Papers by Ruifeng Xu

Mask-then-Fill: A Flexible and Effective Data Augmentation Framework for Event Extraction (2022.findings-emnlp)

Challenge:	Existing evaluation metrics for open-domain dialogue systems are limited by the diversity of possible outcomings.
Approach:	They propose a method to augment a reference set to improve reliability . they propose BLEU to measure similarity between a predicted response and a small set of references .
Outcome:	The proposed model improves the reliability of reference-based metrics with augmented reference sets.

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Jun Gao, Changlong Yu, Wei Wang, Huan Zhao, Ruifeng Xu

Improving In-Context Learning with Prediction Feedback for Sentiment Analysis (2024.findings-acl)

Challenge:	Existing data augmentation methods for event extraction are costly and time-consuming.
Approach:	They propose a data augmentation framework that randomly masks out an adjunct sentence fragment and infills a variable-length text span with a fine-tuned infilling model.
Outcome:	The proposed framework can generate more diverse data while keeping the original structure unchanged . it can replace a fragment of arbitrary length in the text with another fragment of variable length .

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Hongling Xu, Qianlong Wang, Yice Zhang, Min Yang, Xi Zeng, Bing Qin, Ruifeng Xu

Low-Resource NLP

Challenge:	Large language models (LLMs) have achieved promising results in sentiment analysis through the in-context learning paradigm.
Approach:	They propose a framework that incorporates prior predictions and feedback to improve sentiment understanding by incorporating prior feedback and leveraging a feedback-driven prompt.
Outcome:	The proposed framework improves on nine sentiment analysis datasets with an average improvement of 5.95% over conventional methods.

The Design and Construction of a Chinese Sarcasm Dataset (2020.lrec-1)

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Xiaochang Gong, Qin Zhao, Jun Zhang, Ruibin Mao, Ruifeng Xu

JointCL: A Joint Contrastive Learning Framework for Zero-Shot Stance Detection (2022.acl-long)

Challenge:	Existing sarcasm datasets are limited to English and Chinese . sarcasm is a multi-layered semi-conscious language phenomenon .
Approach:	They propose to build a high-quality Chinese sarcasm dataset using user comments . they use manual annotated sarkastic texts and non-sarcastic texts to train sarcasm classifier .
Outcome:	The proposed dataset contains 2,486 manual annotated sarcastic texts and 89,296 non-sarcatic texts.

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Bin Liang, Qinglin Zhu, Xiang Li, Min Yang, Lin Gui, Yulan He, Ruifeng Xu

Comprehensive and Efficient Distillation for Lightweight Sentiment Analysis Models (2025.emnlp-main)

Challenge:	Existing methods achieve promising performance in in-target stance detection when trained and tested on the same datasets.
Approach:	They propose a joint contrastive learning framework to generalize stance features for unseen targets.
Outcome:	The proposed framework achieves state-of-the-art on three benchmark datasets.

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Guangyu Xie, Yice Zhang, Jianzhu Bao, Qianlong Wang, Yang Sun, Bingbing Wang, Ruifeng Xu

Green & Sustainable NLP

Challenge:	Recent efforts to develop lightweight and practical sentiment analysis models are limited by manual instruction and large-scale user texts.
Approach:	They propose a framework for sentiment analysis that uses attribute-based instruction construction and difficulty-based data filtering to distill knowledge.
Outcome:	The proposed framework outperforms baseline methods in data efficiency and performance.

Multi-modal Stance Detection: New Datasets and Model (2024.findings-acl)

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Bin Liang, Ang Li, Jingqian Zhao, Lin Gui, Min Yang, Yue Yu, Kam-Fai Wong, Ruifeng Xu

CLHA: A Simple Yet Effective Contrastive Learning Framework for Human Alignment (2024.lrec-main)

Challenge:	Existing methods for stance detection for pure texts have limited results to multi-modal content.
Approach:	They propose a multi-modal stance detection framework that leverages target information to learn multi-modal stance features from textual and visual modalities.
Outcome:	The proposed framework achieves state-of-the-art in multi-modal stance detection on five datasets based on Twitter .

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Feiteng Fang, Liang Zhu, Xi Feng, Jinchang Hou, Qixuan Zhao, Chengming Li, Xiping Hu, Ruifeng Xu, Min Yang

Reducing Spurious Correlations in Aspect-based Sentiment Analysis with Explanation from Large Language Models (2023.findings-emnlp)

Challenge:	Large language models (LLMs) have attracted considerable attention from academic and industrial communities due to their outstanding performance in various natural language processing tasks.
Approach:	They propose a Contrastive Learning Framework for Human Alignment to evaluate the noise within the data and dynamically adjust the training process.
Outcome:	The proposed framework surpasses other algorithms in terms of reward model scores, automatic evaluations, and human assessments on the widely used dataset "Helpful and Harmless"

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Qianlong Wang, Keyang Ding, Bin Liang, Min Yang, Ruifeng Xu

Reducing Spurious Correlations for Answer Selection by Feature Decorrelation and Language Debiasing (2022.coling-1)

Challenge:	Aspect-based sentiment analysis models are susceptible to learning spurious correlations between words . a recent study shows that feature engineering is time-consuming and costly .
Approach:	They propose to use a template to prompt LLMs to generate an appropriate explanation for the sentiment polarity of each aspect to reduce spurious correlations.
Outcome:	The proposed methods improve ABSA models and their generalization ability.

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Zeyi Zhong, Min Yang, Ruifeng Xu

Interpretable Proof Generation via Iterative Backward Reasoning (2022.naacl-main)

Challenge:	Existing deep neural models rely on spurious correlations between prediction labels and input features, which in general suffer from robustness and generalization.
Approach:	They propose a feature decorrelation module to remove feature dependencies and reduce spurious correlations by learning a weight for each instance at the training phase.
Outcome:	The proposed method improves the robustness of the neural ANswer selection models from the sample and feature perspectives.

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Hanhao Qu, Yu Cao, Jun Gao, Liang Ding, Ruifeng Xu

BERT-EMD: Many-to-Many Layer Mapping for BERT Compression with Earth Mover’s Distance (2020.emnlp-main)

Challenge:	Existing proof generation tasks require reasoning capabilities, but they usually just request for an answer without the reasoning procedure that would make it interpretable.
Approach:	They propose an iterative backward reasoning model to solve the proof generation tasks on rule-based Question Answering.
Outcome:	The proposed model improves in-domain performance and cross-domain transferability over existing models.

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Jianquan Li, Xiaokang Liu, Honghong Zhao, Ruifeng Xu, Min Yang, Yaohong Jin

Enhancing Noise Robustness of Retrieval-Augmented Language Models with Adaptive Adversarial Training (2024.acl-long)

Challenge:	Pre-trained language models have been proposed and applied to many NLP tasks, yielding state-of-the-art performance, but high storage and computational costs obstruct them to be effectively deployed on resource-constrained devices and real-time applications.
Approach:	They propose a BERT distillation method which allows each intermediate student layer to learn from any intermediate teacher layers.
Outcome:	The proposed method can learn from different teacher layers adaptively for different NLP tasks.

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Feiteng Fang, Yuelin Bai, Shiwen Ni, Min Yang, Xiaojun Chen, Ruifeng Xu

Multiple Knowledge-Enhanced Interactive Graph Network for Multimodal Conversational Emotion Recognition (2024.findings-emnlp)

Challenge:	Large language models (LLMs) exhibit substantial capabilities yet face challenges such as hallucination, outdated knowledge, and untraceable reasoning processes.
Approach:	They propose a retrieval-augmented generation approach that leverages adaptive adversarial training to dynamically adjust the model’s training process in response to retrieval noises.
Outcome:	The proposed approach improves the performance of the LLaMA-2 7B model under diverse noise conditions.

