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    <abstract>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. This paper has been accepted in NAACL 2019.</abstract>
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%0 Conference Proceedings
%T Evaluating Coherence in Dialogue Systems using Entailment
%A Dziri, Nouha
%A Kamalloo, Ehsan
%A Mathewson, Kory
%A Zaiane, Osmar
%Y Axelrod, Amittai
%Y Yang, Diyi
%Y Cunha, Rossana
%Y Shaikh, Samira
%Y Waseem, Zeerak
%S Proceedings of the 2019 Workshop on Widening NLP
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F dziri-etal-2019-evaluating-coherence
%X 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. This paper has been accepted in NAACL 2019.
%U https://aclanthology.org/W19-3646
%P 146-148

Markdown (Informal)

[Evaluating Coherence in Dialogue Systems using Entailment](https://aclanthology.org/W19-3646) (Dziri et al., WiNLP 2019)

• Evaluating Coherence in Dialogue Systems using Entailment (Dziri et al., WiNLP 2019)

ACL

• Nouha Dziri, Ehsan Kamalloo, Kory Mathewson, and Osmar Zaiane. 2019. Evaluating Coherence in Dialogue Systems using Entailment. In Proceedings of the 2019 Workshop on Widening NLP, pages 146–148, Florence, Italy. Association for Computational Linguistics.