Alignment and task success in spoken dialogue

By David Reitter, Johanna D. Moore. Journal of Memory and Language, 2014

165 citations recorded by the source corpus.

Abstract 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.

Subfields: Artificial Intelligence, Language and Linguistics

View Paper: https://www.semanticscholar.org/paper/a4a304f59e7cd6b0cbd39dba5b40805851dfc70b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Michael. White
2. William Schuler
3. Rajakrishnan Rajkumar
4. Marten van Schijndel

In Language and Linguistics

1. Michael. White
2. William Schuler
3. Rajakrishnan Rajkumar
4. Marten van Schijndel

Citations in the corpus

• Investigating locality effects and surprisal in written English syntactic choice phenomena. (2016, methodology): “To calculate these complexity measures, an incremental probabilistic leftcorner parser (van Schijndel, Exley, & Schuler 2013) based on the Petrov et al. (2006) latent-variable PCFG computes the n-best parses at each word, where n is the desired beam width.”