| Paper ID | HLT-4.2 |
| Paper Title |
IMPROVING DIALOGUE RESPONSE GENERATION VIA KNOWLEDGE GRAPH FILTER |
| Authors |
Yanmeng Wang, Ye Wang, Xingyu Lou, Ping An Technology, China; Wenge Rong, Beihang University, China; Zhenghong Hao, Shaojun Wang, Ping An Technology, China |
| Session | HLT-4: Dialogue Systems 2: Response Generation |
| Location | Gather.Town |
| Session Time: | Tuesday, 08 June, 14:00 - 14:45 |
| Presentation Time: | Tuesday, 08 June, 14:00 - 14:45 |
| Presentation |
Poster
|
| Topic |
Human Language Technology: [HLT-DIAL] Discourse and Dialog |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Current generative dialogue systems tend to produce generic dialog responses, which lack useful information and semantic coherence. An promising method to alleviate this problem is to integrate knowledge triples from knowledge base. However, current approaches mainly augment Seq2Seq framework with knowledge-aware mechanism to retrieve a large number of knowledge triples without considering specific dialogue context, which probably results in knowledge redundancy and incomplete knowledge comprehension. In this paper, we propose to leverage the contextual word representation of dialog post to filter out irrelevant knowledge with an attention-based triple filter network. We introduce a novel knowledge-enriched framework to integrate the filtered knowledge into the dialogue representation. Entity copy is further proposed to facilitate the integration of the knowledge during generation. Experiments on dialogue generation tasks have shown the proposed framework's promising potential. |
