Papers by Ying Wu

From Alignment to Entailment: A Unified Textual Entailment Framework for Entity Alignment (2023.findings-acl)

Challenge:	Entity name matching always retrieves irrelevant facts from the view of local entity words.
Approach:	They propose a knowledge selection approach and a generative model that integrates commonsense knowledge into the dialogue response generation by integrating commonsensical knowledge into a query.
Outcome:	The proposed approach improves on the most metrics and comparable to baselines.

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Yu Zhao, Yike Wu, Xiangrui Cai, Ying Zhang, Haiwei Zhang, Xiaojie Yuan

ObjChangeVR: Object State Change Reasoning from Continuous Egocentric Views in VR Environments (2026.eacl-long)

Challenge:	Existing methods encode the triples of entities as embeddings and learn to align the embeddables, which prevents the direct interaction between the original information of the cross-KG entities.
Approach:	They propose to transform the triples into unified textual sequences and model the EA task as a bi-directional textual entailment task between the sequences of cross-KG entities.
Outcome:	The proposed approach outperforms the state-of-the-art methods on five cross-lingual datasets and allows the mutual enhancement of the heterogeneous information.

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Shiyi Ding, Shaoen Wu, Ying Chen

Reasoning

Challenge:	Recent advances in multimodal large language models (MLLMs) have shown remarkable capabilities in scene understanding.
Approach:	They propose a framework that combines viewpoint-aware and temporal-based retrieval to identify relevant frames, along with cross-view reasoning that reconciles inconsistent evidence from multiple viewpoints.
Outcome:	Extensive experiments show that the proposed framework outperforms baseline approaches across multiple MLLMs.

It is AI’s Turn to Ask Humans a Question: Question-Answer Pair Generation for Children’s Story Books (2022.acl-long)

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Bingsheng Yao, Dakuo Wang, Tongshuang Wu, Zheng Zhang, Toby Li, Mo Yu, Ying Xu

Natural Language Interfaces

Challenge:	Existing question answering (QA) techniques are created mainly to answer questions asked by humans, but in educational applications, teachers often need to decide what questions to ask .
Approach:	They propose to use a fairytale-themed storybook as input to generate QA pairs that can test a student's comprehension skills.
Outcome:	The proposed system outperforms state-of-the-art QAG baseline systems and builds an interactive story-telling application for the future real-world deployment.

Anchoring-Guidance Fine-Tuning (AnGFT): Elevating Professional Response Quality in Role-Playing Conversational Agents (2025.emnlp-main)

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Qibin Li, Zhen Xu, Shengyuan Bai, Nianmin Yao, Kaili Sun, Bowen Wu, Ying Li, Baoxun Wang

Language Models

Challenge:	Large Language Models (LLMs) have demonstrated significant advancements in various fields, notably in Role-Playing Conversational Agents (RPCAs).
Approach:	They propose an Anchoring-Guidance Fine-Tuning Framework to integrate relevant expert knowledge into RPCAs' training process to mitigate this issue.
Outcome:	The proposed framework significantly improves the RPCAs’ performance in handling role-specific professional queries while preserving their robust role-playing abilities.

A Confidence-based Partial Label Learning Model for Crowd-Annotated Named Entity Recognition (2023.findings-acl)

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Limao Xiong, Jie Zhou, Qunxi Zhu, Xiao Wang, Yuanbin Wu, Qi Zhang, Tao Gui, Xuanjing Huang, Jin Ma, Ying Shan

Information Extraction & Text Mining

Challenge:	Existing models for named entity recognition (NER) are based on large-scale labeled datasets, which always obtain using crowdsourcing.
Approach:	They propose a CONfidence-based partial Label Learning method to integrate prior and posterior confidences for crowd-annotated named entity recognition models.
Outcome:	The proposed model improves on real-world and synthetic datasets compared with baselines.

Towards Robust Few-Shot Relation Classification: Incorporating Relation Description with Agreement (2025.findings-emnlp)

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Mengting Hu, Jianfeng Wu, Ming Jiang, Yalan Xie, Zhunheng Wang, Rui Ying, Xiaoyi Liu, Ruixuan Xu, Hang Gao, Renhong Cheng

Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network (P18-1)

Challenge:	Existing approaches to recognize relational relationships with a few support samples are limited for unlimited queries.
Approach:	They propose a simple but effective framework that uses relation descriptions as external knowledge to enhance the model’s comprehension of the relation semantics.
Outcome:	The proposed framework outperforms strong baselines while being robust against various NOTA rates.

