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To facilitate conversational question answering (CQA) over hybrid contexts infinance, we present a new dataset, named PACIFIC. Compared with existing CQAdatasets, PACIFIC exhibits three key features: (i) proactivity, (ii) numericalreasoning, and (iii) hybrid context of tables and text. A new task is definedaccordingly to study Proactive Conversational Question Answering (PCQA), whichcombines clarification question generation and CQA. In addition, we propose anovel method, namely UniPCQA, to adapt a hybrid format of input and outputcontent in PCQA into the Seq2Seq problem, including the reformulation of thenumerical reasoning process as code generation. UniPCQA performs multi-tasklearning over all sub-tasks in PCQA and incorporates a simple ensemble strategyto alleviate the error propagation issue in the multi-task learning bycross-validating top-$k$ sampled Seq2Seq outputs. We benchmark the PACIFICdataset with extensive baselines and provide comprehensive evaluations on eachsub-task of PCQA.

PACIFIC: Towards Proactive Conversational Question Answering over Tabular and Textual Data in Finance