An Auto-Encoder Matching Model for Learning Utterance-Level Semantic Dependency in Dialogue Generation

Liangchen Luo, Jingjing Xu, Junyang Lin, Qi Zeng, Xu Sun


Abstract
Generating semantically coherent responses is still a major challenge in dialogue generation. Different from conventional text generation tasks, the mapping between inputs and responses in conversations is more complicated, which highly demands the understanding of utterance-level semantic dependency, a relation between the whole meanings of inputs and outputs. To address this problem, we propose an Auto-Encoder Matching (AEM) model to learn such dependency. The model contains two auto-encoders and one mapping module. The auto-encoders learn the semantic representations of inputs and responses, and the mapping module learns to connect the utterance-level representations. Experimental results from automatic and human evaluations demonstrate that our model is capable of generating responses of high coherence and fluency compared to baseline models.
Anthology ID:
D18-1075
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
702–707
Language:
URL:
https://aclanthology.org/D18-1075
DOI:
10.18653/v1/D18-1075
Bibkey:
Cite (ACL):
Liangchen Luo, Jingjing Xu, Junyang Lin, Qi Zeng, and Xu Sun. 2018. An Auto-Encoder Matching Model for Learning Utterance-Level Semantic Dependency in Dialogue Generation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 702–707, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
An Auto-Encoder Matching Model for Learning Utterance-Level Semantic Dependency in Dialogue Generation (Luo et al., EMNLP 2018)
Copy Citation:
PDF:
https://preview.aclanthology.org/update-css-js/D18-1075.pdf
Code
 lancopku/AMM
Data
DailyDialog