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Geng Tu, Jun Wang, Zhenyu Li, Shiwei Chen, Bin Liang, Xi Zeng, Min Yang, Ruifeng Xu

Beta Distribution Guided Aspect-aware Graph for Aspect Category Sentiment Analysis with Affective Knowledge (2021.emnlp-main)

Challenge:	Multimodal Emotion Recognition in Conversations models struggle due to lack of Common Sense Knowledge (CSK).
Approach:	They propose a multimodal approach to integrate multiple knowledge into the edge representations by integrating textual and visual CSK.
Outcome:	The proposed model outperforms state-of-the-art methods on two popular datasets.

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Bin Liang, Hang Su, Rongdi Yin, Lin Gui, Min Yang, Qin Zhao, Xiaoqi Yu, Ruifeng Xu

An Empirical Study of Sentiment-Enhanced Pre-Training for Aspect-Based Sentiment Analysis (2023.findings-acl)

Challenge:	Existing methods for aspect category sentiment analysis do not necessarily occur in a sentence.
Approach:	They propose a Beta Distribution-guided aspect-aware graph construction based on external knowledge . they use aspect-related words as the pivots to derive aspect-relevant weights .
Outcome:	The proposed approach outperforms the state-of-the-art methods on 6 benchmark datasets.

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Yice Zhang, Yifan Yang, Bin Liang, Shiwei Chen, Bing Qin, Ruifeng Xu

A Challenge Dataset and Effective Models for Aspect-Based Sentiment Analysis (D19-1)

Challenge:	Existing approaches to Aspect-Based Sentiment Analysis (ABSA) are lacking in a comprehensive evaluation and fair comparison.
Approach:	They propose to use a knowledge-mining method to build a large-scale knowledge-annotated SPT corpus and integrate sentiment knowledge into pre-training.
Outcome:	The proposed method is able to build a large-scale knowledge-annotated SPT corpus and compares with other methods.

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Qingnan Jiang, Lei Chen, Ruifeng Xu, Xiang Ao, Min Yang

A Multi-persona Framework for Argument Quality Assessment (2025.acl-long)

Challenge:	Existing ABSA methods only use one aspect or multiple aspects with the same sentiment polarity . recent studies show that neural network methods can be trained end-to-end and automatically learn important features.
Approach:	They propose a large-scale multi-aspect multi-sentiment dataset with two different aspects with different sentiment polarities.
Outcome:	The proposed model outperforms the state-of-the-art models on the large-scale dataset . it is based on a novel neural network approach that can be trained end-to-end .

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Bojun Jin, Jianzhu Bao, Yufang Hou, Yang Sun, Yice Zhang, Huajie Wang, Bin Liang, Ruifeng Xu

Challenge:	Existing methods for argument quality assessment do not consider multi-perspective evaluation due to subjective nature of arguments.
Approach:	They propose a multi-persona framework for argument quality assessment that simulates diverse evaluator perspectives through large language models.
Outcome:	The proposed framework outperforms baselines while providing comprehensive multi-perspective rationales on IBM-Rank-30k and IBM-ArgQ-5.3kArgs datasets.

Sparse Activation Editing for Reliable Instruction Following in Narratives (2025.emnlp-main)

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Runcong Zhao, Chengyu Cao, Qinglin Zhu, Xiucheng Ly, Shun Shao, Lin Gui, Ruifeng Xu, Yulan He

Challenge:	Existing benchmarks fail to capture the challenges of instruction following in complex narrative contexts.
Approach:	They propose a training-free framework that identifies and edits instruction-relevant neurons using only natural language instructions without requiring labelled data.
Outcome:	The proposed framework improves instruction following by identifying and editing instruction-relevant neurons using only natural language instructions, without requiring labelled data.

Self-Training with Pseudo-Label Scorer for Aspect Sentiment Quad Prediction (2024.acl-long)

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Yice Zhang, Jie Zeng, Weiming Hu, Ziyi Wang, Shiwei Chen, Ruifeng Xu

Have my arguments been replied to? Argument Pair Extraction as Machine Reading Comprehension (2022.acl-short)

Challenge:	Aspect Sentiment Quad Prediction (ASQP) aims to predict all quads (aspect term, aspect category, opinion term, sentiment polarity) for a given review.
Approach:	They propose a self-training framework with a pseudo-label scorer to assess the match between reviews and their pseudo-labels and train a generative model on it.
Outcome:	The proposed framework can predict all quads (aspect term, aspect category, opinion term, sentiment polarity) for a given review, and it can significantly improve self-training.

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Jianzhu Bao, Jingyi Sun, Qinglin Zhu, Ruifeng Xu

Amalgamating Knowledge from Two Teachers for Task-oriented Dialogue System with Adversarial Training (2020.emnlp-main)

Challenge:	Existing studies identify argument pairs indirectly by predicting sentence-level relations between two documents, neglecting the holistic argument-level interactions.
Approach:	They propose to use machine reading comprehension to extract argument pairs from two documents . they propose to employ an AM query to identify all arguments in two documents, then an APE query to extract its paired arguments from another document.
Outcome:	The proposed method outperforms the state-of-the-art method by 7.11% in F1 score.

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Wanwei He, Min Yang, Rui Yan, Chengming Li, Ying Shen, Ruifeng Xu

Transition-based Directed Graph Construction for Emotion-Cause Pair Extraction (2020.acl-main)

Challenge:	Existing approaches to task-oriented dialogue systems require a large number of handcrafted features and labels.
Approach:	They propose a "Two-Teacher One-Student" learning framework for task-oriented dialogue . the framework amalgamates knowledge from two teacher networks and provides guidance .
Outcome:	The proposed framework outperforms baseline methods on two benchmark datasets . it can retrieve accurate KB entities and generate human-like responses simultaneously .

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Chuang Fan, Chaofa Yuan, Jiachen Du, Lin Gui, Min Yang, Ruifeng Xu

PITA: Prompting Task Interaction for Argumentation Mining (2024.acl-long)

Challenge:	Existing methods to extract emotions and causes from unannotated text are pipelined, causing error propagation.
Approach:	They propose to transform a task into a procedure of parsing-like directed graph construction . they propose to generate a directed graph with labeled edges based on a sequence of actions .
Outcome:	The proposed method outperforms the state-of-the-art methods by 6.71% (p0.01) in F1 measure.

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Yang Sun, Muyi Wang, Jianzhu Bao, Bin Liang, Xiaoyan Zhao, Caihua Yang, Min Yang, Ruifeng Xu

Text Classification

Challenge:	Argumentation mining (AM) aims to detect arguments and their inherent relations from textual compositions.
Approach:	They propose a method to model the inter-relationships among three subtasks within a generative framework.
Outcome:	The proposed method achieves state-of-the-art performance on two AM benchmarks.

Learning from Sibling Mentions with Scalable Graph Inference in Fine-Grained Entity Typing (2022.acl-long)

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Yi Chen, Jiayang Cheng, Haiyun Jiang, Lemao Liu, Haisong Zhang, Shuming Shi, Ruifeng Xu

Jointly Learning Aspect-Focused and Inter-Aspect Relations with Graph Convolutional Networks for Aspect Sentiment Analysis (2020.coling-main)

Challenge:	Existing models struggle to handle hard mentions due to insufficient contexts, limiting their overall typing performance.
Approach:	They propose to exploit sibling mentions to enhance the mention representations by adding unseen test mentions as new nodes for inference.
Outcome:	The proposed model outperforms ten strong baseline models and outperformed strong baselines.

