  Existing task-oriented conversational search systems heavily rely on domain
ontologies with pre-defined slots and candidate value sets. In practical
applications, these prerequisites are hard to meet, due to the emerging new
user requirements and ever-changing scenarios. To mitigate these issues for
better interaction performance, there are efforts working towards detecting
out-of-vocabulary values or discovering new slots under unsupervised or
semi-supervised learning paradigm. However, overemphasizing on the conversation
data patterns alone induces these methods to yield noisy and arbitrary slot
results. To facilitate the pragmatic utility, real-world systems tend to
provide a stringent amount of human labelling quota, which offers an
authoritative way to obtain accurate and meaningful slot assignments.
Nonetheless, it also brings forward the high requirement of utilizing such
quota efficiently. Hence, we formulate a general new slot discovery task in an
information extraction fashion and incorporate it into an active learning
framework to realize human-in-the-loop learning. Specifically, we leverage
existing language tools to extract value candidates where the corresponding
labels are further leveraged as weak supervision signals. Based on these, we
propose a bi-criteria selection scheme which incorporates two major strategies,
namely, uncertainty-based sampling and diversity-based sampling to efficiently
identify terms of interest. We conduct extensive experiments on several public
datasets and compare with a bunch of competitive baselines to demonstrate the
effectiveness of our method. We have made the code and data used in this paper
publicly available.