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Xiangyang Zhou, Lu Li, Daxiang Dong, Yi Liu, Ying Chen, Wayne Xin Zhao, Dianhai Yu, Hua Wu

Natural Language Interfaces

Challenge:	Existing models for matching dialogue responses rely on semantic and functional dependencies . a recent study only uses the last utterance in context for matching a reply .
Approach:	They propose a model that matches a response with its multi-turn context using attention.
Outcome:	The proposed model outperforms the state-of-the-art models on two large-scale multi-turn response selection tasks.

Instance-level Randomization: Toward More Stable LLM Evaluations (2025.findings-emnlp)

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Yiyang Li, Yonghuang Wu, Ying Luo, Liangtai Sun, Zishu Qin, Lin Qiu, Xuezhi Cao, Xunliang Cai

Fantastic Questions and Where to Find Them: FairytaleQA – An Authentic Dataset for Narrative Comprehension (2022.acl-long)

Challenge:	Evaluations of large language models suffer from instability, where small changes of random factors can lead to drastic fluctuations of scores and even model rankings.
Approach:	They propose an instance-level randomization method to reduce variance and improve fairness in evaluations by randomizing all factors that affect evaluation scores for every single instance.
Outcome:	The proposed method reduces variance and improves fairness in model comparisons by using instance-level randomization.

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Ying Xu, Dakuo Wang, Mo Yu, Daniel Ritchie, Bingsheng Yao, Tongshuang Wu, Zheng Zhang, Toby Li, Nora Bradford, Branda Sun, Tran Hoang, Yisi Sang, Yufang Hou, Xiaojuan Ma, Diyi Yang, Nanyun Peng, Zhou Yu, Mark Warschauer

Question Answering

Challenge:	Existing QA datasets rarely distinguish fine-grained reading skills, such as the understanding of varying narrative elements.
Approach:	They propose to use FairytaleQA to generate 10,580 questions based on 278 children-friendly stories to assess model's fine-grained learning skills.
Outcome:	The proposed dataset consists of 10,580 questions derived from 278 children-friendly stories, covering seven types of narrative elements or relations.

Refine, Align, and Aggregate: Multi-view Linguistic Features Enhancement for Aspect Sentiment Triplet Extraction (2024.findings-acl)

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Guixin Su, Mingmin Wu, Zhongqiang Huang, Yongcheng Zhang, Tongguan Wang, Yuxue Hu, Ying Sha

Sentiment Analysis

Challenge:	Aspect Sentiment Triplet Extraction (ASTE) aims to extract the triplets of aspect terms, their associated sentiment and opinion terms.
Approach:	They propose to use multi-view linguistic features enhancement to explore the prior indication effect in the “Refine, Align, and Aggregate” learning process to enhance aspect-opinion relations.
Outcome:	The proposed model achieves state-of-the-art on several benchmark datasets and is robust to state- of-the art constraints.

Test-Time Scaling in Multimodal Foundation Models: A Comprehensive Survey of Generation and Reasoning (2026.findings-acl)

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Cong Wan, Ying He, Zhongzhan Huang, Hefeng Wu

Unified Grid Tagging Scheme for Aspect Sentiment Quad Prediction (2025.coling-main)

Challenge:	Recent advances have adapted this paradigm to Multimodal Foundation Models (MFMs), unlocking their potential in multimodal reasoning and generation.
Approach:	They propose a taxonomy framework that categorizes existing methodologies into three distinct strategies: sampling-based, feedback-based and search-based approaches.
Outcome:	The proposed framework categorizes existing methodologies into three distinct strategies: sampling-based, feedback-based and search-based approaches.

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Guixin Su, Yongcheng Zhang, Tongguan Wang, Mingmin Wu, Ying Sha

Syntactic Text Processing

Challenge:	Existing table-filling methods decompose the ASQP task into subtasks without considering the association between sentiment elements.
Approach:	They propose a simple yet effective Unified Grid Tagging Scheme to extract sentiment quadruplets in one shot . they leverage syntactic dependency tree and AMR graph to enrich association between sentiment elements .
Outcome:	The proposed model extracts all sentiment elements in quads for a given review to explain the reason for the sentiment.

