|Title:||The Theory and Use of Clarification Requests in Dialogue||Add Dissertation|
|Author:||Matthew Purver||Update Dissertation|
|Email:||click here to access email|
|Institution:||King's College London, Department of Computer Science|
|Linguistic Subfield(s):||Computational Linguistics; Pragmatics; Semantics;|
|Abstract:||Clarification requests are an important, relatively common and yet under-studied dialogue device allowing a user to ask about some feature (e.g. the meaning or form) of an utterance, or part thereof. They can take many different forms (often highly elliptical) and can have many different meanings (requesting various types of information). This thesis combines empirical, theoretical and implementational work to provide a study of the various types of clarification request that exist, give a theoretical analysis thereof, and show how the results can be applied to add useful capabilities to a prototype computational dialogue system.
A series of empirical studies (corpus-based and experimental) are described which establish a taxonomy of the possible types of clarification request together with information about their meaning and usage, about the phrase types and conditions that trigger them and their particular forms and interpretations, and about the likely methods of responding to them.
A syntactic and semantic analysis using the HPSG framework is given which extends the work of (Ginzburg & Cooper, 2004) to cover the main classes of the above taxonomy, and to account for the clarificational potential of those word and phrase types which commonly cause clarification requests. This is shown to have interesting implications for the semantics of various lexical and phrasal types, in particular suggesting that noun phrases be given a simple witness-set based representation.
Finally, the theoretical analysis and empirical findings are applied within a HPSG grammar and a prototype text-based dialogue system, CLARIE. Implemented in Prolog using the TrindiKit, the system combines the information-state-based dialogue management of GoDiS (Larsson et al., 2000) and the HPSG-based ellipsis resolution of SHARDS (Ginzburg et al., 2001) and adds the capability to interpret and respond to user clarification requests, and generate its own clarifications where necessary to deal with incomprehensible or contradictory input, resolve unknown or ambiguous reference, and learn out-of-vocabulary words.