Evaluating Coherence in Dialogue Systems using Entailment
- Nouha Dziri
- Ehsan Kamalloo
- Kory Wallace Mathewson
- Osmar R. Zaiane, University of Alberta (Database)
Evaluating open-domain dialogue systems is difficult due to the diversity of possible correct answers. Automatic metrics such as BLEU correlate weakly with human annotations, resulting in a significant bias across different models and datasets. Some researchers resort to human judgment experimentation for assessing response quality, which is expensive, time consuming, and not scalable. Moreover, judges tend to evaluate a small number of dialogues, meaning that minor differences in evaluation configuration may lead to dissimilar results. In this paper, we present interpretable metrics for evaluating topic coherence by making use of distributed sentence representations. Furthermore, we introduce calculable approximations of human judgment based on conversational coherence by adopting state-of-the-art entailment techniques. Results show that our metrics can be used as a surrogate for human judgment, making it easy to evaluate dialogue systems on large-scale datasets and allowing an unbiased estimate for the quality of the responses.
Citation
N. Dziri, E. Kamalloo, K. Mathewson, O. Zaiane. "Evaluating Coherence in Dialogue Systems using Entailment". NAACL Annual Conference of the North American Chapter of the Association for Computational Linguisti, Minneapolis, USA, pp 3806–3812, June 2019.| Keywords: | |
| Category: | In Conference |
| Web Links: | doi |
| ACL |
BibTeX
@incollection{Dziri+al:NAACL19,
author = {Nouha Dziri and Ehsan Kamalloo and Kory Wallace Mathewson and Osmar
R. Zaiane},
title = {Evaluating Coherence in Dialogue Systems using Entailment},
Pages = {3806–3812},
booktitle = {NAACL Annual Conference of the North American Chapter of the
Association for Computational Linguisti},
year = 2019,
}Last Updated: September 15, 2020Submitted by Sabina P
