Papers by Yulan He

Two Heads Are Better Than One: Dual-Model Verbal Reflection at Inference-Time (2025.emnlp-main)

Challenge:	Long-document Question Answering (QA) challenges with large-scale text and long-distance dependencies.
Approach:	They propose a method that leverages large language models to control retrieval process . they propose 'attention-based' retrieval methods that construct hierarchical graphs .
Outcome:	The proposed method achieves LLM-level performance while maintaining computational complexity comparable to RAG methods.

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Jiazheng Li, Yuxiang Zhou, Junru Lu, Gladys Tyen, Lin Gui, Cesare Aloisi, Yulan He

Boundary-Aware LLM Augmentation for Low-Resource Event Argument Extraction (2026.eacl-long)

Challenge:	Existing LLMs struggle to reliably detect subtle reasoning errors in ASAS tasks.
Approach:	They propose a dual-model framework with a dedicated Critic model trained for effective reflection that generates precise verbal feedback.
Outcome:	The proposed framework outperforms existing ASAS benchmarks and provides valuable insights into the performance of the proposed framework.

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Zhaoyue Sun, Gabriele Pergola, Yulan He

EXPLAIN, EDIT, GENERATE: Rationale-Sensitive Counterfactual Data Augmentation for Multi-hop Fact Verification (2023.emnlp-main)

Challenge:	Event argument extraction (EAE) is a crucial task in information extraction but its performance heavily depends on expensive annotated data.
Approach:	They investigate argument replacement, adjunction rewriting, their combination, and annotation generation using four LLM-based augmentation strategies.
Outcome:	The proposed methods improve performance over boundary-agnostic methods and provide detailed analysis of quality from multiple perspectives.

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Yingjie Zhu, Jiasheng Si, Yibo Zhao, Haiyang Zhu, Deyu Zhou, Yulan He

Distilling ChatGPT for Explainable Automated Student Answer Assessment (2023.findings-emnlp)

Challenge:	Existing methods to augment training data with counterfactuals fail to handle multi-hop fact verification due to their incapability to preserve complex logical relationships.
Approach:	They propose to augment training data with counterfactuals that alter causal features of the original data by preserving logical relationships.
Outcome:	The proposed method outperforms the baselines and can generate linguistically diverse counterfactuals without disrupting their logical relationships.

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Jiazheng Li, Lin Gui, Yuxiang Zhou, David West, Cesare Aloisi, Yulan He

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

Challenge:	Existing automated student answer assessment models lack explainable and faithful feedback.
Approach:	They propose a framework that leverages ChatGPT for student answer scoring and rationale generation.
Outcome:	The proposed method improves the overall QWK score by 11% compared to ChatGPT.

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

Modeling Subjectivity in Cognitive Appraisal with Language Models (2025.findings-emnlp)

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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Yuxiang Zhou, Hainiu Xu, Desmond Ong, Maria Liakata, Petr Slovak, Yulan He

Explainable Depression Detection in Clinical Interviews with Personalized Retrieval-Augmented Generation (2025.findings-acl)

Challenge:	a new study explores how language models can quantify subjectivity in cognitive appraisal . existing post-hoc calibration methods fail to achieve satisfactory performance .
Approach:	They investigate how language models can quantify subjectivity in cognitive appraisal . existing post-hoc calibration methods often fail to achieve satisfactory performance .
Outcome:	The proposed model can quantify subjectivity in cognitive appraisal using fine-tuned models and prompt-based large language models.

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Linhai Zhang, Ziyang Gao, Deyu Zhou, Yulan He

Calibrating LLMs with Preference Optimization on Thought Trees for Generating Rationale in Science Question Scoring (2024.findings-emnlp)

Challenge:	Existing systems rely on black-box neural networks, which lack interpretability, which is crucial in mental health contexts.
Approach:	They propose a Retrieval-augmented generation framework for Explainable depression detection that retrieves evidence from clinical interview transcripts, providing explanations for predictions.
Outcome:	The proposed framework retrieves evidence from clinical interview transcripts, providing explanations for predictions.

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Jiazheng Li, Hainiu Xu, Zhaoyue Sun, Yuxiang Zhou, David West, Cesare Aloisi, Yulan He

NapSS: Paragraph-level Medical Text Simplification via Narrative Prompting and Sentence-matching Summarization (2023.findings-eacl)

Challenge:	Existing methods for generating rationales that justify scoring decisions are not accurate and often contain hallucinated information.
Approach:	They propose a framework capable of generating more faithful rationales and matching performance with classifier-based scoring systems.
Outcome:	The proposed framework achieves 38% improvement in QWK score compared to prior work . it can be used to match performance with classifier-based scoring systems .

