are also required. Our framework leads us to view discourse structure as emerging via opportunistic responses to task structure.