  To facilitate conversational question answering (CQA) over hybrid contexts in
finance, we present a new dataset, named PACIFIC. Compared with existing CQA
datasets, PACIFIC exhibits three key features: (i) proactivity, (ii) numerical
reasoning, and (iii) hybrid context of tables and text. A new task is defined
accordingly to study Proactive Conversational Question Answering (PCQA), which
combines clarification question generation and CQA. In addition, we propose a
novel method, namely UniPCQA, to adapt a hybrid format of input and output
content in PCQA into the Seq2Seq problem, including the reformulation of the
numerical reasoning process as code generation. UniPCQA performs multi-task
learning over all sub-tasks in PCQA and incorporates a simple ensemble strategy
to alleviate the error propagation issue in the multi-task learning by
cross-validating top-$k$ sampled Seq2Seq outputs. We benchmark the PACIFIC
dataset with extensive baselines and provide comprehensive evaluations on each
sub-task of PCQA.
