Modeling Local Contexts for Joint Dialogue Act Recognition and Sentiment Classification with Bi-channel Dynamic Convolutions

Jingye Li, Hao Fei, Donghong Ji


Abstract
In this paper, we target improving the joint dialogue act recognition (DAR) and sentiment classification (SC) tasks by fully modeling the local contexts of utterances. First, we employ the dynamic convolution network (DCN) as the utterance encoder to capture the dialogue contexts. Further, we propose a novel context-aware dynamic convolution network (CDCN) to better leverage the local contexts when dynamically generating kernels. We extended our frameworks into bi-channel version (i.e., BDCN and BCDCN) under multi-task learning to achieve the joint DAR and SC. Two channels can learn their own feature representations for DAR and SC, respectively, but with latent interaction. Besides, we suggest enhancing the tasks by employing the DiaBERT language model. Our frameworks obtain state-of-the-art performances against all baselines on two benchmark datasets, demonstrating the importance of modeling the local contexts.
Anthology ID:
2020.coling-main.53
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Editors:
Donia Scott, Nuria Bel, Chengqing Zong
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
616–626
Language:
URL:
https://aclanthology.org/2020.coling-main.53
DOI:
10.18653/v1/2020.coling-main.53
Bibkey:
Cite (ACL):
Jingye Li, Hao Fei, and Donghong Ji. 2020. Modeling Local Contexts for Joint Dialogue Act Recognition and Sentiment Classification with Bi-channel Dynamic Convolutions. In Proceedings of the 28th International Conference on Computational Linguistics, pages 616–626, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
Modeling Local Contexts for Joint Dialogue Act Recognition and Sentiment Classification with Bi-channel Dynamic Convolutions (Li et al., COLING 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.coling-main.53.pdf
Data
DailyDialog