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In open-domain dialogue generation tasks, contexts and responses in mostdatasets are one-to-one mapped, violating an important many-to-manycharacteristic: a context leads to various responses, and a response answersmultiple contexts. Without such patterns, models poorly generalize and preferresponding safely. Many attempts have been made in either multi-turn settingsfrom a one-to-many perspective or in a many-to-many perspective but limited tosingle-turn settings. The major challenge to many-to-many augment multi-turndialogues is that discretely replacing each turn with semantic similaritybreaks fragile context coherence. In this paper, we propose DialoGue PathSampling (DialoGPS) method in continuous semantic space, the first many-to-manyaugmentation method for multi-turn dialogues. Specifically, we map a dialogueto our extended Brownian Bridge, a special Gaussian process. We sample latentvariables to form coherent dialogue paths in the continuous space. A dialoguepath corresponds to a new multi-turn dialogue and is used as augmented trainingdata. We show the effect of DialoGPS with both automatic and human evaluation.

DialoGPS: Dialogue Path Sampling in Continuous Semantic Space for Data Augmentation in Multi-Turn Conversations