Papers by Yeshuang Zhu
Selecting Stickers in Open-Domain Dialogue through Multitask Learning (2022.findings-acl)
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| Challenge: | Existing methods to select appropriate stickers in open-domain dialogues have not been explored. |
| Approach: | They propose a multitask learning method consisting of three auxiliary tasks to combine multimodal information to enhance the understanding of dialogue history, emotion and semantic meaning of stickers. |
| Outcome: | The proposed model can combine multimodal information and achieve significantly higher accuracy over strong baselines. |
Toward Fully Exploiting Heterogeneous Corpus:A Decoupled Named Entity Recognition Model with Two-stage Training (2021.findings-acl)
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| Challenge: | Named Entity Recognition (NER) is a fundamental and widely used task in natural language processing. |
| Approach: | They propose a decoupled NER model with two-stage training to take advantage of heterogeneous corpus, including dictionaries, distantly supervised instances, and human-annotated instances. |
| Outcome: | Empirical results show that the proposed model improves against baselines and can be scaled to a large extent. |
AutoCAD: Automatically Generate Counterfactuals for Mitigating Shortcut Learning (2022.findings-emnlp)
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| Challenge: | Existing methods for generating counterfactuals rely on human efforts or task-specific designs. |
| Approach: | They propose to use a fully automatic and task-agnostic CAD generation framework to generate diverse counterfactuals. |
| Outcome: | The proposed framework outperforms human-in-the-loop and task-specific CAD methods on multiple out-of-domain and challenge benchmarks. |