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.

翻译：为促进金融领域混合情境下的对话问答（CQA），我们提出新数据集PACIFIC。与现有CQA数据集相比，PACIFIC具有三个核心特征：（i）主动性，（ii）数值推理，以及（iii）表格与文本的混合情境。据此定义新任务——主动式对话问答（PCQA），该任务融合澄清问题生成与CQA技术。我们进一步提出创新方法UniPCQA，将PCQA中混合格式的输入输出内容适配为Seq2Seq问题，包括将数值推理过程重构为代码生成。UniPCQA对PCQA所有子任务执行多任务学习，并通过交叉验证top-k采样的Seq2Seq输出来设计简单集成策略，以缓解多任务学习中的误差传播问题。我们基于PACIFIC数据集建立了广泛的基线基准，并对PCQA各子任务进行了全面评估。