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Bin Liang, Rongdi Yin, Lin Gui, Jiachen Du, Ruifeng Xu

Exploring Quality and Diversity in Synthetic Data Generation for Argument Mining (2025.emnlp-main)

Challenge:	Existing methods for aspect sentiment analysis do not include explicit sentiment expressions.
Approach:	They propose to construct a heterogeneous graph by leveraging aspect-focused and inter-aspect contextual dependencies for the specific aspect.
Outcome:	The proposed model outperforms state-of-the-art methods on four benchmark datasets and significantly boosts performance in comparison with BERT.

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Jianzhu Bao, Yuqi Huang, Yang Sun, Wenya Wang, Yice Zhang, Bojun Jin, Ruifeng Xu

Error Comparison Optimization for Large Language Models on Aspect-Based Sentiment Analysis (2025.acl-long)

Challenge:	Argument Mining (AM) is hindered by the scarcity of structure-annotated datasets, which are expensive to create manually.
Approach:	They propose to use quality-oriented synthesis and diversity-oriented approach to generate argumentative texts with diverse topics and argument structures.
Outcome:	The proposed approach significantly improves existing models in full-data and low-resource settings.

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Qianlong Wang, Keyang Ding, Hengxin Gao, Hui Wang, Ruifeng Xu

SEMGraph: Incorporating Sentiment Knowledge and Eye Movement into Graph Model for Sentiment Analysis (2022.emnlp-main)

Challenge:	Existing methods for aspect-based sentiment analysis (ABSA) only compare current predictions and labels on each sample, yet fail to perceive and understand its error outputs from different degrees.
Approach:	They propose a framework that can perceive and understand the degree of errors by learning from comparative error pairs.
Outcome:	The proposed framework exceeds baselines and achieves the desired performance.

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Bingbing Wang, Bin Liang, Jiachen Du, Min Yang, Ruifeng Xu

Context-aware Embedding for Targeted Aspect-based Sentiment Analysis (P19-1)

Challenge:	Existing research on sentiment analysis based on eye movement signals has been attributed importance.
Approach:	They propose a linguistic probing eye movement paradigm to extract eye movement features based on the relationship between linguistic features and human reading behavior.
Outcome:	The proposed graph architecture achieves state-of-the-art performance on two sentiment analysis datasets with eye movement signals and three sentiment analysis data without eye movement signal.

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Bin Liang, Jiachen Du, Ruifeng Xu, Binyang Li, Hejiao Huang

Representation Learning

Challenge:	Existing methods do not specifically pre-train reasonable embeddings for targets and aspects in TABSA.
Approach:	They propose to refine the embeddings of targets and aspects using a sparse coefficient vector . this allows the embeds to be refined from highly correlative words instead of context-independent vectors .
Outcome:	Experiments show that the proposed method improves on two benchmark datasets.

Layer-wise Regularized Dropout for Neural Language Models (2024.lrec-main)

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Shiwen Ni, Min Yang, Ruifeng Xu, Chengming Li, Xiping Xiping Hu

BC-Prover: Backward Chaining Prover for Formal Theorem Proving (2024.emnlp-main)

Challenge:	Existing methods to regularize dropout are consistency training and dropout is a problem in many pre-trained neural language models.
Approach:	They propose a layer-wise regularized dropout technique which regularizes dropout at the output layer using consistency training.
Outcome:	The proposed model can be regarded as a "self-distillation" framework, in which each sub-model generated by dropout is the other's "teacher" model and "student" model.

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Yuhang He, Jihai Zhang, Jianzhu Bao, Fangquan Lin, Cheng Yang, Bing Qin, Ruifeng Xu, Wotao Yin

A Generative Model for End-to-End Argument Mining with Reconstructed Positional Encoding and Constrained Pointer Mechanism (2022.emnlp-main)

Challenge:	Existing methods for interactive theorem proving in formal logic lack robustness and robustness.
Approach:	They propose a backward chaining framework guided by pseudo steps for proofstep generation that prioritizes pseudo steps.
Outcome:	The proposed framework improves on the miniF2F benchmark.

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Jianzhu Bao, Yuhang He, Yang Sun, Bin Liang, Jiachen Du, Bing Qin, Min Yang, Ruifeng Xu

Argument Pair Extraction with Mutual Guidance and Inter-sentence Relation Graph (2021.emnlp-main)

Challenge:	Argument mining (AM) is a challenging task as it requires recognizing complex argumentation structures involving multiple subtasks.
Approach:	They propose a generative framework where expected outputs of AM are framed as a simple target sequence.
Outcome:	The proposed framework achieves state-of-the-art on two AM benchmarks.

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Jianzhu Bao, Bin Liang, Jingyi Sun, Yice Zhang, Min Yang, Ruifeng Xu

Cue-CoT: Chain-of-thought Prompting for Responding to In-depth Dialogue Questions with LLMs (2023.findings-emnlp)

Challenge:	Existing studies on argumentation mining focus on monological argumentation and dialogical argumentation.
Approach:	They propose a mutual guidance framework that could guide arguments in one passage . they propose an inter-sentence relation graph to effectively model the inter-relations between two sentences .
Outcome:	The proposed method outperforms the current state-of-the-art model.

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Hongru Wang, Rui Wang, Fei Mi, Yang Deng, Zezhong Wang, Bin Liang, Ruifeng Xu, Kam-Fai Wong

A Diffusion Model for Event Skeleton Generation (2023.findings-acl)

Challenge:	Existing LLMs generate responses based on the dialogue context, overlooking the underlying linguistic cues about the user status exhibited in the context.
Approach:	They propose a linguistic cue-based chain-of-thoughts method which enhances the LLMs inference with an intermediate reasoning step to find cues exhibited in the dialogue.
Outcome:	The proposed method outperforms standard prompting methods on in-depth dialogue questions and linguistic cues exhibited in the context.

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Fangqi Zhu, Lin Zhang, Jun Gao, Bing Qin, Ruifeng Xu, Haiqin Yang

MCPG: A Flexible Multi-Level Controllable Framework for Unsupervised Paraphrase Generation (2022.findings-emnlp)

Challenge:	Existing methods for event schema generation are noise-sensitive and error-accumulating, e.g., inability to correct errors while generating schema.
Approach:	They propose a novel diffusion event graph model that embeds and roundes event graphs into learnable latent representations and a denoising process to maintain the model's robustness.
Outcome:	The proposed model achieves better results than existing state-of-the-art models on three IED bombing datasets.

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Yi Chen, Haiyun Jiang, Lemao Liu, Rui Wang, Shuming Shi, Ruifeng Xu

Text Generation

Challenge:	Existing studies on controllable unsupervised paraphrase generation are expensive and require supervised training on large parallel corpora.
Approach:	They propose a method for controllable unsupervised paraphrase generation that is flexible to adapt to specific domains without extra training.
Outcome:	The proposed method outperforms state-of-the-art unsupervised baselines by a margin.

