Zhengliang Shi
2023
RADE: Reference-Assisted Dialogue Evaluation for Open-Domain Dialogue
Zhengliang Shi
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Weiwei Sun
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Shuo Zhang
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Zhen Zhang
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Pengjie Ren
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Zhaochun Ren
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Evaluating open-domain dialogue systems is challenging for reasons such as the one-to-many problem, i.e., many appropriate responses other than just the golden response. As of now, automatic evaluation methods need better consistency with humans, while reliable human evaluation can be time- and cost-intensive. To this end, we propose the Reference-Assisted Dialogue Evaluation (RADE) approach under the multi-task learning framework, which leverages the pre-created utterance as reference other than the gold response to relief the one-to-many problem. Specifically, RADE explicitly compares reference and the candidate response to predict their overall scores.Moreover, an auxiliary response generation task enhances prediction via a shared encoder.To support RADE, we extend three datasets with additional rated responses other than just a golden response by human annotation.Experiments on our three datasets and two existing benchmarks demonstrate the effectiveness of our method, where Pearson, Spearman, and Kendall correlations with human evaluation outperform state-of-the-art baselines.