Papers by Yafeng Ren
MRN: A Locally and Globally Mention-Based Reasoning Network for Document-Level Relation Extraction (2021.findings-acl)
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| Challenge: | Existing studies on document-level relation extraction focus on sentencelevel RE, but recent studies reveal that a large number of relations can actually be expressed through multiple sentences, which necessitates document- level RE. |
| Approach: | They propose a document-level relation extraction model that captures local and global contextual information as well as close and distant mention interactions. |
| Outcome: | The proposed model outperforms state-of-the-art models on three widely used datasets, namely DocRED, CDR, and GDA. |
DDGIP: Radiology Report Generation Through Disease Description Graph and Informed Prompting (2025.findings-naacl)
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| Challenge: | Automatic radiology report generation is challenging due to inherent biases in medical imaging data. |
| Approach: | They propose a disease description graph that encapsulates comprehensive and pertinent disease information. |
| Outcome: | The proposed model outperforms state-of-the-art models on two widely-used datasets . the proposed model is based on a three-layer decoder and improves on existing models . |
Time-Aware Language Modeling for Historical Text Dating (2023.findings-emnlp)
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| Challenge: | Existing approaches to automatic text dating ignore diachronic change of words, which may affect the efforts of text modeling. |
| Approach: | They propose a time-aware language model to learn temporal word representations by transferring language models of general domains to those of time-specific ones and build a hierarchical modeling approach to represent diachronic documents by encoding them with temporal representations. |
| Outcome: | The proposed model outperforms state-of-the-art approaches in historical text dating and other NLP tasks. |
Mimic and Conquer: Heterogeneous Tree Structure Distillation for Syntactic NLP (2020.findings-emnlp)
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| Challenge: | Existing work on integrating syntactic information into neural networks uses a single tree, such as a constituency or a dependency tree. |
| Approach: | They propose a method to integrate heterogeneous structure knowledge into a unified sequential LSTM encoder. |
| Outcome: | The proposed method outperforms tree encoders on four syntax-dependent tasks and is efficient and accurate. |
Retrofitting Structure-aware Transformer Language Model for End Tasks (2020.emnlp-main)
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| Challenge: | Experimental results show that structure-aware Transformer language model achieves improved perplexity, meanwhile inducing accurate syntactic phrases. |
| Approach: | They propose to exploit syntactic distance to encode phrasal constituency and dependency connection into Transformer language model and leverage it for structure integration. |
| Outcome: | The proposed model achieves significant improvements for both semantic- and syntactic-dependent tasks. |
Better Combine Them Together! Integrating Syntactic Constituency and Dependency Representations for Semantic Role Labeling (2021.findings-acl)
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| Challenge: | Existing studies use only one singleton syntax for semantic role labeling (SRL). |
| Approach: | They propose a TreeLSTM-based integration that integrates phrasal boundaries and semantic relations from dependency into a labelaware GCN solution for simultaneously modeling syntactic edges and labels. |
| Outcome: | The proposed system achieves state-of-the-art performance on span-based and dependency-based SRL. |
Improving Text Understanding via Deep Syntax-Semantics Communication (2020.findings-emnlp)
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| Challenge: | Recent studies show that integrating syntactic tree models with sequential semantic models can bring improved task performance. |
| Approach: | They propose a deep neural communication model between syntax and semantics to improve the performance of text understanding. |
| Outcome: | The proposed model outperforms baseline models on syntax-dependent tasks by a large margin. |
High-order Refining for End-to-end Chinese Semantic Role Labeling (2020.aacl-main)
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| Challenge: | Current semantic role labeling methods are limited to short-term features and local decisions. |
| Approach: | They propose a high-order refining mechanism to perform interaction between all predicate-argument pairs via attention calculation. |
| Outcome: | The proposed model achieves state-of-the-art on Chinese SRL data, including CoNLL09 and Universal Proposition Bank, while relieving the long-range dependency issues. |