  Retrieval-based chatbot selects the appropriate response from candidates
according to the context, which heavily depends on a response selection module.
A response selection module is generally a scoring model to evaluate candidates
and is usually trained on the annotated positive response and sampled negative
responses. Sampling negative responses lead to two risks: a). The sampled
negative instances, especially that from random sampling methods, are mostly
irrelevant to the dialogue context and too easy to be fitted at the training
stage while causing a weak model in the real scenario. b). The so-called
negative instances may be positive, which is known as the fake negative
problem. To address the above issue, we employ pre-trained language models,
such as the DialoGPT to construct more challenging negative instances to
enhance the model robustness. Specifically, we provide garbled context to the
pre-trained model to generate responses and filter the fake negative ones. In
this way, our negative instances are fluent, context-related, and more
challenging for the model to learn, while can not be positive. Extensive
experiments show that our method brings significant and stable improvements on
the dialogue response selection capacity.
