Towards Complex Scenarios: Building End-to-End Task-Oriented Dialogue System across Multiple Knowledge Bases
Keywords:SNLP: Conversational AI/Dialogue Systems
AbstractWith the success of the sequence-to-sequence model, end-to-end task-oriented dialogue systems (EToDs) have obtained remarkable progress. However, most existing EToDs are limited to single KB settings where dialogues can be supported by a single KB, which is still far from satisfying the requirements of some complex applications (multi-KBs setting). In this work, we first empirically show that the existing single-KB EToDs fail to work on multi-KB settings that require models to reason across various KBs. To solve this issue, we take the first step to consider the multi-KBs scenario in EToDs and introduce a KB-over-KB Heterogeneous Graph Attention Network (KoK-HAN) to facilitate model to reason over multiple KBs. The core module is a triple-connection graph interaction layer that can model different granularity levels of interaction information across different KBs (i.e., intra-KB connection, inter-KB connection and dialogue-KB connection). Experimental results confirm the superiority of our model for multiple KBs reasoning.
How to Cite
Qin, L., Li, Z., Yu, Q., Wang, L., & Che, W. (2023). Towards Complex Scenarios: Building End-to-End Task-Oriented Dialogue System across Multiple Knowledge Bases. Proceedings of the AAAI Conference on Artificial Intelligence, 37(11), 13483-13491. https://doi.org/10.1609/aaai.v37i11.26581
AAAI Technical Track on Speech & Natural Language Processing