A pressing challenge in current dialogue systems is to successfully converse
with users on topics with information distributed across different modalities.
Previous work in multiturn dialogue systems has primarily focused on either
text or table information. In more realistic scenarios, having a joint
understanding of both is critical as knowledge is typically distributed over
both unstructured and structured forms. We present a new dialogue dataset,
HybriDialogue, which consists of crowdsourced natural conversations grounded on
both Wikipedia text and tables. The conversations are created through the
decomposition of complex multihop questions into simple, realistic multiturn
dialogue interactions. We propose retrieval, system state tracking, and
dialogue response generation tasks for our dataset and conduct baseline
experiments for each. Our results show that there is still ample opportunity
for improvement, demonstrating the importance of building stronger dialogue
systems that can reason over the complex setting of information-seeking
dialogue grounded on tables and text.

本篇论文提出一个基于维基百科文本和表格的 HybriDialogue 数据集，通过将复杂的多跳问题分解为简单的、现实的多轮对话，创建了众包自然语言对话。针对该数据集，我们提出了检索、系统状态跟踪和对话响应生成任务，并进行了基线实验。实验结果表明，还有很大的改进空间，展示了构建可以在表格和文本上进行基于信息检索的复杂对话的更强大的对话系统的重要性。