Year: 2020
Type: conference-paper
Abstract: In this work, we investigate the human perception of coherence in open-domain dialogues.In particular, we address the problem of annotating and modeling the coherence of nextturn candidates while considering the entire history of the dialogue.First, we create the Switchboard Coherence (SWBD-Coh) corpus, a dataset of human-human spoken dialogues annotated with turn coherence ratings, where next-turn candidate utterances ratings are provided considering the full dialogue context.Our statistical analysis of the corpus indicates how turn coherence perception is affected by patterns of distribution of entities previously introduced and the Dialogue Acts used.Second, we experiment with different architectures to model entities, Dialogue Acts and their combination and evaluate their performance in predicting human coherence ratings on SWBD-Coh.We find that models combining both DA and entity information yield the best performances both for response selection and turn coherence rating.
Authors: Alessandra Cervone, Giuseppe Riccardi
Institution: University of Trento
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W3034410548