  Current state-of-the-art neural dialogue models learn from human
conversations following the data-driven paradigm. As such, a reliable training
corpus is the crux of building a robust and well-behaved dialogue model.
However, due to the open-ended nature of human conversations, the quality of
user-generated training data varies greatly, and effective training samples are
typically insufficient while noisy samples frequently appear. This impedes the
learning of those data-driven neural dialogue models. Therefore, effective
dialogue learning requires not only more reliable learning samples, but also
fewer noisy samples. In this paper, we propose a data manipulation framework to
proactively reshape the data distribution towards reliable samples by
augmenting and highlighting effective learning samples as well as reducing the
effect of inefficient samples simultaneously. In particular, the data
manipulation model selectively augments the training samples and assigns an
importance weight to each instance to reform the training data. Note that, the
proposed data manipulation framework is fully data-driven and learnable. It not
only manipulates training samples to optimize the dialogue generation model,
but also learns to increase its manipulation skills through gradient descent
with validation samples. Extensive experiments show that our framework can
improve the dialogue generation performance with respect to various automatic
evaluation metrics and human judgments.
