Crowdsourced dialogue corpora are usually limited in scale and topic coverage due to the expensive cost of data curation. This would hinder the generalization of downstream dialogue models to open-domain topics. In this work, we leverage large language models for dialogue augmentation in the task of emotional support conversation (ESC). By treating dialogue augmentation as a dialogue completion task, we prompt a fine-tuned language model to complete full dialogues from available dialogue posts of various topics, which are then postprocessed based on heuristics. Applying this approach, we construct AugESC, an augmented dataset for the ESC task, which largely extends the scale and topic coverage of the crowdsourced ESConv corpus. Through comprehensive human evaluation, we demonstrate that our approach is superior to strong baselines of dialogue augmentation and that AugESC has comparable dialogue quality to the crowdsourced corpus. We also conduct human interactive evaluation and prove that post-training on AugESC improves downstream dialogue models' generalization ability to open-domain topics. These results suggest the utility of AugESC and highlight the potential of large language models in improving data-scarce dialogue generation tasks.

翻译：众包对话语料库因数据整理成本高昂，通常规模有限且话题覆盖不足，这会阻碍下游对话模型向开放域话题的泛化。本研究在大规模语言模型的情感支持对话任务中引入对话增强技术。通过将对话增强视为对话补全任务，我们引导微调语言模型基于不同话题的可用对话帖子补全完整对话，随后依据启发式规则进行后处理。采用该方法构建了面向情感支持对话任务的增强数据集AugESC，该数据集显著扩展了众包语料库ESConv的规模与话题覆盖范围。通过全面人工评估，证明本方法优于强对话增强基线模型，且AugESC具有与原始众包语料库相当的对话质量。交互式人工评估进一步证实，在AugESC上进行后训练可提升下游对话模型在开放域话题上的泛化能力。这些结果验证了AugESC的有效性，并凸显了大语言模型在缓解数据稀缺型对话生成任务中的潜力。