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Title: Uncommon ground : the distribution of dialogue contexts
Author: Eshghi, Arash
ISNI:       0000 0004 2676 4842
Awarding Body: Queen Mary, University of London
Current Institution: Queen Mary, University of London
Date of Award: 2009
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Context in dialogue is at once regarded as a set of resources enabling successful interpretation and is altered by such interpretations. A key problem for models of dialogue, then, is to specify how the shared context evolves. However, these models have been developed mainly to account for the way context is built up through direct interaction between pairs of participants. In multi-party dialogue, patterns of direct interaction between participants are often more unevenly distributed. This thesis explores the effects of this characteristic on the development of shared contexts. A corpus analysis of ellipsis shows that side-participants can reach the same level of grounding as speaker and addressee. Such dialogues result in collective contexts that are not reducible to their component dyadic interactions. It is proposed that this is characteristic of dialogues in which a subgroup of the participants are organised into a party, who act as a unified aggregate to carry the conversation forward. Accordingly, the contextual increments arising from a dialogue move from one party member can affect the party as a whole. Grounding, like turn-taking, can therefore operate between parties rather than individuals. An experimental test of this idea is presented which provides evidence for the practical reality of parties. Two further experiments explore the impact of party membership on the accessibility of context. The results indicate that participants who, for a stretch of talk, fall inside and outside of the interacting parties, effect divergent contextual increments. This is evidence for the emergence of distinct dialogue contexts in the same conversation. Finally, it is argued that these findings present significant challenges for how formal models of dialogue deal with individual contributions. In particular, they point to the need for such models to index the resulting contextual increments to specific subsets of the participants
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Computer Science