  Despite recent progress on conversational systems, they still do not perform
smoothly and coherently when faced with ambiguous requests. When questions are
unclear, conversational systems should have the ability to ask clarifying
questions, rather than assuming a particular interpretation or simply
responding that they do not understand. Previous studies have shown that users
are more satisfied when asked a clarifying question, rather than receiving an
unrelated response. While the research community has paid substantial attention
to the problem of predicting query ambiguity in traditional search contexts,
researchers have paid relatively little attention to predicting when this
ambiguity is sufficient to warrant clarification in the context of
conversational systems. In this paper, we propose an unsupervised method for
predicting the need for clarification. This method is based on the measured
coherency of results from an initial answer retrieval step, under the
assumption that a less ambiguous query is more likely to retrieve more coherent
results when compared to an ambiguous query. We build a graph from retrieved
items based on their context similarity, treating measures of graph
connectivity as indicators of ambiguity. We evaluate our approach on two
recently released open-domain conversational question answering datasets,
ClariQ and AmbigNQ, comparing it with neural and non-neural baselines. Our
unsupervised approach performs as well as supervised approaches while providing
better generalization.