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Junru Lu, Jiazheng Li, Byron Wallace, Yulan He, Gabriele Pergola

Text Generation

Challenge:	a recent study shows that accessing medical literature is difficult for laypeople because it is written for specialists and contains medical jargon.
Approach:	They propose a two-stage strategy to identify relevant content to be simplified . they first generate reference summaries via sentence matching between the original and simplified abstracts .
Outcome:	The proposed approach improves on a seq2seq-based test set on an English medical corpus . it also improves the SARI score by 1.1% .

LlmLink: Dual LLMs for Dynamic Entity Linking on Long Narratives with Collaborative Memorisation and Prompt Optimisation (2025.coling-main)

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Lixing Zhu, Jun Wang, Yulan He

CODI: Compressing Chain-of-Thought into Continuous Space via Self-Distillation (2025.emnlp-main)

Challenge:	Existing methods focus on supervised fine-tuning or limited to one-off prediction, which poses a challenge where the context is long.
Approach:	They propose a dynamic approach to CoREFerence resolution in chunked long narratives by deploying dual Large Language Models.
Outcome:	The proposed model achieves performance gains over existing models and fine-tuning approaches on long narrative datasets, significantly reducing the resources required for inference and training.

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Zhenyi Shen, Hanqi Yan, Linhai Zhang, Zhanghao Hu, Yali Du, Yulan He

Topic-Driven and Knowledge-Aware Transformer for Dialogue Emotion Detection (2021.acl-long)

Challenge:	Prior implicit CoT methods have underperformed in terms of efficiency and robustness by relying on natural language tokens for reasoning.
Approach:	They propose a training framework that compresses natural language CoT into continuous space by aligning hidden states of a designated token.
Outcome:	The proposed framework outperforms the existing state-of-the-art in 3.1x compression rate and 28.2% accuracy on GSM8k scale.

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Lixing Zhu, Gabriele Pergola, Lin Gui, Deyu Zhou, Yulan He

NOVER: Incentive Training for Language Models via Verifier-Free Reinforcement Learning (2025.emnlp-main)

Challenge:	Emotion detection in dialogues requires the identification of thematic topics underlying a conversation, commonsense knowledge, and the intricate transition patterns between affective states.
Approach:	They propose a Topic-Driven Knowledge-Aware Transformer model that integrates topic representation and commonsense knowledge from ATOMIC for dialogue emotion detection.
Outcome:	The proposed model outperforms state-of-the-art models on four dialogue datasets . it can detect topics which help distinguish emotion categories, the authors show .

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Wei Liu, Siya Qi, Xinyu Wang, Chen Qian, Yali Du, Yulan He

DrugWatch: A Comprehensive Multi-Source Data Visualisation Platform for Drug Safety Information (2024.acl-demos)

Challenge:	Recent advances in reinforcement learning, such as DeepSeek R1-Zero, highlight the effectiveness of incentive training, but these methods rely on external verifiers, which limits their applicability to domains like mathematics and coding, where such verifier is readily available.
Approach:	They propose a general reinforcement learning framework that requires only standard supervised fine-tuning data with no need for an external verifier.
Outcome:	The proposed framework outperforms the model of the same size distilled from large reasoning models such as DeepSeek R1 671B by 7.7%.

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Artem Bobrov, Domantas Saltenis, Zhaoyue Sun, Gabriele Pergola, Yulan He

The Mystery of In-Context Learning: A Comprehensive Survey on Interpretation and Analysis (2024.emnlp-main)

Challenge:	Drug safety research is crucial for maintaining public health, but resources available to the public are limited.
Approach:	They propose an easy-to-use and interactive multi-source information visualisation platform for drug safety study.
Outcome:	The proposed platform provides a one-stop information analysis, retrieval, and annotation service.

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Yuxiang Zhou, Jiazheng Li, Yanzheng Xiang, Hanqi Yan, Lin Gui, Yulan He

Topic-Aware Evidence Reasoning and Stance-Aware Aggregation for Fact Verification (2021.acl-long)

Challenge:	In-context learning (ICL) is a capability that enables large language models to excel in proficiency through demonstration examples.
Approach:	They present a survey on the interpretation and analysis of in-context learning . they focus on theoretical and empirical perspectives on the concept .
Outcome:	The proposed model can perform tasks with minimal examples without re-training and has demonstrated proficiency across various tasks with a minimal set of task-oriented examples.

