Publications by authors named "Arash Eshghi"

In spontaneous conversation, speakers seldom have a full plan of what they are going to say in advance: they need to conceptualise and plan as they articulate each word in turn. This often leads to long pauses mid-utterance. Listeners either wait out the pause, offer a possible completion, or respond with an incremental clarification request (iCR), intended to recover the rest of the truncated turn.

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Social robots have limited social competences. This leads us to view them as depictions of social agents rather than actual social agents. However, people also have limited social competences.

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People give feedback in conversation: both positive signals of understanding, such as nods, and negative signals of misunderstanding, such as frowns. How do signals of understanding and misunderstanding affect the coordination of language use in conversation? Using a chat tool and a maze-based reference task, we test two experimental manipulations that selectively interfere with feedback in live conversation: (a) "Attenuation" that replaces positive signals of understanding such as "right" or "okay" with weaker, more provisional signals such as "errr" or "umm" and (2) "Amplification" that replaces relatively specific signals of misunderstanding from clarification requests such as "on the left?" with generic signals of trouble such as "huh?" or "eh?". The results show that Amplification promotes rapid convergence on more systematic, abstract ways of describing maze locations while Attenuation has no significant effect.

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Anecdotal evidence suggests that participants in conversation can sometimes act as a coalition. This implies a level of conversational organization in which groups of individuals form a coherent unit. This paper investigates the implications of this phenomenon for psycholinguistic and semantic models of shared context in dialog.

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We present empirical evidence from dialogue that challenges some of the key assumptions in the Pickering & Garrod (P&G) model of speaker-hearer coordination in dialogue. The P&G model also invokes an unnecessarily complex set of mechanisms. We show that a computational implementation, currently in development and based on a simpler model, can account for more of this type of dialogue data.

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