Neural Response Generation with Meta-words (P19-1)

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Can Xu, Wei Wu, Chongyang Tao, Huang Hu, Matt Schuerman, Ying Wang

Simple but Effective Compound Geometric Operations for Temporal Knowledge Graph Completion (2024.acl-long)

Challenge:	Experimental results show that meta-words can be used to generate open domain dialogues . human-machine conversation is a fundamental problem in NLP .
Approach:	They propose a goal-tracking memory network that formalizes meta-word expression as a target in response generation and manages the generation process with a state memory panel and a controller.
Outcome:	The proposed model outperforms state-of-the-art generation models in response relevance, response diversity, and accuracy.

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Rui Ying, Mengting Hu, Jianfeng Wu, Yalan Xie, Xiaoyi Liu, Zhunheng Wang, Ming Jiang, Hang Gao, Linlin Zhang, Renhong Cheng

K-PLUG: Knowledge-injected Pre-trained Language Model for Natural Language Understanding and Generation in E-Commerce (2021.findings-emnlp)

Challenge:	Current methods embed factual knowledge into continuous vector space and apply geometric operations to learn potential patterns in temporal knowledge graphs.
Approach:	They propose a temporal knowledge graph completion method that uses two geometric operations to learn missing facts in temporal graphs.
Outcome:	The proposed method significantly outperforms existing temporal knowledge graph embedding models.

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Song Xu, Haoran Li, Peng Yuan, Yujia Wang, Youzheng Wu, Xiaodong He, Ying Liu, Bowen Zhou

HetGCoT: Heterogeneous Graph-Enhanced Chain-of-Thought LLM Reasoning for Academic Question Answering (2025.findings-emnlp)

Challenge:	Existing pre-trained language models are not explicitly aware of domain-specific knowledge, which is essential for downstream tasks in many domains, such as tasks in e-commerce scenarios.
Approach:	They propose a knowledge-injected pre-trained language model that can be transferred to both natural language understanding and generation tasks.
Outcome:	The proposed model significantly outperforms baselines across the board in e-commerce scenarios.

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Runsong Jia, Mengjia Wu, Ying Ding, Jie Lu, Yi Zhang

Question Answering

Challenge:	graph neural networks capture structured graph information, but lack integration at the reasoning level.
Approach:	They propose a framework that leverages graph structural information to reason interpretable academic QA results.
Outcome:	The proposed framework outperforms sota baselines on OpenAlex and DBLP datasets.

DuReader-Retrieval: A Large-scale Chinese Benchmark for Passage Retrieval from Web Search Engine (2022.emnlp-main)

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Yifu Qiu, Hongyu Li, Yingqi Qu, Ying Chen, QiaoQiao She, Jing Liu, Hua Wu, Haifeng Wang

Information Retrieval

Challenge:	Existing datasets for non-English passage retrieval are lacking in quality and accuracy.
Approach:	They present a large-scale Chinese dataset for passage retrieval . they reduce false negatives by manually annotating results pooled from multiple retrievers .
Outcome:	The proposed dataset reduces false negatives in development and testing sets and removes similar training queries.

Learning to Ask Denotative and Connotative Questions for Knowledge-based VQA (2024.findings-emnlp)

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Xiaoying Xing, Peixi Xiong, Lei Fan, Yunxuan Li, Ying Wu

MoSE: Modality Split and Ensemble for Multimodal Knowledge Graph Completion (2022.emnlp-main)

Challenge:	Large language models have attracted increasing attention due to their prominent performance on various tasks.
Approach:	They propose to let LLMs learn to ask informative questions to collect visual information . they introduce concepts of denotation and connotation to promote image and question understanding .
Outcome:	The proposed model can generate high-quality questions and efficiently collect required information without expensive training or annotations.

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Yu Zhao, Xiangrui Cai, Yike Wu, Haiwei Zhang, Ying Zhang, Guoqing Zhao, Ning Jiang

A Joint Neural Model for Information Extraction with Global Features (2020.acl-main)

Challenge:	Existing methods to predict missing entities share relation representation across modalities, which results in mutual interference between modality.
Approach:	They propose a framework for multimodal knowledge graph completion that learns modality-split relation embeddings for each modality instead of a single modality shared one.
Outcome:	The proposed framework outperforms state-of-the-art methods on three KG datasets.

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Ying Lin, Heng Ji, Fei Huang, Lingfei Wu

Information Extraction & Text Mining

Challenge:	Existing joint neural models for Information Extraction use local task-specific classifiers to predict labels for individual instances.
Approach:	They propose a joint neural framework that extracts the optimal IE result as a graph from an input sentence.
Outcome:	The proposed model achieves new state-of-the-art on all subtasks and does not use any language-specific feature.

