Papers by Xichen Shang
Pair-Based Joint Encoding with Relational Graph Convolutional Networks for Emotion-Cause Pair Extraction (2022.emnlp-main)
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| Challenge: | Emotion cause pair extraction (ECPE) aims to extract emotion clauses and corresponding cause clauses. |
| Approach: | They propose a novel task called emotion-cause pair extraction to extract emotion clauses and corresponding cause clauses. |
| Outcome: | The proposed task can extract emotion clauses and cause clauses, and achieve state-of-the-art performance on the Chinese benchmark corpus. |
A Span-based Dynamic Local Attention Model for Sequential Sentence Classification (2021.acl-short)
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| Challenge: | Existing methods for sentence classification ignore latent segment structure of document, in which contiguous sentences have coherent semantics. |
| Approach: | They propose a span-based dynamic local attention model that captures structural information by supervised dynamic local focus. |
| Outcome: | The proposed model outperforms state-of-the-art models on two benchmark datasets. |
Joint Constrained Learning with Boundary-adjusting for Emotion-Cause Pair Extraction (2023.acl-long)
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| Challenge: | Emotion-Cause Pair Extraction (ECPE) aims to identify the document’s emotion clauses and corresponding cause clauses. |
| Approach: | They propose a constrained learning framework with boundary-adjusting for Emotion-Cause Pair Extraction that summarizes prior rules and forces the model to take them into consideration in optimization. |
| Outcome: | The proposed framework achieves competitive results compared with state-of-the-art methods on unbalanced data and proves robustness on unbalancing data. |
Preserving Commonsense Knowledge from Pre-trained Language Models via Causal Inference (2023.acl-long)
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Junhao Zheng, Qianli Ma, Shengjie Qiu, Yue Wu, Peitian Ma, Junlong Liu, Huawen Feng, Xichen Shang, Haibin Chen
| Challenge: | Existing studies attribute catastrophic forgetting to fine-tuning, and they retain pre-trained knowledge indiscriminately without identifying what knowledge is transferable. |
| Approach: | They propose a unified objective for fine-tuning to retrieve the causality back from pre-trained data and use it to mitigate negative transfer while preserving knowledge. |
| Outcome: | The proposed method outperforms state-of-the-art fine-tuning methods on commonsense QA datasets and can be implemented as a plug-in module to inflate the performance of existing QA models. |