Unsupervised Evaluation of Interactive Dialog with DialoGPT

Shikib Mehri; Maxine Eskenazi

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arXiv · 2006.12719

Unsupervised Evaluation of Interactive Dialog with DialoGPT

Shikib Mehri·Maxine Eskenazi

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Abstract

It is important to define meaningful and interpretable automatic evaluation metrics for open-domain dialog research. Standard language generation metrics have been shown to be ineffective for dialog. This paper introduces the FED metric (fine-grained evaluation of dialog), an automatic evaluation metric which uses DialoGPT, without any fine-tuning or supervision. It also introduces the FED dataset which is constructed by annotating a set of human-system and human-human conversations with eighteen fine-grained dialog qualities. The FED metric (1) does not rely on a ground-truth response, (2) does not require training data and (3) measures fine-grained dialog qualities at both the turn and whole dialog levels. FED attains moderate to strong correlation with human judgement at both levels.

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Shikib Mehri, Maxine Eskenazi. 2020-06-23. Unsupervised Evaluation of Interactive Dialog with DialoGPT. https://arxiv.org/abs/2006.12719

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