RAIDEN Benchmark: Evaluating Role-playing Conversational Agents with Measurement-Driven Custom Dialogues (2025.coling-main)

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Bowen Wu, Kaili Sun, Ziwei Bai, Ying Li, Baoxun Wang

Natural Language Interfaces

Challenge:	Existing benchmarks for RPCA evaluation are lacking for dialogues . authors introduce specialized judging LLM for automatic RPca evaluation . compelling role-playing agent is expected to lead to more in-depth conversations .
Approach:	They propose a benchmark to assess the effectiveness of RPCA interactions using dialogues . they introduce a specialized judging LLM tailored for automatic RPca evaluation .
Outcome:	The proposed benchmark focuses on assessing particular dimensions at different stages of a conversation, facilitated through interactions conducted by annotators.

Section-Aware Commonsense Knowledge-Grounded Dialogue Generation with Pre-trained Language Model (2022.coling-1)

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Sixing Wu, Ying Li, Ping Xue, Dawei Zhang, Zhonghai Wu

Plot2Code: A Comprehensive Benchmark for Evaluating Multi-modal Large Language Models in Code Generation from Scientific Plots (2025.findings-naacl)

Challenge:	Pre-trained language models can be expected to deepen the fusing of dialogue context and knowledge because of their superior ability of semantic understanding.
Approach:	They propose a two-stage framework to integrate a linearized knowledge into plan text using a ranking network PriorRanking to estimate the relevance of a retrieved knowledge fact.
Outcome:	The proposed framework improves the performance of pre-trained language models by using section-aware strategies to encode the linearized knowledge.

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Chengyue Wu, Zhixuan Liang, Yixiao Ge, Qiushan Guo, Zeyu Lu, Jiahao Wang, Ying Shan, Ping Luo

LLaMA Pro: Progressive LLaMA with Block Expansion (2024.acl-long)

Challenge:	Multi-modal Large Language Models have shown remarkable progress in visual contexts, yet their ability to convert visual figures into executable code remains underexplored.
Approach:	They propose to use a set of visual coding metrics to assess MLLMs' visual . pass rate, text-match ratio, and GPT-4V rating judgement to assess the quality of generated code and rendered images.
Outcome:	The proposed benchmark includes 132 high-quality matplotlib plots across six plot types, as well as 150 and 86 plots from Python’s and R’s plotly libraries respectively, totaling 368 plots.

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Chengyue Wu, Yukang Gan, Yixiao Ge, Zeyu Lu, Jiahao Wang, Ye Feng, Ying Shan, Ping Luo

Refining Idioms Semantics Comprehension via Contrastive Learning and Cross-Attention (2024.lrec-main)

Challenge:	Existing studies have demonstrated that pre-trained LLMs are limited in certain domains, such as programming, mathematics, biomedical, or finance.
Approach:	They propose a new post-pretraining method with an expansion of Transformer blocks to tune the expanded blocks using only new corpus, efficiently and effectively improving the model’s knowledge while mitigating forgetting.
Outcome:	The proposed model outperforms existing models in programming and math and its instruction-following counterpart LLaMA Pro-8.3B in general tasks, programming, and mathematics.

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Mingmin Wu, Guixin Su, Yongcheng Zhang, Zhongqiang Huang, Ying Sha

KSAM: Infusing Multi-Source Knowledge into Dialogue Generation via Knowledge Source Aware Multi-Head Decoding (2022.findings-acl)

Challenge:	Existing methods based on deep learning struggle to grasp idiom semantics due to the figurative meanings of many idiomas deviating from their literal interpretations.
Approach:	They propose a Chinese idiom cloze test to capture comprehensive idiomatics and a semantic sense contrastive learning module to enhance the representation of idiomics.
Outcome:	The proposed model outperforms state-of-the-art models on the Chinese idiom cloze test and on other benchmark datasets.

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Sixing Wu, Ying Li, Dawei Zhang, Zhonghai Wu

Overcoming Language Priors in Visual Question Answering via Distinguishing Superficially Similar Instances (2022.coling-1)

Challenge:	Existing knowledge-enhanced methods only seek knowledge from a single source, resulting in insufficient coverage of a given knowledge source.
Approach:	They propose to use multiple independent decoder heads to infuse multi-source knowledge into dialogue generation more efficiently by incorporating external knowledge into the dialogue generation.
Outcome:	The proposed approach overcomes three challenges in infusing multi-source knowledge into dialogue generation more efficiently.

