23/08/2020

Context-to-session matching: Utilizing whole session for response selection in information-seeking dialogue systems

Zhenxin Fu, Shaobo Cui, Mingyue Shang, Feng Ji, Dongyan Zhao, Haiqing Chen, Rui Yan

Keywords: text matching, graph attention network, response selection

Abstract Paper Similar Papers

Abstract: We study the retrieval-based multi-turn information-seeking dialogue systems, which are widely used in many scenarios. Most of the previous works select the response according to the matching degree between the query’s context and the candidate responses. Though great progress has been made, existing works ignore the contexts of the responses, which could provide rich information for selecting the most appropriate response. The more similar the query’s context and certain response’s context are, the more likely they are to indicate the same question, and thus, the more likely this response is to answer the query. In this paper, we consider the response and its context as a whole session and explore the task of matching the query’s context with the sessions. More specifically, we propose to match between the query’s context and response’s context and integrate the context-to-context matching with context-to-response matching. Experiment results prove that our proposed context-to-session method outperforms the strong baselines significantly.

The video of this talk cannot be embedded. You can watch it here:
https://dl.acm.org/doi/10.1145/3394486.3403211#sec-supp
(Link will open in new window)
 0
 0
 0
 0
  Share    
This is an embedded video. Talk and the respective paper are published at KDD 2020 virtual conference. If you are one of the authors of the paper and want to manage your upload, see the question "My papertalk has been externally embedded..." in the FAQ section.

Comments

Post Comment
no comments yet
code of conduct: tbd Characters remaining: 140

Similar Papers

 0
 0
 0
 0
 9:37 
 0
 0
 0
 0
 4:56 
 0
 0
 0
 0
 14:14 
 0
 0
 0
 0
 9:21 
 0
 0
 0
 0
 11:50 
 0
 0
 0
 0
 13:14 
 0
 0
 0
 1
 11:39 
 0
 0
 0
 0
 11:43 
 0
 0
 0
 0
 9:51 
 0
 0
 0
 0
 11:20 
 0
 0
 0
 0
 14:09 
 0
 0
 0
 0
 14:42 
 0
 0
 0
 0
 6:46 
 0
 0
 0
 0
 14:08 
 0
 0
 0
 0
 10:03 
 0
 0
 0
 0
 14:26 
 0
 0
 0
 0
 11:37 
 0
 0
 0
 0
 14:06 
 0
 0
 0
 0
 3:10 
 0
 0
 0
 0
 7:16 
 0
 0
 0
 0
 13:22 
 2
 1
 0
 0
 13:27 
 0
 0
 0
 1
 11:14 
 0
 0
 0
 1
 16:40 
 0
 0
 0
 0
 5:08 
 0
 0
 0
 0
 11:40 
 0
 0
 0
 0
 5:04 
 0
 0
 0
 0
 14:46 
 0
 0
 0
 0
 11:19 
 0
 0
 0
 0
 10:56 
 0
 0
 0
 0
 9:47 
 0
 0
 0
 0
 16:34 
 0
 0
 0
 0
 6:13 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 10:13 
 0
 0
 0
 0
 8:40 
 0
 0
 0
 0
 3:17 
 0
 0
 0
 0
 16:41 
 0
 0
 0
 0
 11:09 
 0
 0
 0
 0
 14:13