Yeshuang Zhu
2022
Selecting Stickers in Open-Domain Dialogue through Multitask Learning
Zhexin Zhang
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Yeshuang Zhu
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Zhengcong Fei
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Jinchao Zhang
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Jie Zhou
Findings of the Association for Computational Linguistics: ACL 2022
With the increasing popularity of online chatting, stickers are becoming important in our online communication. Selecting appropriate stickers in open-domain dialogue requires a comprehensive understanding of both dialogues and stickers, as well as the relationship between the two types of modalities. To tackle these challenges, we propose a multitask learning method comprised of three auxiliary tasks to enhance the understanding of dialogue history, emotion and semantic meaning of stickers. Extensive experiments conducted on a recent challenging dataset show that our model can better combine the multimodal information and achieve significantly higher accuracy over strong baselines. Ablation study further verifies the effectiveness of each auxiliary task. Our code is available at https://github.com/nonstopfor/Sticker-Selection.
2021
Toward Fully Exploiting Heterogeneous Corpus:A Decoupled Named Entity Recognition Model with Two-stage Training
Yun Hu
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Yeshuang Zhu
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Jinchao Zhang
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Changwen Zheng
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Jie Zhou
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
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Co-authors
- Jinchao Zhang 2
- Jie Zhou 2
- Yun Hu 1
- Changwen Zheng 1
- Zhexin Zhang 1
- show all...