Healing Unsafe Dialogue Responses with Weak Supervision Signals
Zi Liang Pinghui Wang Ruofei Zhang Shuo Zhang Xiaofan Ye Yi Huang Junlan Feng
Recent years have seen increasing concerns about the unsafe response
generation of large-scale dialogue systems, where agents will learn offensive
or biased behaviors from the real-world corpus. Some methods are proposed to
address the above issue by detecting and replacing unsafe training examples in
a pipeline style. Though effective, they suffer from a high annotation cost and
adapt poorly to unseen scenarios as well as adversarial attacks. Besides, the
neglect of providing safe responses (e.g. simply replacing with templates) will
cause the information-missing problem of dialogues. To address these issues, we
propose an unsupervised pseudo-label sampling method, TEMP, that can
automatically assign potential safe responses. Specifically, our TEMP method
groups responses into several clusters and samples multiple labels with an
adaptively sharpened sampling strategy, inspired by the observation that unsafe
samples in the clusters are usually few and distribute in the tail. Extensive
experiments in chitchat and task-oriented dialogues show that our TEMP
outperforms state-of-the-art models with weak supervision signals and obtains
comparable results under unsupervised learning settings.