Forgetting before Learning: Utilizing Parametric Arithmetic for Knowledge Updating in Large Language Models (2024.acl-long)

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Shiwen Ni, Dingwei Chen, Chengming Li, Xiping Hu, Ruifeng Xu, Min Yang

Learning to Describe for Predicting Zero-shot Drug-Drug Interactions (2023.emnlp-main)

Challenge:	Existing methods of model editing and knowledge updating add additional network parameters, knowledge bases, knowledge base, and model parameters.
Approach:	They propose a new paradigm for fine-tuning called F-Learning that employs parametric arithmetic to facilitate the forgetting of old knowledge and learning of new knowledge.
Outcome:	The proposed model outperforms existing models on two datasets and is comparable to full fine-tuning and LoRA fine-uning.

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Fangqi Zhu, Yongqi Zhang, Lei Chen, Bing Qin, Ruifeng Xu

Improving Empathetic Response Generation by Recognizing Emotion Cause in Conversations (2021.findings-emnlp)

Challenge:	Existing computational methods for DDI prediction fail to capture interactions for new drugs due to the lack of knowledge.
Approach:	They propose a problem setup as zero-shot DDI prediction that deals with the case of new drugs by using textual information from online databases.
Outcome:	The proposed method improves on several settings including zero-shot and few-shot DDI prediction and the selected texts are semantically relevant.

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Jun Gao, Yuhan Liu, Haolin Deng, Wei Wang, Yu Cao, Jiachen Du, Ruifeng Xu

COPR: Continual Human Preference Learning via Optimal Policy Regularization (2025.findings-acl)

Challenge:	Existing approaches to empathetic response generation ignore the emotion cause . existing dialogue systems lack emotion understanding and empathy .
Approach:	They propose a framework that integrates emotion cause information into empathetic response generation by predicting context emotion labels and sequence of emotion cause-oriented labels.
Outcome:	The proposed framework improves empathetic response generation by incorporating emotion cause information into the model.

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Han Zhang, Lin Gui, Yu Lei, Yuanzhao Zhai, Yehong Zhang, Zhuo Zhang, Yulan He, Hui Wang, Yue Yu, Kam-Fai Wong, Bin Liang, Ruifeng Xu

Focal Training and Tagger Decouple for Grammatical Error Correction (2023.findings-acl)

Challenge:	Reinforcement Learning from Human Feedback (RLHF) is effective for aligning Large Language Models with human preferences, but its complex process limits its ability to continually learn human feedback.
Approach:	They propose a non-RL offline method to convert historical optimal policies into optimization constraints when continually learning new preferences.
Outcome:	The proposed method outperforms strong CL baselines in terms of reward-based evaluations and human assessment.

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Minghuan Tan, Min Yang, Ruifeng Xu

Syntactic Text Processing

Challenge:	Existing methods for Grammatical Error Correction (GEC) are categorized into sequence-to-sequence approaches, tagging-based approaches, and hybrid approaches.
Approach:	They propose to decouple error detection layer from label tagging layer and to down-weight label imbalance and tabbing entanglement loss using Focal Loss.
Outcome:	The proposed methods are effective over three latest Chinese Grammatical Error Correction datasets.

Boundary-Driven Table-Filling for Aspect Sentiment Triplet Extraction (2022.emnlp-main)

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Yice Zhang, Yifan Yang, Yihui Li, Bin Liang, Shiwei Chen, Yixue Dang, Min Yang, Ruifeng Xu

An Empirical Study on Multiple Information Sources for Zero-Shot Fine-Grained Entity Typing (2021.emnlp-main)

Challenge:	Existing work focuses on extracting aspect terms and opinion terms without considering the relations between aspect terms .
Approach:	They propose a task to extract aspect terms, opinion terms, and expressed sentiments from a two-dimensional (2D) table.
Outcome:	The proposed method achieves state-of-the-art on several public benchmarks and is well-suited to the ASTE task.

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Yi Chen, Haiyun Jiang, Lemao Liu, Shuming Shi, Chuang Fan, Min Yang, Ruifeng Xu

A Training-Free Debiasing Framework with Counterfactual Reasoning for Conversational Emotion Detection (2023.emnlp-main)

Challenge:	Auxiliary information from multiple sources has been demonstrated to be effective in zero-shot fine-grained entity typing (ZFET) however, there is no comprehensive understanding of how to make better use of the existing information sources and how they affect the performance of ZFET.
Approach:	They propose a multi-source fusion model targeting auxiliary information from multiple sources to improve zero-shot fine-grained entity typing (ZFET)
Outcome:	The proposed model achieves 11.42% and 22.84% gains over state-of-the-art baselines on BBN and Wiki respectively with regard to macro F1 scores.

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Geng Tu, Ran Jing, Bin Liang, Min Yang, Kam-Fai Wong, Ruifeng Xu

Enhancing Large Language Models Against Inductive Instructions with Dual-critique Prompting (2024.naacl-long)

Challenge:	Existing studies in Emotion Recognition in Conversations (ERC) focus on capturing context-sensitive and speaker-sensitive dependencies, ignoring the unintended dataset biases of data.
Approach:	They propose a training-free debiasing framework that extracts biases from the model by generating counterfactual utterances and contexts and mitigates them using simple yet empirically robust element-wise subtraction operations.
Outcome:	Experiments on three public datasets show that the proposed framework improves generalization ability and fairness across different ERC models.

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Rui Wang, Hongru Wang, Fei Mi, Boyang Xue, Yi Chen, Kam-Fai Wong, Ruifeng Xu

PiKGL: Leveraging Pruned Knowledge Graphs for Explainable Stance Detection (2026.tacl-1)

Challenge:	Existing studies have focused on how LLMs handle inductive instructions, which may stem from users’ false beliefs or malicious intents.
Approach:	They propose a benchmark of Inductive Instructions where false knowledge is incorporated into instructions in multiple different styles.
Outcome:	The proposed model improves robustness against inductive instructions, despite different inductive styles and complexity.

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Bingbing Wang, Jingjie Lin, Zhixin Bai, Xintong Song, Qianlong Wang, Min Yang, Xi Zeng, Jing Li, Ruifeng Xu

MIND Your Reasoning: A Meta-Cognitive Intuitive-Reflective Network for Dual-Reasoning in Multimodal Stance Detection (2026.acl-long)

Challenge:	Experimental results demonstrate that a Pruned interpretable knowledge Graph Learning framework for explainable stance detection is state-of-the-art for social media stance prediction.
Approach:	They propose a Pruned interpretable knowledge Graph Learning framework for explainable stance detection that incorporates commonsense knowledge and prunes redundant information to ensure precision and minimize noise.
Outcome:	The proposed framework achieves state-of-the-art on three public datasets.

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Bingbing Wang, Zhengda Jin, Bin Liang, Wenjie Li, Jing Li, Ruifeng Xu, Min Zhang

Learning First-Order Logic Rules for Argumentation Mining (2025.acl-long)

Challenge:	Existing methods operate by learning to fuse modalities, leading to frequent misjudgments.
Approach:	They propose a paradigm shift from learning to fuse to *learning the reason's process' inspired by the dual-process theory of human cognition, MIND operationalizes a self-improving loop.
Outcome:	The proposed model significantly outperforms baseline models and exhibits strong generalization.

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Yang Sun, Guanrong Chen, Hamid Alinejad-Rokny, Jianzhu Bao, Yuqi Huang, Bin Liang, Kam-Fai Wong, Min Yang, Ruifeng Xu

Continual Learning for Task-oriented Dialogue System with Iterative Network Pruning, Expanding and Masking (2021.acl-short)

Challenge:	Argumentation Mining (AM) aims to extract argumentative structures from texts by identifying argumentation components (ACs) and their argumentative relations (ARs).
Approach:	They propose a First- Order Logic reasoning framework for AM to capture logical reasoning paths within argumentative texts.
Outcome:	The proposed framework outperforms strong baselines while significantly improving explainability.

