Papers by Ruifan Li
KE-GCL: Knowledge Enhanced Graph Contrastive Learning for Commonsense Question Answering (2022.findings-emnlp)
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| Challenge: | Existing models for commonsense question answering lack effective representations of knowledge graphs. |
| Approach: | They propose a Knowledge Enhanced Graph Contrastive Learning model by incorporating contextual descriptions into QA pairs and adopting a graph contrastive learning scheme. |
| Outcome: | The proposed model outperforms existing methods consistently on two benchmark datasets. |
COM-MRC: A COntext-Masked Machine Reading Comprehension Framework for Aspect Sentiment Triplet Extraction (2022.emnlp-main)
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| Challenge: | Aspect Sentiment Triplet Extraction (ASTE) aims to extract sentiment triplets from sentences, but when faced with multiple aspect terms, the MRC-based methods could fail due to the interference from other aspect terms. |
| Approach: | They propose a COntext-Masked MRC framework for Aspect Sentiment Triplet Extraction (ASTE) which aims to extract sentiment triplets from sentences . |
| Outcome: | The proposed framework outperforms state-of-the-art methods on benchmark datasets and shows that it can extract sentiment triplets from multiple aspect terms. |
USSA: A Unified Table Filling Scheme for Structured Sentiment Analysis (2023.acl-long)
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| Challenge: | Structured Sentiment Analysis (SSA) is a problem of bi-lexical dependency parsing . previous studies have cast it as a bottleneck because of overlap and discontinuity issues . |
| Approach: | They propose a bi-lexical dependency parsing graph and a table-filling scheme that addresses overlap and discontinuity issues. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on benchmark datasets. |
Dual Graph Convolutional Networks for Aspect-based Sentiment Analysis (2021.acl-long)
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| Challenge: | Existing methods to model relationships between aspects and opinion words are inefficient due to informal expressions and complexity of online reviews. |
| Approach: | They propose a dual graph convolutional networks model that considers complementarity of syntax structures and semantic correlations simultaneously. |
| Outcome: | The proposed model outperforms state-of-the-art methods on three public datasets and validates it. |
Multimodal Aspect-Based Sentiment Analysis under Conditional Relation (2025.coling-main)
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| Challenge: | Existing methods to analyze social media sentiments rely on image-based aspects. |
| Approach: | They propose a multi-task framework to extract aspect terms from text-image pairs and identify their sentiments. |
| Outcome: | The proposed framework outperforms existing methods on a text-image dataset. |
Progressive Planning and Reinforced Reasoning: Large Language Model-Guided Multi-hop Question Answering over Knowledge Graph (2026.findings-acl)
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| Challenge: | Existing approaches to multi-hop question answering lack effective intermediate guidance and policy networks focus on local neighborhood information, making it difficult to anticipate the long-term consequences of decisions. |
| Approach: | They propose a framework that converts decomposed sub-question sequences into stepwise decision guidance and a structure-aware lookahead policy network to enhance the agent's global state awareness and decision foresight in complex environments. |
| Outcome: | The proposed framework surpasses state-of-the-art methods while showing strong generalization. |
XY-Tokenizer: Mitigating the Semantic-Acoustic Conflict in Low-Bitrate Speech Codecs (2026.acl-long)
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Yitian Gong, Luozhijie Jin, Kuangwei Chen, Dong Zhang, Ruifan Deng, Xiaogui Yang, Xin Zhang, Zhaoye Fei, Qinyuan Cheng, Shimin Li, Xipeng Qiu
| Challenge: | Existing speech codecs struggle to balance these objectives at low bitrates . XY-Tokenizer achieves stronger semantic alignment than representative semantic-distillation codec . |
| Approach: | They propose a low-bitrate speech codec that aligns discrete speech representations with text while preserving fine-grained acoustic details for reconstruction. |
| Outcome: | The proposed codec outperforms existing low-bitrate speech codecs in speech understanding and generation tasks. |
Multimodal Document-level Triple Extraction via Dynamic Graph Enhancement and Relation-Aware Reflection (2025.findings-emnlp)
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| Challenge: | Existing methods for extracting structured triples knowledge from multimodal documents face limitations in simultaneously processing long textual content and multiple associated images for triple extraction. |
| Approach: | They propose a multimodal document-level triple extraction framework that integrates multimodal text and visual content into a large language model and injects the global information and external knowledge into the model. |
| Outcome: | The proposed framework outperforms the state-of-the-art methods and fills the gap in multimodal document extraction. |
Enhanced Multi-Channel Graph Convolutional Network for Aspect Sentiment Triplet Extraction (2022.acl-long)
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| Challenge: | Existing methods to extract aspect triplets ignore the relationships between words . Enhanced Multi-Channel Graph Convolutional Network model can be used to learn relation-aware node representations. |
| Approach: | They propose an Enhanced Multi-Channel Graph Convolutional Network model to fully utilize the relations between words for ASTE task. |
| Outcome: | The proposed model outperforms state-of-the-art methods significantly on a benchmark dataset. |
A Simple Model for Distantly Supervised Relation Extraction (2022.coling-1)
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| Challenge: | Recent methods focus on exploiting bag representations with complex de-noising scheme to achieve remarkable performance. |
| Approach: | They propose a BERT-based Graph convolutional network model that exploits bag representations . their model extracts key information from each instance and constructs a bag graph . |
| Outcome: | The proposed model improves on two benchmark datasets, i.e., NYT10 and GDS. |