Semantically Conditioned Dialog Response Generation via Hierarchical Disentangled Self-Attention

Wenhu Chen, Jianshu Chen, Pengda Qin, Xifeng Yan, William Yang Wang


Abstract
Semantically controlled neural response generation on limited-domain has achieved great performance. However, moving towards multi-domain large-scale scenarios are shown to be difficult because the possible combinations of semantic inputs grow exponentially with the number of domains. To alleviate such scalability issue, we exploit the structure of dialog acts to build a multi-layer hierarchical graph, where each act is represented as a root-to-leaf route on the graph. Then, we incorporate such graph structure prior as an inductive bias to build a hierarchical disentangled self-attention network, where we disentangle attention heads to model designated nodes on the dialog act graph. By activating different (disentangled) heads at each layer, combinatorially many dialog act semantics can be modeled to control the neural response generation. On the large-scale Multi-Domain-WOZ dataset, our model can yield a significant improvement over the baselines on various automatic and human evaluation metrics.
Anthology ID:
P19-1360
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3696–3709
Language:
URL:
https://aclanthology.org/P19-1360
DOI:
10.18653/v1/P19-1360
Bibkey:
Cite (ACL):
Wenhu Chen, Jianshu Chen, Pengda Qin, Xifeng Yan, and William Yang Wang. 2019. Semantically Conditioned Dialog Response Generation via Hierarchical Disentangled Self-Attention. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3696–3709, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Semantically Conditioned Dialog Response Generation via Hierarchical Disentangled Self-Attention (Chen et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1360.pdf
Code
 budzianowski/multiwoz +  additional community code
Data
MultiWOZ