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Real human conversation data are complicated, heterogeneous, and noisy, fromwhich building open-domain dialogue systems remains a challenging task. Infact, such dialogue data still contains a wealth of information and knowledge,however, they are not fully explored. In this paper, we show existingopen-domain dialogue generation methods that memorize context-response paireddata with autoregressive or encode-decode language models underutilize thetraining data. Different from current approaches, using external knowledge, weexplore a retrieval-generation training framework that can take advantage ofthe heterogeneous and noisy training data by considering them as "evidence". Inparticular, we use BERTScore for retrieval, which gives better qualities of theevidence and generation. Experiments over publicly available datasetsdemonstrate that our method can help models generate better responses, evensuch training data are usually impressed as low-quality data. Such performancegain is comparable with those improved by enlarging the training set, evenbetter. We also found that the model performance has a positive correlationwith the relevance of the retrieved evidence. Moreover, our method performedwell on zero-shot experiments, which indicates that our method can be morerobust to real-world data.

Pan More Gold from the Sand: Refining Open-domain Dialogue Training with Noisy Self-Retrieval Generation