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Yike Wu, Yu Zhao, Shiwan Zhao, Ying Zhang, Xiaojie Yuan, Guoqing Zhao, Ning Jiang

Visual Data in NLP

Challenge:	Existing VQA models rely on the superficial correlation between question type and frequent answers to make predictions, without really understanding the input.
Approach:	They propose a training framework that explicitly encourages the VQA model to distinguish between superficially similar instances.
Outcome:	The proposed framework achieves state-of-the-art performance on VQA-CP v2 . it explicitly encourages the model to distinguish between the superficially similar instances .

XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation (2020.emnlp-main)

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Yaobo Liang, Nan Duan, Yeyun Gong, Ning Wu, Fenfei Guo, Weizhen Qi, Ming Gong, Linjun Shou, Daxin Jiang, Guihong Cao, Xiaodong Fan, Ruofei Zhang, Rahul Agrawal, Edward Cui, Sining Wei, Taroon Bharti, Ying Qiao, Jiun-Hung Chen, Winnie Wu, Shuguang Liu, Fan Yang, Daniel Campos, Rangan Majumder, Ming Zhou

Multilinguality

Challenge:	XGLUE provides a benchmark dataset to train large-scale cross-lingual pre-trained models . XCLUE provides 11 diversified tasks that cover both understanding and generation scenarios .
Approach:	They introduce a new benchmark dataset to train large-scale cross-lingual pre-trained models using multilingual and bilingual corpora.
Outcome:	The proposed dataset is labeled in English and includes only natural language understanding tasks.

Prompt Optimization for Relation Extraction using Reinforcement Learning (2026.findings-acl)

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Ying Liu, Dong Shuai, Cui Zibo, TengQi Ye, Gang Wu

Diverse and Informative Dialogue Generation with Context-Specific Commonsense Knowledge Awareness (2020.acl-main)

Challenge:	Existing prompt-based methods rely heavily on large-scale annotated datasets limiting their applicability in domain-specific and low-resource scenarios.
Approach:	They propose a reinforcement learning-based automated prompt optimization framework for domain relation extraction that optimizes prompt quality through interaction with a black-box LLM.
Outcome:	The proposed framework outperforms existing prompt-based methods and supervised baselines on multiple extraction datasets across medical, financial, legal, and news domains.

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Sixing Wu, Ying Li, Dawei Zhang, Yang Zhou, Zhonghai Wu

LLMs Can Simulate Standardized Patients via Agent Coevolution (2025.acl-long)

Challenge:	Generative dialogue systems tend to produce generic and boring responses, causing boring conversations . a novel commonsense knowledge-aware dialogue generation model is proposed to solve this problem .
Approach:	They propose to retrieve and introduce knowledge facts from knowledge graphs to reduce boring conversations . they use a Felicitous Fact mechanism to help the model focus on context-relevant knowledge facts .
Outcome:	The proposed model outperforms the state-of-the-art approach in most experiments.

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Zhuoyun Du, LujieZheng LujieZheng, Renjun Hu, Yuyang Xu, Xiawei Li, Ying Sun, Wei Chen, Jian Wu, Haolei Cai, Haochao Ying

Challenge:	Training medical personnel using standardized patients (SPs) remains a complex challenge, necessitating extensive domain expertise and role-specific practice.
Approach:	They propose a simulated patient framework that allows patient agents to simulate diagnostic process through multi-turn dialogues.
Outcome:	The proposed framework improves over existing reasoning methods by more than 10% in requirement alignment and better human preference after evolving over 200 cases for 10 hours with excellent generalizability.

GAIA: A Fine-grained Multimedia Knowledge Extraction System (2020.acl-demos)

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Manling Li, Alireza Zareian, Ying Lin, Xiaoman Pan, Spencer Whitehead, Brian Chen, Bo Wu, Heng Ji, Shih-Fu Chang, Clare Voss, Daniel Napierski, Marjorie Freedman

Visual Data in NLP

Challenge:	Open source knowledge extraction tools are used for many real-world applications, but there is no comprehensive system for KE.
Approach:	They propose a multimedia knowledge extraction system that takes multimedia data from various sources and languages as input and creates a coherent, structured knowledge base.
Outcome:	The system achieves top performance at the recent NIST TAC SM-KBP2019 evaluation.