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Jiasheng Si, Deyu Zhou, Tongzhe Li, Xingyu Shi, Yulan He

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

Challenge:	Existing methods for fact verification focus on analyzing semantic interaction between claim and evidence but fail to capture their topical consistency . Existing models focus on the aggregation of multiple pieces of evidence without considering their implicit stances to the claim, thereby introducing spurious information.
Approach:	They propose a topic-aware evidence reasoning and stance-again aggregation model that checks topical consistency between claims and evidence.
Outcome:	The proposed model outperforms state-of-the-art models on two benchmark datasets.

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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.

Extracting Event Temporal Relations via Hyperbolic Geometry (2021.emnlp-main)

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Xingwei Tan, Gabriele Pergola, Yulan He

Representation Learning

Challenge:	Recent neural approaches to event temporal relation extraction map events to embeddings in the Euclidean space and train a classifier to detect temporal relations between event pairs.
Approach:	They propose to embed events into hyperbolic spaces to model hierarchical structures . they propose to use hyperbolical embeddings to directly infer event relations .
Outcome:	The proposed architecture is based on two approaches to encode events and their temporal relations in hyperbolic spaces.

Adversarial Learning of Poisson Factorisation Model for Gauging Brand Sentiment in User Reviews (2021.eacl-main)

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Runcong Zhao, Lin Gui, Gabriele Pergola, Yulan He

Robustness in NLP

Challenge:	Existing models for sentiment-topic extraction assume topics are grouped under discrete sentiment categories such as ‘positive’, ‘negative’ and ‘neural’.
Approach:	They propose a Brand-Topic Model which aims to detect brand-associated polarity-bearing topics from product reviews.
Outcome:	The proposed model outperforms existing models on Amazon reviews and shows that it is more coherent and unique than existing models.

Large Language Models Fall Short: Understanding Complex Relationships in Detective Narratives (2024.findings-acl)

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Runcong Zhao, Qinglin Zhu, Hainiu Xu, Jiazheng Li, Yuxiang Zhou, Yulan He, Lin Gui

Tracking Brand-Associated Polarity-Bearing Topics in User Reviews (2023.tacl-1)

Challenge:	Existing datasets for narrative understanding fail to represent complexity and uncertainty of relationships in real-life social scenarios.
Approach:	They propose a benchmark for extracting and analysing intricate character relation graphs from detective narratives using large-scale large-language models.
Outcome:	The proposed dataset extracts and analyses character relation graphs from detective narratives using advanced Large Language Models like GPT-3.5, GPT-4, and Llama2 .

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Runcong Zhao, Lin Gui, Hanqi Yan, Yulan He

Challenge:	Existing models that infer brand polarity scores from reviews are not able to infer polarities directly.
Approach:	They propose a dynamic Brand-Topic Model which detects and tracks brand-associated sentiment scores and polarity-bearing topics from product reviews organized in temporally ordered time intervals.
Outcome:	The proposed model outperforms competitive models on a MakeupAlley and hotel review datasets.

RoleMRC: A Fine-Grained Composite Benchmark for Role-Playing and Instruction-Following (2025.findings-acl)

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Junru Lu, Jiazheng Li, Guodong Shen, Lin Gui, Siyu An, Yulan He, Di Yin, Xing Sun

Beyond Text: Incorporating Metadata and Label Structure for Multi-Label Document Classification using Heterogeneous Graphs (2021.emnlp-main)

Challenge:	Existing role-playing datasets mostly contribute to controlling role style and knowledge boundaries, but overlook role-following in instruction-follower scenarios.
Approach:	They propose a fine-grained role-playing and instruction-following composite benchmark, named RoleMRC, which includes multi-turn dialogues between ideal roles and humans, including free chats or discussions upon given passages .
Outcome:	The proposed model improves instruction-following without compromising general role-playing and reasoning capabilities.

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Chenchen Ye, Linhai Zhang, Yulan He, Deyu Zhou, Jie Wu

Leveraging ChatGPT in Pharmacovigilance Event Extraction: An Empirical Study (2024.eacl-short)

Challenge:	Existing methods for multi-label document classification ignore the heterogeneous graphical structures of metadata and labels.
Approach:	They propose a neural network based approach to multi-label document classification that uses two heterogeneous graphs to model metadata and labels.
Outcome:	The proposed approach outperforms state-of-the-art models on two benchmark datasets.

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Zhaoyue Sun, Gabriele Pergola, Byron Wallace, Yulan He

LearnLens: LLM-Enabled Personalised, Curriculum-Grounded Feedback with Educators in the Loop (2025.emnlp-demos)

Challenge:	pharmacovigilance event extraction is a key field of healthcare that involves identifying, evaluating, understanding, and preventing adverse effects.
Approach:	They investigate the ability of large language models (LLMs) to extract adverse events from medical text.
Outcome:	The proposed model performs reasonably well with demonstration selection strategies, but falls short compared to fully fine-tuned small models.

