Papers by Sinno Jialin Pan
Deep Weighted MaxSAT for Aspect-based Opinion Extraction (2020.emnlp-main)
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| Challenge: | Existing deep learning models lack the capability to encode explicit domain knowledge to model complex causal relationships among variables. |
| Approach: | They propose a model that uses a weighted version of MaxSAT to model logic inference . they propose to use this model to rectify erroneous predictions from deep neural networks . |
| Outcome: | The proposed model combines the benefits of high-level feature learning, knowledge reasoning, and structured learning with observable performance gain for aspect-based opinion extraction. |
Analytical FFN-to-MoE Restructuring via Activation Pattern Analysis (2026.acl-long)
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Zehua Pei, Hui-Ling Zhen, Lancheng Zou, Xianzhi Yu, Wulong Liu, Sinno Jialin Pan, Mingxuan Yuan, Bei Yu
| Challenge: | Large language models (LLMs) are fast but require expensive pre-training . a new approach to scale large language models into MoEs reduces inference costs . |
| Approach: | They propose an analytical post-training framework that rapidly restructures FFNs into sparse MoE architectures using only a small calibration dataset. |
| Outcome: | The proposed framework outperforms existing methods on a small calibration dataset. |
Recursive Neural Structural Correspondence Network for Cross-domain Aspect and Opinion Co-Extraction (P18-1)
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| Challenge: | supervised learning methods have been used for fine-grained opinion analysis but lack of labeled data hinders learning . authors develop a recursive neural network that could reduce domain shift in word level . a recent paper shows that unsupervised methods fail to adapt well across domains . |
| Approach: | They propose a supervised neural network that reduces domain shift effectively in word level . they treat these relations as invariant "pivot information" across domains to build structural correspondences . |
| Outcome: | The proposed model reduces domain shift effectively in word level through syntactic relations . it can be used to predict the relation between two adjacent words in the dependency tree . |
Reinforcing Compositional Retrieval: Retrieving Step-by-Step for Composing Informative Contexts (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) have demonstrated remarkable capabilities across numerous tasks, yet they often rely on external context to handle complex tasks. |
| Approach: | They propose a tri-encoder sequential retriever that models a Markov Decision Process (MDP) this method decomposes the probability of retrieving a set of elements into a sequence of conditional probabilities and allows each retrieval step to be conditioned on previously selected examples. |
| Outcome: | The proposed method outperforms baselines and shows that it can handle multiple pieces of evidence or examples. |
Bidirectional Generative Framework for Cross-domain Aspect-based Sentiment Analysis (2023.acl-long)
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| Challenge: | Aspect-based sentiment analysis (ABSA) is a task of analyzing people's sentiments at the aspect level. |
| Approach: | They propose a unified bidirectional generative framework to tackle cross-domain ABSA tasks . the framework trains a model in both text-to-label and label-totext directions . |
| Outcome: | The proposed framework trains a model in both label-to-label and label- to-text directions to learn domain-agnostic features. |