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Current state-of-the-art neural dialogue models learn from humanconversations following the data-driven paradigm. As such, a reliable trainingcorpus is the crux of building a robust and well-behaved dialogue model.However, due to the open-ended nature of human conversations, the quality ofuser-generated training data varies greatly, and effective training samples aretypically insufficient while noisy samples frequently appear. This impedes thelearning of those data-driven neural dialogue models. Therefore, effectivedialogue learning requires not only more reliable learning samples, but alsofewer noisy samples. In this paper, we propose a data manipulation framework toproactively reshape the data distribution towards reliable samples byaugmenting and highlighting effective learning samples as well as reducing theeffect of inefficient samples simultaneously. In particular, the datamanipulation model selectively augments the training samples and assigns animportance weight to each instance to reform the training data. Note that, theproposed data manipulation framework is fully data-driven and learnable. It notonly manipulates training samples to optimize the dialogue generation model,but also learns to increase its manipulation skills through gradient descentwith validation samples. Extensive experiments show that our framework canimprove the dialogue generation performance with respect to various automaticevaluation metrics and human judgments.

Data Manipulation: Towards Effective Instance Learning for Neural Dialogue Generation via Learning to Augment and Reweight