Papers by Heyan Chai
Aspect-to-Scope Oriented Multi-view Contrastive Learning for Aspect-based Sentiment Analysis (2023.findings-emnlp)
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| Challenge: | Existing methods for Aspect-based sentiment analysis (ABSA) focus on mining syntactic or semantic information, which suffers from noisy interference when multiple aspects exist in a sentence. |
| Approach: | They propose a scope-assisted multi-view graph contrastive learning framework that captures correlation and difference between aspect and syntactic/semantic information. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on five benchmark datasets and verifies its effectiveness and robustness. |
Affective Knowledge Enhanced Multiple-Graph Fusion Networks for Aspect-based Sentiment Analysis (2022.emnlp-main)
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| Challenge: | Existing methods for sentiment analysis ignore the roles of syntax dependency relation labels and affective semantic information in determining the sentiment polarity of social media users. |
| Approach: | They propose a new multi-graph fusion network to leverage the richer syntax dependency relation labels and affective semantic information of words. |
| Outcome: | The proposed model outperforms state-of-the-art methods on three datasets. |
Improving Multi-task Stance Detection with Multi-task Interaction Network (2022.emnlp-main)
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| Challenge: | Recent studies have proposed multi-task learning models that introduce sentiment information to boost stance detection but neglect to capture the fine-grained task-specific interaction between stance and sentiment tasks, thus degrading performance. |
| Approach: | They propose a novel multi-task interaction network (MTIN) that captures the word-level interaction between tasks, so as to obtain richer task representations. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on two real-world datasets. |
Improving Gradient Trade-offs between Tasks in Multi-task Text Classification (2023.acl-long)
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| Challenge: | Existing methods to mitigate task conflict problem are heuristics or gradient-based algorithms to achieve an arbitrary Pareto optimal trade-off among different tasks . |
| Approach: | They propose a gradient trade-off approach to mitigate the task conflict problem by using heuristics or gradient-based algorithms to achieve an arbitrary Pareto optimal trade- off among different tasks. |
| Outcome: | The proposed model can achieve an arbitrary Pareto optimal trade-off among different tasks near the main objective of multi-task text classification (MTC) it is found that training all tasks simultaneously yields degraded performance than learning them independently, leading to poor training. |