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Binzong Geng, Fajie Yuan, Qiancheng Xu, Ying Shen, Ruifeng Xu, Min Yang

Masked Language Models Know Which are Popular: A Simple Ranking Strategy for Commonsense Question Answering (2022.findings-emnlp)

Challenge:	Existing methods to learn consecutive tasks without forgetting how to perform previously trained problems are lacking.
Approach:	They propose a continual learning method which preserves performance on previously encountered tasks while accelerating learning progress on subsequent tasks.
Outcome:	The proposed method preserves performance on previously encountered tasks while accelerating learning progress on subsequent tasks.

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Xuan Luo, Chuang Fan, Yice Zhang, Wanguo Jiang, Bing Qin, Ruifeng Xu

AEQ-Bench: Measuring Empathy of Omni-Modal Large Models (2026.findings-acl)

Challenge:	Empirical results show that pre-trained language models can improve the typical answer generation of GLMs.
Approach:	They propose a ranking strategy that exploits WordNet to train a ranker that picks out the most popular answers for commonsense questions.
Outcome:	The proposed ranking strategy is tested on a commonsense question answering (QA) dataset and on negative samples from WordNet.

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Xuan Luo, Lewei Yao, Lanqing Hong, Kai Chen, Dehua Tao, Daxin Tan, Yukun Deng, Ruifeng Xu, Jing Li

Hybrid Neural Attention for Agreement/Disagreement Inference in Online Debates (D18-1)

Challenge:	Existing benchmarks focus on cognitive abilities, such as knowledge retrieval, complex reasoning, and instruction following, largely overlooking empathy evaluation.
Approach:	They propose to benchmark two core empathetic capabilities of omnimodal large models (OLMs) generating empatries by comprehending affective cues from multi-modal inputs and judging empathy of audio responses without relying on text transcription.
Outcome:	The proposed benchmark outperforms existing models with audio output capabilities but is unreliable for evaluating fine-grained paralinguistic expressiveness.

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Di Chen, Jiachen Du, Lidong Bing, Ruifeng Xu

Decomposing Argumentative Essay Generation via Dialectical Planning of Complex Reasoning (2024.findings-acl)

Challenge:	Existing models for agreement/disagreement in debates lack the ability to model these two factors together.
Approach:	They propose a hybrid attention model which combines self and cross attention mechanism to locate salient part from textual context and interaction between users.
Outcome:	The proposed model outperforms the state-of-the-art models on three (dis)agreement inference datasets.

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Yuhang He, Jianzhu Bao, Yang Sun, Bin Liang, Min Yang, Bing Qin, Ruifeng Xu

Dual Dynamic Memory Network for End-to-End Multi-turn Task-oriented Dialog Systems (2020.coling-main)

Challenge:	Argumentative Essay Generation (AEG) is a challenging task in computational argumentation, where detailed logical reasoning and effective rhetorical skills are essential.
Approach:	They propose an argumentative planning strategy for prompting large language models to generate high-quality essays by sketch planning and dialectical planning.
Outcome:	The proposed method generates more dialectical and persuasive essays with higher diversity compared to baselines.

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Jian Wang, Junhao Liu, Wei Bi, Xiaojiang Liu, Kejing He, Ruifeng Xu, Min Yang

TP-Link: Fine-grained Pre-Training for Text-to-SQL Parsing with Linking Information (2024.lrec-main)

Challenge:	Existing task-oriented dialog systems struggle to dynamically model long dialog context for interactions and effectively incorporate knowledge base (KB) information into dialog generation.
Approach:	They propose a dual dynamic memory network for multi-turn dialog generation . the model dynamically expands the dialog memory turn by turn and keeps track of dialog history .
Outcome:	The proposed model outperforms baseline models on three benchmark datasets on human evaluation and automatic evaluation.

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Ziqiang Liu, Shujie Li, Zefeng Cai, Xiangyu Li, Yunshui Li, Chengming Li, Xiping Hu, Ruifeng Xu, Min Yang

Programming Languages in NLP

Challenge:	Existing pre-training frameworks for text-to-SQL parsing have shown inherent differences in distributions between tables and plain text.
Approach:	They propose a framework to improve context-dependent Text-to-SQL parsing by leveraging Linking information.
Outcome:	The proposed framework achieves state-of-the-art performance on two leading downstream benchmarks.

Context or Knowledge is Not Always Necessary: A Contrastive Learning Framework for Emotion Recognition in Conversations (2023.findings-acl)

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Geng Tu, Bin Liang, Ruibin Mao, Min Yang, Ruifeng Xu

MMSD2.0: Towards a Reliable Multi-modal Sarcasm Detection System (2023.findings-acl)

Challenge:	Existing studies focus on modeling context-sensitive dependencies and knowledge-sensitive dependences.
Approach:	They propose a framework based on contrastive learning called CKCL to distinguish utterances for better vector representations.
Outcome:	The proposed framework outperforms state-of-the-art models on four datasets.

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Libo Qin, Shijue Huang, Qiguang Chen, Chenran Cai, Yudi Zhang, Bin Liang, Wanxiang Che, Ruifeng Xu

The UIR Uncertainty Corpus for Chinese: Annotating Chinese Microblog Corpus for Uncertainty Identification from Social Media (L18-1)

Challenge:	Existing benchmarks for multi-modal sarcasm detection have some shortcomings . a new framework can leverage multi-grained cues from multiple perspectives for multimodal detection .
Approach:	They propose a correction dataset that removes spurious cues and re-annotates the unreasonable samples.
Outcome:	The proposed framework outperforms the existing benchmarks in multi-modal sarcasm detection.

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Binyang Li, Jun Xiang, Le Chen, Xu Han, Xiaoyan Yu, Ruifeng Xu, Tengjiao Wang, Kam-fai Wong

Challenge:	Uncertainty identification is an important semantic processing task, critical to the quality of information in terms of factuality in many NLP techniques and applications.
Approach:	They propose to annotate Chinese microblogs with an open uncertainty corpus . they propose to use contextual uncertain semantics rather than traditional cue-phrases to identify uncertainty .
Outcome:	The proposed corpus can be used to identify uncertainty in social media texts.

Large Language Models as Reader for Bias Detection (2025.findings-emnlp)

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Xuan Luo, Jing Li, Zhong Wenzhong, Geng Tu, Ruifeng Xu

NUMCoT: Numerals and Units of Measurement in Chain-of-Thought Reasoning using Large Language Models (2024.findings-acl)

Challenge:	Traditional methods analyze text from the writer’s perspective, leaving the reader’s viewpoint underexplored.
Approach:	They investigate whether large language models can be leveraged as readers for bias detection by generating reader-perspective comments.
Outcome:	The proposed model performs comparable to GPT4's in detecting bias in media content.

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Ancheng Xu, Minghuan Tan, Lei Wang, Min Yang, Ruifeng Xu

Set Learning for Generative Information Extraction (2023.emnlp-main)

Challenge:	Existing LLMs are not able to handle numerals and units of measurement, but they can be improved by introducing perturbations.
Approach:	They propose to analyze existing LLMs on processing numerals and units of measurement by perturbing their datasets.
Outcome:	The proposed model improves on ancient Chinese arithmetic problems and can handle numeral and measurement conversions.

