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        <title>Modeling Local Contexts for Joint Dialogue Act Recognition and Sentiment Classification with Bi-channel Dynamic Convolutions</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T Modeling Local Contexts for Joint Dialogue Act Recognition and Sentiment Classification with Bi-channel Dynamic Convolutions
%A Li, Jingye
%A Fei, Hao
%A Ji, Donghong
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F li-etal-2020-modeling
%X 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.
%R 10.18653/v1/2020.coling-main.53
%U https://aclanthology.org/2020.coling-main.53
%U https://doi.org/10.18653/v1/2020.coling-main.53
%P 616-626

Markdown (Informal)

[Modeling Local Contexts for Joint Dialogue Act Recognition and Sentiment Classification with Bi-channel Dynamic Convolutions](https://aclanthology.org/2020.coling-main.53) (Li et al., COLING 2020)

• Modeling Local Contexts for Joint Dialogue Act Recognition and Sentiment Classification with Bi-channel Dynamic Convolutions (Li et al., COLING 2020)

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.