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：众包对话语料库通常因数据整理成本高昂而规模有限、主题覆盖不足，这限制了对话模型向开放域主题的泛化能力。在本工作中，我们针对情感支持对话任务，利用大语言模型进行对话增强。通过将对话增强视为对话补全任务，我们引导经微调的语言模型基于不同主题的可用对话帖补全完整对话，再根据启发式规则进行后处理。运用该方法，我们构建了面向ESC任务的增强数据集AugESC，大幅扩展了众包ESConv语料库的规模与主题覆盖面。通过全面的人工评估，我们证明该方法优于对话增强的强基线方法，且AugESC具有与原始众包语料库相当的对话质量。此外，通过人机交互评估，我们证实基于AugESC的后训练可提升下游对话模型向开放域主题的泛化能力。这些结果验证了AugESC的实用性，并凸显大语言模型在改进数据稀缺型对话生成任务中的潜力。