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Jiangnan Li, Yice Zhang, Bin Liang, Kam-Fai Wong, Ruifeng Xu

E-EVAL: A Comprehensive Chinese K-12 Education Evaluation Benchmark for Large Language Models (2024.findings-acl)

Challenge:	Recent efforts to employ sequence-to-sequence models to solve IE tasks have been focused on a single problem: structured objects are an unordered set, resulting in a potential order bias.
Approach:	They propose a sequence-to-sequence (Seq2Sequen) model that considers multiple permutations of structured objects to optimize set probability approximately.
Outcome:	The proposed model improves existing frameworks on vast tasks and datasets.

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Jinchang Hou, Chang Ao, Haihong Wu, Xiangtao Kong, Zhigang Zheng, Daijia Tang, Chengming Li, Xiping Hu, Ruifeng Xu, Shiwen Ni, Min Yang

ArgGenBench: Benchmarking the Complex Controlled Argument Generation Capability of Large Language Models (2026.acl-long)

Challenge:	despite the rapid development of Large Language Models, there is no dedicated benchmark for evaluating LLMs in Chinese K-12 education.
Approach:	They propose to develop a benchmark specifically tailored for Chinese K-12 education.
Outcome:	EVAL is the first evaluation benchmark specifically tailored for Chinese K-12 education.

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Bojun Jin, Jianzhu Bao, Yang Sun, Yice Zhang, Ruifeng Xu

Targeted Distillation for Sentiment Analysis (2025.emnlp-main)

Challenge:	Existing studies focus on limited control signals such as topic, stance, length, style, strategy, audience, and key aspects, failing to capture this complexity.
Approach:	They propose a benchmark that integrates multi-dimensional control into a single instruction to evaluate LLMs' ability to produce persuasive arguments.
Outcome:	The proposed benchmarks show that existing models fail to capture multifaceted argumentative control signals.

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Yice Zhang, Guangyu Xie, Jingjie Lin, Jianzhu Bao, Qianlong Wang, Xi Zeng, Ruifeng Xu

Green & Sustainable NLP

Challenge:	Recent studies demonstrate that large language models exhibit remarkable capabilities and achieve state-of-the-art performance in diverse sentiment analysis tasks.
Approach:	They propose a distillation framework that decouples knowledge from alignment and introduces a sentiment analysis benchmark that covers a diverse set of tasks.
Outcome:	The proposed framework improves models' generalization to unseen tasks and their generalization is strong against existing small-scale models.

Discourse Structure-Aware Prefix for Generation-Based End-to-End Argumentation Mining (2024.findings-acl)

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Yang Sun, Guanrong Chen, Caihua Yang, Jianzhu Bao, Bin Liang, Xi Zeng, Min Yang, Ruifeng Xu

Neural Topic Model with Reinforcement Learning (D19-1)

Challenge:	Recent advances in AM models overlook the integration of supplementary discourse structure information, resulting in suboptimal outcomes.
Approach:	They propose a framework which generates discourse structure-aware prefixes for each layer of the generation model.
Outcome:	The proposed framework achieves state-of-the-art performance on two AM benchmarks.

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Lin Gui, Jia Leng, Gabriele Pergola, Yu Zhou, Ruifeng Xu, Yulan He

Probing Structural Knowledge from Pre-trained Language Model for Argumentation Relation Classification (2022.findings-emnlp)

Challenge:	Experimental results show superior performance on perplexity and topic coherence measures compared to state-of-the-art topic models.
Approach:	They propose to incorporate topic coherence measures as reward signals to guide the learning of a VAE-based topic model.
Outcome:	The proposed model is able to separating background words dynamically from topic words eliminating the pre-processing step of filtering infrequent and/or top frequent words, typically required for learning traditional topic models.

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Yang Sun, Bin Liang, Jianzhu Bao, Min Yang, Ruifeng Xu

A Knowledge Regularized Hierarchical Approach for Emotion Cause Analysis (D19-1)

Challenge:	Argumentation relation classification (ARC) is the most challenging subtask of argumentation mining.
Approach:	They propose a dual prior graph neural network to explore probing knowledge and syntactical information for comprehensively modeling the relationship between AC pairs.
Outcome:	The proposed model outperforms the state-of-the-art models on three public datasets.

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Chuang Fan, Hongyu Yan, Jiachen Du, Lin Gui, Lidong Bing, Min Yang, Ruifeng Xu, Ruibin Mao

Progressive Self-Training with Discriminator for Aspect Term Extraction (2021.emnlp-main)

Challenge:	Emotion cause analysis aims to identify the reasons behind emotions . previous models focus on learning architecture with local textual information .
Approach:	They propose a method to extract emotion cause with hierarchical neural model and knowledge-based regularizations by sentiment lexicon and common knowledge.
Outcome:	The proposed method outperforms baselines on two public datasets in different languages and outperformed competitive baselines by 2.08%.

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Qianlong Wang, Zhiyuan Wen, Qin Zhao, Min Yang, Ruifeng Xu

Probing Graph Decomposition for Argument Pair Extraction (2023.findings-acl)

Challenge:	Existing approaches to extract aspect terms from review sentences are limited due to lack of annotated data.
Approach:	They propose to refine conventional self-training to progressive self-teaching to reduce noise . they use a discriminator to filter the noisy pseudo-labels.
Outcome:	The proposed model outperforms baseline models and achieves state-of-the-art performance on four SemEval datasets.

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Yang Sun, Bin Liang, Jianzhu Bao, Yice Zhang, Geng Tu, Min Yang, Ruifeng Xu

A Simple and Efficient Learning-Style Prompting for LLM Jailbreaking (2026.findings-eacl)

Challenge:	Argument pair extraction (APE) aims to extract interactive argument pairs from two passages within a discussion.
Approach:	They propose a method to extract interactive argument pairs from two passages . they propose to decompose the probing graph into four sub-graphs based on inter- and intra-passage perspectives .
Outcome:	The proposed method improves on strong baselines on two benchmark datasets.

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Xuan Luo, Yue Wang, Zefeng He, Geng Tu, Jing Li, Ruifeng Xu

Flexibly Utilize Memory for Long-Term Conversation via a Fragment-then-Compose Framework (2025.emnlp-main)

Challenge:	Learning-style queries can reliably elicit harmful responses, highlighting a critical safety blind spot in modern LLMs.
Approach:	They propose a new reframing paradigm that hides intention by learning from LLMs and uses 4 conceptual components to construct learning-style queries.
Outcome:	The proposed framework achieves top attack success rates on most models and across malicious categories while maintaining high efficiency with concise prompts.

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Cai Ke, Yiming Du, Bin Liang, Yifan Xiang, Lin Gui, Zhongyang Li, Baojun Wang, Yue Yu, Hui Wang, Kam-Fai Wong, Ruifeng Xu

OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models (2025.acl-long)

Challenge:	Large language models extract useful information from conversation history to enhance the response in long-term conversations.
Approach:	They propose a Fragment-then-Compose framework to optimize memory utilization for long-term open-domain conversation.
Outcome:	The proposed framework can be used to extract useful information from conversation history . it can be adapted to different situations and improve response generation .