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Runcong Zhao, Artem Bobrov, Jiazheng Li, Cesare Aloisi, Yulan He

Challenge:	Existing systems that provide personalised, curriculum-aligned feedback are time-intensive and time-consuming.
Approach:	They propose a modular, LLM-based system that generates personalised, curriculum-aligned feedback in science education.
Outcome:	The proposed system generates personalised, curriculum-aligned feedback in science education.

Position Bias Mitigation: A Knowledge-Aware Graph Model for Emotion Cause Extraction (2021.acl-long)

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Hanqi Yan, Lin Gui, Gabriele Pergola, Yulan He

RGAR: Recurrence Generation-augmented Retrieval for Factual-aware Medical Question Answering (2025.findings-emnlp)

Challenge:	Existing models for ECE tend to explore relative position information and suffer from the dataset bias.
Approach:	They propose to generate adversarial examples where relative position is no longer indicative feature of cause clauses to address the dataset bias.
Outcome:	The proposed method performs on par with existing state-of-the-art methods on the original ECE dataset and is more robust against adversarial attacks compared to existing models.

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Sichu Liang, Linhai Zhang, Hongyu Zhu, Wenwen Wang, Yulan He, Deyu Zhou

EnigmaToM: Improve LLMs’ Theory-of-Mind Reasoning Capabilities with Neural Knowledge Base of Entity States (2025.findings-acl)

Challenge:	Existing retrieval approaches often overlook patient-specific factual knowledge embedded in EHRs . existing retrieval frameworks often overlook this factual information, limiting its effectiveness in clinical decision-making.
Approach:	They propose a recurrence generation-augmented retrieval framework that synergizes factual and conceptual knowledge from dual sources.
Outcome:	The proposed framework improves on factual-aware medical QA benchmarks.

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Hainiu Xu, Siya Qi, Jiazheng Li, Yuxiang Zhou, Jinhua Du, Caroline Catmur, Yulan He

Knowledge Representation

Challenge:	Existing ToM reasoning methods rely excessively on off-the-shelf LLMs, reducing their efficiency and limiting their applicability to high-order ToM.
Approach:	They propose a neuro-symbolic framework that integrates a Neural Knowledge Base of Entity States and knowledge injection to enhance ToM reasoning.
Outcome:	The proposed framework improves ToM reasoning on ToMi, HiToM, and FANToM benchmarks.

Evaluating LLMs’ Assessment of Mixed-Context Hallucination Through the Lens of Summarization (2025.findings-acl)

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Siya Qi, Rui Cao, Yulan He, Zheng Yuan

Text Generation

Challenge:	Large language models generate coherent text and follow instructions across diverse tasks, but a critical challenge in scaling LLM applications is hallucination, where the generated content lacks factual grounding or deviates from the intended discourse context.
Approach:	They use summarization as a representative task to evaluate LLMs' capability in detecting mixed-context hallucinations, specifically distinguishing between factual and non-factual hallucinos.
Outcome:	The proposed model distinguishes between factual and non-factual hallucinations, and their performance bottlenecks.

Open Event Extraction from Online Text using a Generative Adversarial Network (D19-1)

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Rui Wang, Deyu Zhou, Yulan He

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

Challenge:	Existing approaches to extract structured representations of open-domain events are limited . a recent study shows that the model outperforms the baseline approaches for extracting events from online texts .
Approach:	They propose an event extraction model based on Generative Adversarial Nets which captures latent events with a generator network and a discriminator to distinguish documents reconstructed from latent and original events.
Outcome:	The proposed model outperforms baseline models on two Twitter and a news article datasets.

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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

Relevant Emotion Ranking from Text Constrained with Emotion Relationships (N18-1)

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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Deyu Zhou, Yang Yang, Yulan He

Beyond Prompting: An Efficient Embedding Framework for Open-Domain Question Answering (2025.acl-long)

Challenge:	Existing methods to detect emotions from text are lexicon-based and learning-based . experimental results show that the proposed framework is better than state-of-the-art methods .
Approach:	They propose to use a multi-label classification problem to generate a ranked list of relevant emotions.
Outcome:	The proposed framework performs better than state-of-the-art methods and multi-label learning methods on two real-world corpora.

