Papers by Yong Dou
Argumentation Mining on Essays at Multi Scales (2020.coling-main)
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| Challenge: | Argumentation mining on essays is a new task in natural language processing. |
| Approach: | They propose a multi-scale argumentation mining model which aims to identify the types and locations of argumentation components from essay text. |
| Outcome: | The proposed model outperforms existing models on mining all types of argumentation components on the Persuasive Essay dataset. |
A New Pipeline for Knowledge Graph Reasoning Enhanced by Large Language Models Without Fine-Tuning (2024.emnlp-main)
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| Challenge: | Conventional knowledge Graph Reasoning models learn the embeddings of KG components over the structure of a KG. |
| Approach: | They propose a pipeline to integrate knowledge from LLMs into KGs without fine-tuning . they propose knowledge alignment, KG reasoning and entity reranking to enhance conventional models . |
| Outcome: | The proposed pipeline can enhance the performance of conventional KGR models in incomplete and general situations. |
IMCI: Integrate Multi-view Contextual Information for Fact Extraction and Verification (2022.coling-1)
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| Challenge: | Existing models for fact extraction and verification fail to utilize multi-view contextual information. |
| Approach: | They propose to integrate multi-view contextual information (IMCI) for fact extraction and verification by combining contextual information with inter-document context. |
| Outcome: | The proposed framework achieves state-of-the-art performance on the open-domain Wikipedia task with a winning FEVER score of 73.96% and label accuracy of 77.25% on the online blind test set. |
RSGT: Relational Structure Guided Temporal Relation Extraction (2022.coling-1)
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| Challenge: | Temporal relation extraction (TRE) is crucial for natural language understanding. |
| Approach: | They propose a Temporal Relational Structure Guided Temporal Relations Extraction task to extract relational structure features that can fit for both inter-sentence and intra-sentent relations. |
| Outcome: | The proposed method improves on two well-known datasets, MATRES and TB-Dense, and can be used for clinical diagnosis and summarization. |
Unsupervised Sentence Textual Similarity with Compositional Phrase Semantics (2022.coling-1)
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| Challenge: | Sentence Textual Similarity (STS) is a classic task that can be applied to downstream NLP applications such as text generation and retrieval. |
| Approach: | They propose a light-weighted Expectation-Correction (EC) formulation for STS computation. |
| Outcome: | The proposed approach is more efficient and scalable than previous approaches. |
Temporal Extrapolation and Knowledge Transfer for Lifelong Temporal Knowledge Graph Reasoning (2023.findings-emnlp)
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| Challenge: | Existing methods for lifelong TKG reasoning only address part of the challenges. |
| Approach: | They propose a temporal-path-based reinforcement learning framework for lifelong TKG reasoning . they add temporal displacement into the action space of RL to extrapolate for the future . |
| Outcome: | The proposed model outperforms existing models against well-adapted baselines on three lifelong TKG reasoning benchmarks. |
Adaptive Threshold Selective Self-Attention for Chinese NER (2022.coling-1)
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| Challenge: | Named entity recognition (NER) is a computationally difficult task in Chinese since there is no natural delimiter between words in sentences. |
| Approach: | They propose a data-driven Adaptive Threshold Selective Self-Attention mechanism to select the most relevant characters to enhance Transformer architecture for Chinese named entity recognition. |
| Outcome: | Experiments on four benchmark Chinese NER datasets show the proposed mechanism improves performance. |
Partial Order-centered Hyperbolic Representation Learning for Few-shot Relation Extraction (2025.coling-main)
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| Challenge: | Existing methods for few-shot relation extraction are limited to labeled instances and rely on data labeling. |
| Approach: | They propose a partial order-centered hyperbolic representation learning framework which imposes constraints on relations on instances by modeling partial order in hyperbolical space. |
| Outcome: | The proposed framework outperforms baseline methods on three benchmark datasets on 1-shot settings lacking relation descriptions. |