Kai Nakamura, Sharon Levy, Yi-Lin Tuan, Wenhu Chen, and William Yang Wang
Our lab: http://nlp.cs.ucsb.edu/index.html
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 conduct several baseline experiments, including retrieval, system state tracking, and dialogue response generation. 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.
Dataset Download: https://drive.google.com/file/d/12GvlGEFvLumJ10TKacXxWK_5XxgYEaH6/view?usp=sharing
See data_api.py