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Zhanghao Hu, Hanqi Yan, Qinglin Zhu, Zhenyi Shen, Yulan He, Lin Gui

Eliminating Biased Length Reliance of Direct Preference Optimization via Down-Sampled KL Divergence (2024.emnlp-main)

Challenge:	Large language models (LLMs) have recently pushed open-domain question answering (ODQA) to new heights.
Approach:	They propose an embedding-level framework that enhances both the retriever and the reader by reordering query representations via lightweight linear layers under an unsupervised contrastive learning objective.
Outcome:	The proposed framework outperforms baselines in accuracy and efficiency across three open-source LLMs, three retrieval methods, and four ODQA benchmarks.

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Junru Lu, Jiazheng Li, Siyu An, Meng Zhao, Yulan He, Di Yin, Xing Sun

Challenge:	Existing studies attributed verbosity to biased labels, but new research shows that DPO can be effective in mitigating verboses.
Approach:	They propose to use a method to reduce the amount of verbosity in LLMs by using a downsampling approach.
Outcome:	The proposed approach overcomes the problem of verbosity by reducing the length reliance of the proposed algorithm.

AERA Chat: An Interactive Platform for Automated Explainable Student Answer Assessment (2025.emnlp-demos)

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Jiazheng Li, Artem Bobrov, Runcong Zhao, Cesare Aloisi, Yulan He

A Scalable Framework for Table of Contents Extraction from Complex ESG Annual Reports (2023.emnlp-main)

Challenge:	Existing systems that use pretrained language models to score student answers are noisy and unreliable.
Approach:	They propose a visualization platform for automated student answer assessment that leverages multiple LLMs to generate rationales.
Outcome:	The proposed platform enables educators to mark tasks and researchers to evaluate rationale quality from different models.

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Xinyu Wang, Lin Gui, Yulan He

OpenToM: A Comprehensive Benchmark for Evaluating Theory-of-Mind Reasoning Capabilities of Large Language Models (2024.acl-long)

Challenge:	Existing methods for document classification focus on local layout, sidelining holistic comprehension of content and organisation.
Approach:	They propose a framework for Table of Contents extraction that uses hierarchical structure to extract text from ESG annual reports.
Outcome:	The proposed framework outperforms the state-of-the-art with a fraction of running time.

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Hainiu Xu, Runcong Zhao, Lixing Zhu, Jinhua Du, Yulan He

BullStop: A Mobile App for Cyberbullying Prevention (2020.coling-demos)

Challenge:	Existing N-ToM benchmarks lack ambiguous and artificial narratives, lack of personality traits and preferences, and limited diversity in the questions posed.
Approach:	They propose a benchmark to assess Neural Theory-of-Mind (N-ToM) with longer and clearer narrative stories, characters with explicit personality traits, actions triggered by character intentions, and questions designed to challenge LLMs’ abilities of modeling characters’ mental states.
Outcome:	The proposed test aims to assess the performance of LLMs in the physical and psychological worlds.

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Semiu Salawu, Yulan He, Jo Lumsden

FIPO: Free-form Instruction-oriented Prompt Optimization with Preference Dataset and Modular Fine-tuning Schema (2025.coling-main)

Challenge:	Existing tools to combat cyberbullying mostly use wordlists or lack flexibility to cope with the evolving nature of social media.
Approach:	BullStop is a mobile app for detecting and preventing cyberbullying and online abuse on social media platforms.
Outcome:	BullStop detects and prevents cyberbullying and online abuse on social media platforms and can automatically initiate actions such as deleting offensive messages and blocking bullies on behalf of the user.

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Junru Lu, Siyu An, Min Zhang, Yulan He, Di Yin, Xing Sun

Implicit Sentiment Analysis with Event-centered Text Representation (2021.emnlp-main)

Challenge:	naive prompts can enhance the task performance of large language models, but they are resource-intensive.
Approach:	They propose an automatic prompt optimization method that refines naive prompts according to task outputs from in-box testing models.
Outcome:	The proposed method is based on a large-scale dataset and performed fairly across multiple models.

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Deyu Zhou, Jianan Wang, Linhai Zhang, Yulan He

Document-Level Multi-Event Extraction with Event Proxy Nodes and Hausdorff Distance Minimization (2023.acl-long)

Challenge:	Existing methods for implicit sentiment analysis simply view noun phrases or entities in text as events or indirectly model events with sophisticated models.
Approach:	They propose an event-centric implicit sentiment analysis that utilizes the sentiment-aware event contained in a sentence to infer sentiment polarity.
Outcome:	The proposed model can detect sentiment in sentences without sentiment words and is compared to existing models on a benchmark dataset.

