Year: 2018
Type: conference-paper
Abstract: To date there has been very little work on assessing discourse coherence methods on real-world data.To address this, we present a new corpus of real-world texts (GCDC) as well as the first large-scale evaluation of leading discourse coherence algorithms.We show that neural models, including two that we introduce here (SENTAVG and PARSEQ), tend to perform best.We analyze these performance differences and discuss patterns we observed in low coherence texts in four domains.
DOI: 10.18653/v1/w18-5023
Authors: Alice Lai, Joel Tetreault
Institution: University of Illinois Urbana-Champaign
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2962790423