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Siming Huang, Tianhao Cheng, Jason Klein Liu, Weidi Xu, Jiaran Hao, Liuyihan Song, Yang Xu, Jian Yang, Jiaheng Liu, Chenchen Zhang, Linzheng Chai, Ruifeng Yuan, Xianzhen Luo, Qiufeng Wang, YuanTao Fan, Qingfu Zhu, Zhaoxiang Zhang, Yang Gao, Jie Fu, Qian Liu, Houyi Li, Ge Zhang, Yuan Qi, Xu Yinghui, Wei Chu, Zili Wang

In-Context Example Retrieval from Multi-Perspectives for Few-Shot Aspect-Based Sentiment Analysis (2024.lrec-main)

Challenge:	Code LLMs lack reproducible data pipelines and training protocols for reproducible advancements in code intelligence.
Approach:	They propose a top-tier code LLM that releases model weights and inference code . reproducible data pipelines, rigorous experimental ablation results and training protocols are included .
Outcome:	The proposed model achieves comparable performance to leading models and serves as an "open cookbook" reproducible training data, rigorous experimental ablation results, and detailed training protocols are also included in the model.

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Qianlong Wang, Hongling Xu, Keyang Ding, Bin Liang, Ruifeng Xu

In-context Learning for Few-shot Multimodal Named Entity Recognition (2023.findings-emnlp)

Challenge:	Existing approaches to solve few-shot aspect-based sentiment analysis (ABSA) are suboptimal for this task because of in-context examples .
Approach:	They propose to retrieve in-context examples for few-shot aspect-based sentiment analysis . they construct positive and negative pairs from three perspectives and train the retriever .
Outcome:	The proposed retrieval framework outperforms baselines on four ABSA datasets.

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Chenran Cai, Qianlong Wang, Bin Liang, Bing Qin, Min Yang, Kam-Fai Wong, Ruifeng Xu

A Synthetic Data Generation Framework for Grounded Dialogues (2023.acl-long)

Challenge:	Existing methods for named entity recognition are time-consuming and laborintensive.
Approach:	They propose a few-shot multimodal named entity recognition task that uses few examples to locate and identify named entities for a text-image pair.
Outcome:	The proposed framework outperforms baselines under several few-shot settings.

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Jianzhu Bao, Rui Wang, Yasheng Wang, Aixin Sun, Yitong Li, Fei Mi, Ruifeng Xu

An Empirical Study on Multiple Knowledge from ChatGPT for Emotion Recognition in Conversations (2023.findings-emnlp)

Challenge:	Existing approaches to train grounded dialogues require large amounts of data.
Approach:	They propose a synthetic data generation framework for grounded dialogues that takes knowledge data and heuristics to determine a dialogue flow and incrementally turn it into a dialog.
Outcome:	The proposed framework significantly boosts model performance in training data and low-resource scenarios.

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Geng Tu, Bin Liang, Bing Qin, Kam-Fai Wong, Ruifeng Xu

CoreEval: Automatically Building Contamination-Resilient Datasets with Real-World Knowledge toward Reliable LLM Evaluation (2025.acl-long)

Challenge:	Existing efforts in ERC focus on context- and speaker-sensitive dependencies, but lack of annotated data and high cost of obtaining such knowledge is a blank slate.
Approach:	They propose a Multiple Knowledge Fusion Model to integrate multiple knowledge generated by Large Language Models (LLMs) they analyze the contribution and complementarity of this knowledge into the model.
Outcome:	The proposed model integrates multiple knowledge generated by LLMs and analyzes its contribution and complementarity on three public datasets.

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Jingqian Zhao, Bingbing Wang, Geng Tu, Yice Zhang, Qianlong Wang, Bin Liang, Jing Li, Ruifeng Xu

Variational Autoregressive Decoder for Neural Response Generation (D18-1)

Challenge:	Publicly available datasets can be used to evaluate performance of large language models . however, contamination of test data can artificially inflate model performance .
Approach:	They propose a Contamination-resilient Evaluation strategy that updates data with real-world knowledge.
Outcome:	The proposed evaluation strategy can be used to update datasets with real-world knowledge.

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Jiachen Du, Wenjie Li, Yulan He, Ruifeng Xu, Lidong Bing, Xuan Wang

Text Generation

Challenge:	Existing variational Bayesian models generate responses from a single latent variable, which is not sufficient to model high variability in responses.
Approach:	They propose a conditional variable auto-encoder that sequentially introduces latent variables to condition the generation of each word in the response sequence.
Outcome:	Empirical results show that the proposed model improves on state-of-the-art models on Opensubtitle and Reddit datasets.

Modeling Intra- and Inter-Modal Relations: Hierarchical Graph Contrastive Learning for Multimodal Sentiment Analysis (2022.coling-1)

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Zijie Lin, Bin Liang, Yunfei Long, Yixue Dang, Min Yang, Min Zhang, Ruifeng Xu

SOAPTriage: SOAP-Guided Multi-View Clinical Text Modeling Framework for Automated ESI Prediction (2026.acl-long)

Challenge:	Existing studies in Multimodal Sentiment Analysis lack a mechanism to understand complex relations between different modalities.
Approach:	They propose a hierarchical graph contrastive learning framework for multimodal sentiment analysis that explores the relationships between modality representations.
Outcome:	The proposed framework outperforms the state-of-the-art in multimodal sentiment analysis on two benchmark datasets.

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Enming Wang, Jianlei Wang, Xueping Peng, Hongjiao Guan, Yinglong Wang, Sibo Wei, Jianbin Guo, Ruifeng Xu, Wenpeng Lu

Planning, Creation, Usage: Benchmarking LLMs for Comprehensive Tool Utilization in Real-World Complex Scenarios (2024.findings-acl)

Challenge:	Emergency departments rely on the Emergency Severity Index (ESI) to assess patient acuity and prioritize care.
Approach:	They propose a SOAP-guided multi-view clinical text modeling framework for automated ESI prediction based on the SOAP paradigm .
Outcome:	The proposed framework outperforms prompting-based, multi-agent, and encoder-based baselines.

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Shijue Huang, Wanjun Zhong, Jianqiao Lu, Qi Zhu, Jiahui Gao, Weiwen Liu, Yutai Hou, Xingshan Zeng, Yasheng Wang, Lifeng Shang, Xin Jiang, Ruifeng Xu, Qun Liu

CLLE: A Benchmark for Continual Language Learning Evaluation in Multilingual Machine Translation (2022.findings-emnlp)

Challenge:	Existing benchmarks focus on simple synthesized queries that do not reflect real-world complexity, thereby offering limited perspectives in evaluating tool utilization.
Approach:	They propose a benchmark to evaluate LLMs’ ability in tool utilization within real-world scenarios.
Outcome:	The proposed benchmark improves LLMs’ ability in tool utilization within real-world scenarios and eliminates the restriction of pre-defined toolset.

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Han Zhang, Sheng Zhang, Yang Xiang, Bin Liang, Jinsong Su, Zhongjian Miao, Hui Wang, Ruifeng Xu

Multilinguality

Challenge:	Existing benchmarks for Continual Language Learning (CLL) are limited due to the complexity of the task and the lack of unified benchmarks.
Approach:	They propose a Continual Language Learning Evaluation benchmark CLLE in multilingual translation.
Outcome:	The proposed method is effective when compared with other strong benchmarks.

