  Conversational systems have made significant progress in generating natural
language responses. However, their potential as conversational search systems
is currently limited due to their passive role in the information-seeking
process. One major limitation is the scarcity of datasets that provide labelled
ambiguous questions along with a supporting corpus of documents and relevant
clarifying questions. This work aims to tackle the challenge of generating
relevant clarifying questions by taking into account the inherent ambiguities
present in both user queries and documents. To achieve this, we propose PAQA,
an extension to the existing AmbiNQ dataset, incorporating clarifying
questions. We then evaluate various models and assess how passage retrieval
impacts ambiguity detection and the generation of clarifying questions. By
addressing this gap in conversational search systems, we aim to provide
additional supervision to enhance their active participation in the
information-seeking process and provide users with more accurate results.
