SelF-Eval: Self-supervised Fine-grained Dialogue Evaluation

Longxuan Ma; Ziyu Zhuang; Weinan Zhang; Mingda Li; Ting Liu

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arXiv · 2208.08094

SelF-Eval: Self-supervised Fine-grained Dialogue Evaluation

Longxuan Ma·Ziyu Zhuang·Weinan Zhang·Mingda Li·Ting Liu

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Abstract

This paper introduces a novel Self-supervised Fine-grained Dialogue Evaluation framework (SelF-Eval). The core idea is to model the correlation between turn quality and the entire dialogue quality. We first propose a novel automatic data construction method that can automatically assign fine-grained scores for arbitrarily dialogue data. Then we train \textbf{SelF-Eval} with a multi-level contrastive learning schema which helps to distinguish different score levels. Experimental results on multiple benchmarks show that SelF-Eval is highly consistent with human evaluations and better than the state-of-the-art models. We give a detailed analysis of the experiments in this paper. Our code is available on GitHub.

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BibTeX RIS

Longxuan Ma, Ziyu Zhuang, Weinan Zhang, Mingda Li, Ting Liu. 2022-08-17. SelF-Eval: Self-supervised Fine-grained Dialogue Evaluation. https://arxiv.org/abs/2208.08094

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