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Xinyu Wang, Lin Gui, Yulan He

SCOPE: Optimizing Key-Value Cache Compression in Long-context Generation (2025.acl-long)

Challenge:	Document-level multi-event extraction aims to extract the structural information from a given document automatically.
Approach:	They propose an alternative approach for document-level multi-event extraction with event proxy nodes and Hausdorff distance minimization.
Outcome:	The proposed method outperforms state-of-the-art methods on two datasets with only a fraction of training time.

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Jialong Wu, Zhenglin Wang, Linhai Zhang, Yilong Lai, Yulan He, Deyu Zhou

Natural Language Inference with Self-Attention for Veracity Assessment of Pandemic Claims (2022.naacl-main)

Challenge:	Excessive compression during the prefill phase impairs comprehension of reasoning tasks . SCOPE is a framework that performs KV cache optimization during the decoding and prefill phases .
Approach:	They propose a framework that performs optimization during the prefill and decoding phases . they propose enabling a sliding strategy to select essential heavy hitters for the decoding phase .
Outcome:	Experiments show that SCOPE can optimize key-value cache for long-context generation tasks . the framework can preserve essential information while minimizing memory usage and transfer .

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Miguel Arana-Catania, Elena Kochkina, Arkaitz Zubiaga, Maria Liakata, Robert Procter, Yulan He

Hatevolution: What Static Benchmarks Don’t Tell Us (2025.findings-acl)

Challenge:	Existing datasets focus on a single medium, information domain or specific application . authors propose novel methods for automated veracity assessment based on Natural Language Inference .
Approach:	They propose to build a PANACEA dataset that combines different data sources with different foci to ensure a unique set of claims.
Outcome:	The proposed methods are competitive with SOTA methods and provide a detailed discussion.

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Chiara Di Bonaventura, Barbara McGillivray, Yulan He, Albert Meroño-Peñuela

Disentangled Learning of Stance and Aspect Topics for Vaccine Attitude Detection in Social Media (2022.naacl-main)

Challenge:	Language evolves rapidly following social dynamics and cultural shifts.
Approach:	They empirically evaluate the robustness of 20 language models across two evolving hate speech experiments and propose time-sensitive benchmarks for their work.
Outcome:	The proposed model evaluations show that the language models are misaligned between static and time-sensitive evaluations.

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Lixing Zhu, Zheng Fang, Gabriele Pergola, Robert Procter, Yulan He

Neural Caption Generation for News Images (L18-1)

Challenge:	Existing approaches to detect vaccine attitudes on social media require abundant annotations and pre-defined aspect categories.
Approach:	They propose a semi-supervised approach to detect vaccine attitudes on social media . they use an autoencoding architecture to learn from unlabelled data the topical information of the domain .
Outcome:	The proposed model outperforms existing aspect-based models on stance detection and tweet clustering.

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Vishwash Batra, Yulan He, George Vogiatzis

Visual Data in NLP

Challenge:	Existing methods for automatic caption generation of images are lacking in the field of image-related applications.
Approach:	They propose a method for automatically generating captions for news images . they propose several deep neural network architectures built upon Recurrent Neural Networks .
Outcome:	The proposed method outperforms a traditional method on a BBC News dataset using automatic evaluation and human evaluation.

CWTM: Leveraging Contextualized Word Embeddings from BERT for Neural Topic Modeling (2024.lrec-main)

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Zheng Fang, Yulan He, Rob Procter

Event Temporal Relation Extraction with Bayesian Translational Model (2023.eacl-main)

Challenge:	Existing topic models rely on bag-of-words (BOW) representations to capture word order information.
Approach:	They propose a neural topic model that integrates contextualized word embeddings from BERT to learn the topic vector of a document without BOW information.
Outcome:	The proposed model generates more coherent and meaningful topics compared to existing models while accommodating unseen words in newly encountered documents.

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Xingwei Tan, Gabriele Pergola, Yulan He

Encourage or Inhibit Monosemanticity? Revisit Monosemanticity from a Feature Decorrelation Perspective (2024.emnlp-main)

Challenge:	Existing methods to extract temporal relations between events lack a principled method to incorporate external knowledge.
Approach:	They propose a Bayesian-based method that models the temporal relation representations as latent variables and infers their values via Bayessian inference and translational functions.
Outcome:	The proposed method outperforms existing methods for event temporal relation extraction on three widely used datasets.

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Hanqi Yan, Yanzheng Xiang, Guangyi Chen, Yifei Wang, Lin Gui, Yulan He

Challenge:	Recent studies focus on monosemanticity on its basic units.
Approach:	They propose to revisit monosemanticity from the feature decorrelation perspective and advocate for its encouragement.
Outcome:	The proposed method improves representation diversity and activation sparsity and improves preference alignment performance.