MoZIP: A Multilingual Benchmark to Evaluate Large Language Models in Intellectual Property (2024.lrec-main)

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Shiwen Ni, Minghuan Tan, Yuelin Bai, Fuqiang Niu, Min Yang, Bowen Zhang, Ruifeng Xu, Xiaojun Chen, Chengming Li, Xiping Hu

Stance Detection on Social Media with Background Knowledge (2023.emnlp-main)

Challenge:	Large language models (LLMs) have demonstrated impressive performance in various natural language processing tasks.
Approach:	They propose a benchmark for the evaluation of large language models in the IP domain . they also propose supervised multilingual large language model called MoZi .
Outcome:	The proposed model outperforms four well-known LLMs on the MoZIP benchmark . the most powerful ChatGPT does not reach the passing level .

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Ang Li, Bin Liang, Jingqian Zhao, Bowen Zhang, Min Yang, Ruifeng Xu

Retrieval-free Knowledge Injection through Multi-Document Traversal for Dialogue Models (2023.acl-long)

Challenge:	Existing studies of stance detection focus on learning stance information about specific targets from context, but in real-world scenarios, we usually have a certain understanding of a target when we express our stance on it.
Approach:	They propose to take the background knowledge of the target into account for better stance detection by categorizing it into episodic and discourse knowledge categories and a heuristic retrieval algorithm based on the topic to retrieve the Wikipedia documents relevant to the sample.
Outcome:	The proposed framework achieves state-of-the-art on four benchmark datasets showing that the proposed framework is able to detect stances in-target and zero-shot scenarios.

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Rui Wang, Jianzhu Bao, Fei Mi, Yi Chen, Hongru Wang, Yasheng Wang, Yitong Li, Lifeng Shang, Kam-Fai Wong, Ruifeng Xu

Emotion-Cause Pair Extraction as Sequence Labeling Based on A Novel Tagging Scheme (2020.emnlp-main)

Challenge:	Existing research on retrieval-augmented and retrieval free dialogue models focuses on retrieving knowledge from external sources and rely on finely annotated retrieval training data and knowledge-grounded responses.
Approach:	They propose a retrieval-free approach by turning knowledge documents into simulated multi-turn dialogues using a Multi-Document Traversal algorithm.
Outcome:	The proposed approach outperforms retrieval-augmented models while being cheaper and faster at domain transfer.

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Chaofa Yuan, Chuang Fan, Jianzhu Bao, Ruifeng Xu

Syntactic Text Processing

Challenge:	Existing methods to extract emotions and causes from unannotated emotion texts are labor intensive and limited applications in real-world scenarios.
Approach:	They propose a novel task to find emotions and corresponding causes in unannotated emotion texts.
Outcome:	The proposed model outperforms the state-of-the-art method by 2.26% (p0.001) in F1 measure.

Role Prompting Guided Domain Adaptation with General Capability Preserve for Large Language Models (2024.findings-naacl)

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Rui Wang, Fei Mi, Yi Chen, Boyang Xue, Hongru Wang, Qi Zhu, Kam-Fai Wong, Ruifeng Xu

A Neural Transition-based Model for Argumentation Mining (2021.acl-long)

Challenge:	Large Language Models (LLMs) suffer catastrophic forgetting when tailored to specific domains . authors present a novel approach to manage multi-domain LLM adaptation .
Approach:	They propose a strategy to manage multi-domain LLM adaptation using self-distillation and role integration.
Outcome:	The proposed model alleviates catastrophic forgetting and inter-domain confusion while maintaining robust general capabilities.

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Jianzhu Bao, Chuang Fan, Jipeng Wu, Yixue Dang, Jiachen Du, Ruifeng Xu

Improving Event Representation via Simultaneous Weakly Supervised Contrastive Learning and Clustering (2022.acl-long)

Challenge:	Existing methods for identifying argumentation structures are inefficient and class imbalanced.
Approach:	They propose a neural transition-based model that incrementally builds an argumentation graph by generating a sequence of actions.
Outcome:	The proposed model can handle tree and non-tree structured argumentation without structural constraints.

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Jun Gao, Wei Wang, Changlong Yu, Huan Zhao, Wilfred Ng, Ruifeng Xu

Target-based Sentiment Annotation in Chinese Financial News (2020.lrec-1)

Challenge:	Existing work exploits easily accessible co-occurrence information of events to learn event representations.
Approach:	They propose a weakly supervised contrastive learning method and a prototype-based clustering method for event representation learning.
Outcome:	The proposed framework outperforms baselines on Hard Similarity and Transitive Sentence Similarity tasks.

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Chaofa Yuan, Yuhan Liu, Rongdi Yin, Jun Zhang, Qinling Zhu, Ruibin Mao, Ruifeng Xu

Target-to-Source Augmentation for Aspect Sentiment Triplet Extraction (2023.emnlp-main)

Challenge:	Using a large corpus of 8,314 target-level sentiment annotations, sentiment classification on multiple opinion aspects/targets level is unsatisfactory.
Approach:	They propose to construct a large-scale target-based sentiment annotation corpus on Chinese financial news text.
Outcome:	The proposed corpus has 8,314 target-level sentiment annotations on Chinese financial news text.

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Yice Zhang, Yifan Yang, Meng Li, Bin Liang, Shiwei Chen, Ruifeng Xu

Multi-Modal Sarcasm Detection via Cross-Modal Graph Convolutional Network (2022.acl-long)

Challenge:	Aspect Sentiment Triplet Extraction (ASTE) is an important task in sentiment analysis, but data scarcity limits performance of existing methods.
Approach:	They propose a target-to-source augmentation approach to alleviate the issue of data scarcity in Aspect Sentiment Triplet Extraction (ASTE) they use fluency and alignment discriminators to provide feedback and use this feedback to optimize the generator.
Outcome:	The proposed approach significantly improves the performance of existing methods.

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Bin Liang, Chenwei Lou, Xiang Li, Min Yang, Lin Gui, Yulan He, Wenjie Pei, Ruifeng Xu

DS2-ABSA: Dual-Stream Data Synthesis with Label Refinement for Few-Shot Aspect-Based Sentiment Analysis (2025.acl-long)

Challenge:	Existing studies on multimodal sarcasm detection using textual and visual information have been limited to text-only approaches.
Approach:	They propose to construct a cross-modal graph for each multi-modal instance to explicitly draw the ironic relations between textual and visual modalities.
Outcome:	The proposed model achieves state-of-the-art in multi-modal sarcasm detection.

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Hongling Xu, Yice Zhang, Qianlong Wang, Ruifeng Xu

AEG: Argumentative Essay Generation via A Dual-Decoder Model with Content Planning (2022.emnlp-main)

Challenge:	Existing methods for annotating data are time-consuming and labor-intensive . Existing low-resource solutions comprise data augmentation and in-context learning .
Approach:	They propose a dual-stream data synthesis framework for few-shot ABSA . it leverages key-point-driven and instance-driven LLMs to generate diverse data .
Outcome:	Extensive experiments show that DS2-ABSA outperforms existing methods . previous studies have shown that the proposed approach generates diverse data .

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Jianzhu Bao, Yasheng Wang, Yitong Li, Fei Mi, Ruifeng Xu

Mitigating Biases of Large Language Models in Stance Detection with Counterfactual Augmented Calibration (2025.naacl-long)

Challenge:	Existing studies on argument generation focus on generating individual short arguments, while research on generating long and coherent argumentative essays is under-explored.
Approach:	They propose a task to automatically generate argumentative essays using a writing prompt.
Outcome:	The proposed model generates persuasive essays with higher diversity and less repetition compared to baselines.

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Ang Li, Jingqian Zhao, Bin Liang, Lin Gui, Hui Wang, Xi Zeng, Xingwei Liang, Kam-Fai Wong, Ruifeng Xu