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Task-oriented dialogue systems typically rely on large amounts ofhigh-quality training data or require complex handcrafted rules. However,existing datasets are often limited in size considering the complexity of thedialogues. Additionally, conventional training signal inference is not suitablefor non-deterministic agent behaviour, i.e. considering multiple actions asvalid in identical dialogue states. We propose the Conversation Graph(ConvGraph), a graph-based representation of dialogues that can be exploitedfor data augmentation, multi-reference training and evaluation ofnon-deterministic agents. ConvGraph generates novel dialogue paths to augmentdata volume and diversity. Intrinsic and extrinsic evaluation across threedatasets shows that data augmentation and/or multi-reference training withConvGraph can improve dialogue success rates by up to 6.4%.

Conversation Graph: Data Augmentation, Training and Evaluation for Non-Deterministic Dialogue Management