A Neural Generative Model for Joint Learning Topics and Topic-Specific Word Embeddings (2020.tacl-1)

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Lixing Zhu, Yulan He, Deyu Zhou

WebWalker: Benchmarking LLMs in Web Traversal (2025.acl-long)

Challenge:	Experimental results show that the proposed model outperforms word-level embedding methods in word similarity evaluation and word sense disambiguation.
Approach:	They propose a generative model that explores local and global context for joint learning topics and topic-specific word embeddings.
Outcome:	The proposed model outperforms word-level embedding methods in word similarity evaluation and word sense disambiguation.

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Jialong Wu, Wenbiao Yin, Yong Jiang, Zhenglin Wang, Zekun Xi, Runnan Fang, Linhai Zhang, Yulan He, Deyu Zhou, Pengjun Xie, Fei Huang

GraphMind: Interactive Novelty Assessment System for Accelerating Scientific Discovery (2025.emnlp-demos)

Challenge:	Large Language Models (LLMs) have demonstrated impressive capabilities across a wide range of natural language processing tasks.
Approach:	They propose a benchmark to assess the ability of LLMs to perform web traversal by using an explore-critic paradigm.
Outcome:	The proposed framework mimics human-like web navigation through an explore-critic paradigm and demonstrates the effectiveness of RAG combined with WebWalker in real-world scenarios.

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Italo Luis da Silva, Hanqi Yan, Lin Gui, Yulan He

Beyond Cross-Modal Alignment: Measuring and Leveraging Modality Gap in Vision-Language Models (2026.findings-acl)

Challenge:	Existing approaches to literature analysis lack transparency and information retrieval module.
Approach:	GraphMind is an easy-to-use interactive web tool designed to assist users in evaluating novelty of scientific papers or drafted ideas.
Outcome:	GraphMind enables users to capture the main structure of a scientific paper, explore related ideas through various perspectives, and assess novelty via providing verifiable contextual insights.

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Hanqi Yan, Xiangxiang Cui, Lu Yin, Jindong Gu, Paul Pu Liang, Yulan He, Yifei Wang

Multimodality

Challenge:	a recent study shows that vision-language models have modality gaps that persist even in well-aligned models.
Approach:	They propose a modality-dominance score to measure and leverage modality gaps . they propose automatic interpretability metrics to evaluate these features in a scalable manner .
Outcome:	The proposed framework allows for training-free probing and editing methods for understanding model perception across genders and generating adversarial examples.

PROPER: A Progressive Learning Framework for Personalized Large Language Models with Group-Level Adaptation (2025.acl-long)

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Linhai Zhang, Jialong Wu, Deyu Zhou, Yulan He

Neural Storyline Extraction Model for Storyline Generation from News Articles (N18-1)

Challenge:	Personalized large language models (LLMs) aim to tailor outputs to user preferences . however, user data is typically sparse, making it challenging to adapt LLMs to specific user patterns.
Approach:	They propose a progressive learning framework that groups users based on preferences and adapts LLMs in stages.
Outcome:	The proposed approach outperforms SOTA models across multiple tasks.

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Deyu Zhou, Linsen Guo, Yulan He

Weak Reward Model Transforms Generative Models into Robust Causal Event Extraction Systems (2024.emnlp-main)

Challenge:	Existing approaches to storyline generation are domain dependent and cannot deal with unseen event types.
Approach:	They propose a neural network-based approach to extract structured representations and evolution patterns of storylines without using annotated data.
Outcome:	The proposed model outperforms state-of-the-art approaches on accuracy and efficiency on three news corpora and it is based on supervised models.

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Italo Silva, Hanqi Yan, Lin Gui, Yulan He

Robustness in NLP

Challenge:	Existing evaluation metrics that reflect the performance of causal event extraction tasks are poorly reflecting the inherent ambiguity of cause and effect boundaries.
Approach:	They propose to use a weak-to-strong supervision method to train an evaluation model while still achieving high performance in training an RL model.
Outcome:	The proposed method achieves high agreement with human-annotated data while still achieving high performance in training an RL model.

Event-Centric Question Answering via Contrastive Learning and Invertible Event Transformation (2022.findings-emnlp)

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Junru Lu, Xingwei Tan, Gabriele Pergola, Lin Gui, Yulan He

Interpretable Relevant Emotion Ranking with Event-Driven Attention (D19-1)

Challenge:	Existing QA frameworks that use event-centric reasoning are lacking.
Approach:	They propose a novel QA model with contrastive learning and invertible event transformation . they use an invertable transformation matrix to project event vectors into a common event embedding space .
Outcome:	The proposed model achieves 8.4% gain in token-level F1 score and 3.0% gain in Exact Match score on the ESTER dataset.

