Papers by Ruifang Liu
TWEETSUM: Event oriented Social Summarization Dataset (2020.coling-main)
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| Challenge: | Developing social summarization systems is becoming more and more critical . but, the publicly available and high-quality large scale social summaries are rare . |
| Approach: | They propose to build a social summarization dataset using twitter's hot events . they collect user relations, hashtags and user profiles to evaluate their summarizing methods . |
| Outcome: | The proposed dataset is based on a dataset from twitter with 12 real world hot events with 44,034 tweets and 11,240 users. |
Representation Degeneration Problem in Prompt-based Models for Natural Language Understanding (2024.lrec-main)
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| Challenge: | Prompt-based fine-tuning (PF) models have shown improved performance on few-shot natural language understanding benchmarks. |
| Approach: | They propose a framework to alleviate anisotropy in the embedding space by aligning with pre-trained language models' training objective. |
| Outcome: | The proposed method outperforms mainstream methods on many NLU benchmarks. |
Clear Up Confusion: Advancing Cross-Domain Few-Shot Relation Extraction through Relation-Aware Prompt Learning (2024.naacl-short)
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Ge Bai, Chenji Lu, Daichi Guo, Shilong Li, Ying Liu, Zhang Zhang, Guanting Dong, Ruifang Liu, Sun Yong
| Challenge: | Existing approaches to few-shot Relation Extraction (RE) are prone to confusion when applying knowledge to a target domain with entirely new types of relations. |
| Approach: | They propose a relation-aware prompt learning method with pre-training to clear confusion by decomposing relation types through an innovative label prompt. |
| Outcome: | The proposed method outperforms previous sota methods and yields better results on cross-domain few-shot RE tasks. |
Always the Best Fit: Adaptive Domain Gap Filling from Causal Perspective for Few-Shot Relation Extraction (2023.findings-emnlp)
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Ge Bai, Chenji Lu, Jiaxiang Geng, Shilong Li, Yidong Shi, Xiyan Liu, Ying Liu, Zhang Zhang, Ruifang Liu
| Challenge: | Existing approaches to cross-domain relation extraction have been limited by domains . data bias between domains can be difficult to fill, especially in few-shot scenarios . |
| Approach: | They propose a framework to bridge the semantic gap caused by data bias between domains . they use syntactic structure, label distribution, and entities to calculate causal effects . |
| Outcome: | The proposed framework fills the domain gap and yields better results on the few-shot task. |
MTGP: Multi-turn Target-oriented Dialogue Guided by Generative Global Path with Flexible Turns (2023.findings-acl)
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| Challenge: | Existing approaches focus on global planning, which plans toward the target before the conversation. |
| Approach: | They propose to generate a global path as a natural language sentence instead of a sequence of nodes. |
| Outcome: | The proposed method has fewer turns, more coherent semantics, and higher success rate than baselines. |
ECC: Synergizing Emotion, Cause and Commonsense for Empathetic Dialogue Generation (2025.coling-main)
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| Challenge: | Empathy improves human-machine dialogue systems by enhancing the user's experience. |
| Approach: | They propose a framework that leverages specialized encoders to capture the key features of emotion, cause, and commonsense and collaboratively models these through a Conditional Variational Auto-Encoder. |
| Outcome: | Empirical results show that the framework outperforms baseline models and offers a robust solution for empathetic dialogue generation. |
RoleBreak: Character Hallucination as a Jailbreak Attack in Role-Playing Systems (2025.coling-main)
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| Challenge: | Existing approaches to combat character hallucination are vulnerable to attack . large language models (LLMs) are capable of generating responses inconsistent with intended personas . |
| Approach: | They propose a novel defence strategy that generates supplemental context through narration to mitigate role-query conflicts and improve query generalization. |
| Outcome: | The proposed defence strategy outperforms refusal-based strategies in character hallucinations and query generalization. |
Continuous Relational Diffusion Driven Topic Model with Multi-grained Text for Microblog (2024.lrec-main)
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| Challenge: | Existing topic models assume that there are only 0/1-state relationships between the two parties in social networks, but the relationship status in real life is more complicated. |
| Approach: | They propose a topic model that leverages unsupervised learning to mine hidden topics in document collections using multi-grained text. |
| Outcome: | The proposed model can be applied to microblog with multi-grained text to realize the representation of the relationship state and make up for the context and structural information lost by previous representation methods. |
Fusion Makes Perfection: An Efficient Multi-Grained Matching Approach for Zero-Shot Relation Extraction (2024.naacl-short)
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| Challenge: | Existing methods to extract unseen relations require laborious manual annotation . a new approach uses fine-grained matching to reduce manual annotation cost . |
| Approach: | They propose an efficient multi-grained matching approach that uses virtual entity matching to reduce manual annotation cost. |
| Outcome: | The proposed approach outperforms the state-of-the-art methods and achieves inference efficiency and accuracy in zero-shot relation extraction tasks. |
Enhancing Label Correlation Feedback in Multi-Label Text Classification via Multi-Task Learning (2021.findings-acl)
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| Challenge: | Existing approaches to multi-task learning fail to capture label correlations . Existing methods suffer from label order dependency, label combination over-fitting and error propagation problems. |
| Approach: | They propose a novel approach with multi-task learning to enhance label correlation feedback. |
| Outcome: | The proposed method outperforms baselines on AAPD and RCV1-V2 datasets. |
Global and Local Hierarchical Prompt Tuning Framework for Multi-level Implicit Discourse Relation Recognition (2024.lrec-main)
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| Challenge: | Recent methods to recognize hierarchical discourse relations without explicit connectives are inefficient and ignore the utilization of the output probability distribution information of the verbalizer. |
| Approach: | They propose a global and local hierarchical prompt tuning framework which leverages top-up propagated probability as the global hierarchy to inject it into multi-level verbalizer. |
| Outcome: | The proposed framework achieves competitive results on two benchmacks. |
Guiding Dialogue Agents to Complex Semantic Targets by Dynamically Completing Knowledge Graph (2023.findings-acl)
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| Challenge: | Existing knowledge graphs are incomplete in tracking complex semantic relations of the target-oriented dialogue. |
| Approach: | They combine methods of knowledge retrieval and relationship prediction to construct a context-related dynamic KG and a metric to evaluate the tracked path automatically. |
| Outcome: | The proposed method can control the agent more logically and smoothly toward the complex target. |