TripleNet: Triple Attention Network for Multi-Turn Response Selection in Retrieval-Based Chatbots

Wentao Ma, Yiming Cui, Nan Shao, Su He, Wei-Nan Zhang, Ting Liu, Shijin Wang, Guoping Hu


Abstract
We consider the importance of different utterances in the context for selecting the response usually depends on the current query. In this paper, we propose the model TripleNet to fully model the task with the triple <context, query, response> instead of <context, response > in previous works. The heart of TripleNet is a novel attention mechanism named triple attention to model the relationships within the triple at four levels. The new mechanism updates the representation of each element based on the attention with the other two concurrently and symmetrically.We match the triple <C, Q, R> centered on the response from char to context level for prediction.Experimental results on two large-scale multi-turn response selection datasets show that the proposed model can significantly outperform the state-of-the-art methods.
Anthology ID:
K19-1069
Volume:
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venue:
CoNLL
SIG:
SIGNLL
Publisher:
Association for Computational Linguistics
Note:
Pages:
737–746
Language:
URL:
https://www.aclweb.org/anthology/K19-1069
DOI:
10.18653/v1/K19-1069
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PDF:
http://aclanthology.lst.uni-saarland.de/K19-1069.pdf