Work

A Generative Joint, Additive, Sequential Model of Topics and Speech Acts in Patient-Doctor Communication

Year: 2013

Type: conference-paper

Abstract: We develop a novel generative model of conversation that jointly captures both the topical content and the speech act type associated with each utterance.Our model expresses both token emission and state transition probabilities as log-linear functions of separate components corresponding to topics and speech acts (and their interactions).We apply this model to a dataset comprising annotated patient-physician visits and show that the proposed joint approach outperforms a baseline univariate model.

Source: Conference on Empirical Methods in Natural Language Processing (EMNLP)

DOI: 10.18653/v1/d13-1182

Authors: Byron C. Wallace, Thomas A. Trikalinos, M. Barton Laws, Ira B. Wilson, Eugene Charniak

Institution: Brown University

Language: English

Topic: Speech and dialogue systems

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: National Institute of Mental Health, National Institute on Drug Abuse

Award: G1440787744