Task-solving in dialogue depends on the convergence of the situation models held by the dialogue partners. The Interactive Alignment Model (Pickering & Garrod, 2004) suggests that this convergence is the result of an interactive alignment process, which is based on mechanistic repetition at a number of linguistic levels. In this paper, we develop two predictions arising from the theory, along with two methods to quantify the known structural priming effects in the full inventory of syntactic choices found in text and speech corpora. (a) Under a rational perspective, we expect increased repetition in task-oriented dialogue compared to spontaneous conversation. We find within- and between-speaker priming in a corpus of spontaneous conversations, but stronger priming in task-oriented dialogue. (b) The Interactive Alignment Model predicts linguistic adaptation to be correlated with task success. We show this effect in a corpus of task-oriented dialogue, where we find a positive correlation of long-term adaptation and a quantifiable task success measure. We argue that the repetition tendency relevant for the high-level alignment of situation models is based on slow adaptation rather than short-term priming. We demonstrate that lexical and syntactic repetition are reliable and computationally exploitable predictors of task success.
All Science Journal Classification (ASJC) codes
- Neuropsychology and Physiological Psychology
- Language and Linguistics
- Experimental and Cognitive Psychology
- Linguistics and Language
- Artificial Intelligence