DuQM: A Chinese Dataset of Linguistically Perturbed Natural Questions for Evaluating the Robustness of Question Matching Models (2022.emnlp-main)

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Hongyu Zhu, Yan Chen, Jing Yan, Jing Liu, Yu Hong, Ying Chen, Hua Wu, Haifeng Wang

Robustness in NLP

Challenge:	a comprehensive evaluation of QM models should be conducted on natural texts, not on artificial adversarial examples . ral models are often not robust to adversarials, which means they predict unexpected outputs .
Approach:	They use a Chinese dataset to evaluate the robustness of QM models . they show that the effect of artificial adversarial examples does not work on natural texts .
Outcome:	The proposed model is more robust than other models on natural questions with 32 linguistic perturbations.

PASUM: A Pre-training Architecture for Social Media User Modeling Based on Text Graph (2024.lrec-main)

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Kun Wu, Xinyi Mou, Lanqing Xue, Zhenzhe Ying, Weiqiang Wang, Qi Zhang, Xuanjing Huang, Zhongyu Wei

P3LM: Probabilistically Permuted Prophet Language Modeling for Generative Pre-Training (2022.findings-emnlp)

Challenge:	Existing studies have incorporated different digital traces to better learn the representations of social media users, limited by overloaded text information and hard-to-collect social network information.
Approach:	They propose a Pre-training Architecture for Social Media User Modeling based on Text Graph and combine microblogs to represent social media users based upon the text graph model.
Outcome:	The proposed framework can represent users based on text even without social network information on microblogs.

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Junwei Bao, Yifan Wang, Ying Jiangyong, Yeyun Gong, Jing Zhao, Youzheng Wu, Xiaodong He

Language Models

Challenge:	Existing autoregressive left-to-right (L2R) models are limited to unidirectional information and constrained on strong local dependencies.
Approach:	They propose a probabilistically permuted prophet language model which strengthens the modeling of bidirectional information and long token dependencies for sequence generation.
Outcome:	Experiments on GLGE dataset show that P3LM improves on natural language generation tasks.

MIND: A Large-scale Dataset for News Recommendation (2020.acl-main)

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Fangzhao Wu, Ying Qiao, Jiun-Hung Chen, Chuhan Wu, Tao Qi, Jianxun Lian, Danyang Liu, Xing Xie, Jianfeng Gao, Winnie Wu, Ming Zhou

APEX: Learning Adaptive Priorities for Multi-Objective Alignment in Vision-Language Generation (2026.findings-acl)

Challenge:	Personalized news recommendation is an important technique for personalized news service.
Approach:	They propose to build a large-scale news recommendation dataset from Microsoft News . they demonstrate that news recommendation relies on the quality of news content understanding .
Outcome:	The proposed dataset contains 1 million users and more than 160k English news articles, each of which has rich textual content such as title, abstract and body.

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Dongliang Chen, Xinlin Zhuang, Junjie Xu, Luojian Xie, Zehui Wang, Jiaxi Zhuang, Haolin Yang, Liang Dou, Xiao He, Xingjiao Wu, Ying Qian

Equal Truth: Rumor Detection with Invariant Group Fairness (2025.findings-emnlp)

Challenge:	APEX optimizes for text-to-image generation by combining learning potential, conflict penalty, and progress need.
Approach:	They propose an algorithm that stabilizes heterogeneous rewards and dynamically schedules objectives . they propose a method that achieves better Pareto trade-offs across four heterogenous objectives based on P3 Adaptive Priorities .
Outcome:	The proposed algorithm achieves better pareto trade-offs across four heterogeneous objectives while maintaining competitive OCR accuracy.

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Junyi Chen, Mengjia Wu, Qian Liu, Jing Sun, Ying Ding, Yi Zhang

More is Better: Enhancing Open-Domain Dialogue Generation via Multi-Source Heterogeneous Knowledge (2021.emnlp-main)

Challenge:	Existing rumor detection methods rarely consider fairness issues inherent in the model . this can lead to biased predictions across stakeholder groups, undermining their detection effectiveness .
Approach:	They propose a framework to address fairness issues inherent in rumor detection models . they perform unsupervised partitioning to dynamically identify potential unfair data patterns . then, they apply invariant learning to these partitions to extract fair and informative feature representations .
Outcome:	The proposed method outperforms strong baselines regarding detection and fairness performance . it also shows robust performance on out-of-distribution samples .

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Sixing Wu, Ying Li, Minghui Wang, Dawei Zhang, Yang Zhou, Zhonghai Wu