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

AugESC: Dialogue Augmentation with Large Language Models for Emotional Support Conversation