Papers by Chengjie Sun
How Pre-trained Language Models Capture Factual Knowledge? A Causal-Inspired Analysis (2022.findings-acl)
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Shaobo Li, Xiaoguang Li, Lifeng Shang, Zhenhua Dong, Chengjie Sun, Bingquan Liu, Zhenzhou Ji, Xin Jiang, Qun Liu
| Challenge: | Recent studies show that pre-trained language models can fill in the missing factual words in cloze-style prompts such as ”Dante was born in [MASK]” . |
| Approach: | They propose to quantitatively measure and evaluate the word-level patterns that PLMs depend on to generate the missing factual words. |
| Outcome: | The proposed model fills in the missing factual words in cloze-style prompts by relying on effective clues or shortcut patterns. |
A Dual Contrastive Learning Framework for Enhanced Multimodal Conversational Emotion Recognition (2025.coling-main)
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| Challenge: | Existing methods struggle to capture emotion shifts due to label replication and fail to preserve positive independent modality contributions during fusion. |
| Approach: | They propose a Dual Contrastive Learning Framework that enhances existing MERC models without additional data. |
| Outcome: | The proposed framework outperforms existing models on two MERC benchmark datasets and shows that it reduces label dependence and enhances emotion-sensitive independent modality features. |
Time-for-Accuracy: Formalizing Chain-of-Thought as an Expansion of Logical Depth (2026.findings-acl)
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| Challenge: | Chain-of-thought (CoT) prompting can improve multi-step reasoning, but it is unclear what kind of additional sequential computation longer traces actually enable. |
| Approach: | They propose a deletion-based measure of step necessity under a specified inference interface to operationalize realized depth beyond raw length. |
| Outcome: | The proposed method combines effective logical depth with Bennett's logical depth to show that it is more efficient than a linear model. |
Towards Faithful Knowledge Graph Explanation Through Deep Alignment in Commonsense Question Answering (2024.emnlp-main)
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| Challenge: | Current methods for generating faithful explanations overlook path decoding faithfulness, leading to divergence between graph encoder outputs and model predictions. |
| Approach: | They propose an algorithm to assess KG representation reliability and an LM-KG distribution-aware Alignment algorithm to improve explanation faithfulness without ground truth. |
| Outcome: | The proposed algorithm improves explanation faithfulness without ground truth and significantly improves fidelity and model performance. |
Pre-training Language Models with Deterministic Factual Knowledge (2022.emnlp-main)
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| Challenge: | Existing studies show that Pre-trained Language Models fail to capture factual knowledge robustly. |
| Approach: | They propose to let PLMs learn the deterministic relationship between context and masked content to improve their ability to capture factual knowledge. |
| Outcome: | The proposed methods improve accuracy and consistency of factual knowledge capturing and boost performance of other knowledge-intensive tasks. |
Knowledge-Interactive Network with Sentiment Polarity Intensity-Aware Multi-Task Learning for Emotion Recognition in Conversations (2021.findings-emnlp)
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| Challenge: | Emotion Recognition in Conversation models neglect direct utterance-knowledge interaction and use emotion-indirect auxiliary tasks to augment semantic information. |
| Approach: | They propose a Knowledge-Interactive Network with sentiment polarity intensity-aware multi-task learning which leverages both commonsense knowledge and sentiment lexicon to augment semantic information. |
| Outcome: | The proposed model shows 1.04% performance improvement over the state-of-the-art model on the IEMOCAP dataset. |