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Yang Yang, Deyu Zhou, Yulan He, Meng Zhang

A User-Centered, Interactive, Human-in-the-Loop Topic Modelling System (2023.eacl-main)

Challenge:	Existing studies ignore the latent event information in documents . Existing methods for detecting emotions are limited to a few words .
Approach:	They propose to integrate event information into a deep learning architecture to extract relevant emotion ranking models using corpus-level event embeddings and document-level events.
Outcome:	The proposed model performs better than state-of-the-art emotion detection and multi-label approaches on three real-world corpora and interpretable results shed light on the events which trigger certain emotions.

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Zheng Fang, Lama Alqazlan, Du Liu, Yulan He, Rob Procter

An Interpretable Neural Network with Topical Information for Relevant Emotion Ranking (D18-1)

Challenge:	Recent research has demonstrated the value of user feedback, but there are still issues to consider, such as the difficulty in tracking changes and comparing different models.
Approach:	They propose a human-in-the-loop topic modeling system that integrates users’ knowledge into the modelling process, enabling them to refine the model iteratively.
Outcome:	The proposed system is based on a series of user studies to assess its performance in progressively more realistic applications.

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Yang Yang, Deyu Zhou, Yulan He

Causal Inference from Text: Unveiling Interactions between Variables (2023.findings-emnlp)

Challenge:	Experimental results show that the extracted emotion-associated topic words represent emotion-evoking events.
Approach:	They propose an interpretable neural network approach for relevant emotion ranking . they initialize the hidden layer to approximate the behavior of topic models .
Outcome:	The proposed approach performs better than state-of-the-art methods on real-world corpora.

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Yuxiang Zhou, Yulan He

Challenge:	Existing methods for estimating causal effects from text only account for latent covariates that affect both treatment and outcome.
Approach:	They propose to disentangle non-confounding covariates from text to minimize selection bias . they conduct experiments on two different treatment factors under various scenarios .
Outcome:	The proposed model outperforms strong baselines on earnings call transcripts . the proposed model is based on a randomized controlled trial .

Neural Topic Model with Reinforcement Learning (D19-1)

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

Disentangling Aspect and Stance via a Siamese Autoencoder for Aspect Clustering of Vaccination Opinions (2023.findings-acl)

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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Lixing Zhu, Runcong Zhao, Gabriele Pergola, Yulan He

Addressing Order Sensitivity of In-Context Demonstration Examples in Causal Language Models (2024.findings-acl)

Challenge:	Existing approaches to mining public opinions about vaccines from social media make direct usage of supervision information constraining the models to predefined aspect classes while still not distinguishing those aspects from users’ stances.
Approach:	They propose a model for vaccination opinion mining from social media that disentangles users’ stances from opinions via a disentangling attention mechanism and a Swapping-Autoencoder.
Outcome:	The proposed model outperforms existing methods on aspect-based opinion mining.

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Yanzheng Xiang, Hanqi Yan, Lin Gui, Yulan He

Mirror: Multiple-perspective Self-Reflection Method for Knowledge-rich Reasoning (2024.acl-long)

Challenge:	In-context learning is a popular paradigm in natural language processing, but its performance can be significantly influenced by the order of in-concept demonstration examples.
Approach:	They propose an unsupervised fine-tuning method to reduce the sensitivity of causal language models to the order of in-context demonstration examples.
Outcome:	The proposed method reduces the sensitivity of CausalLMs to the order of in-context examples and exhibits robust generalizability.

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Hanqi Yan, Qinglin Zhu, Xinyu Wang, Lin Gui, Yulan He

PHEE: A Dataset for Pharmacovigilance Event Extraction from Text (2022.emnlp-main)

Challenge:	Large language models (LLMs) struggle with knowledge-rich problems without external resources.
Approach:	They propose a Multiple-perspective self-reflection method that allows LLMs to reflect from multiple-perceptive clues, achieved through a heuristic interaction between a Navigator and a Reasoner.
Outcome:	The proposed method is superior to other self-reflection methods on five reasoning datasets.

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Zhaoyue Sun, Jiazheng Li, Gabriele Pergola, Byron Wallace, Bino John, Nigel Greene, Joseph Kim, Yulan He

Assessing the Reasoning Capabilities of LLMs in the context of Evidence-based Claim Verification (2025.findings-acl)

Challenge:	Using NLP methods to discover and extract adverse drug events from unstructured textual data is difficult because it requires time-consuming manual curation.
Approach:	They propose to use a hierarchical event schema to extract annotated events from medical case reports and biomedical literature to analyze patient data.
Outcome:	The proposed dataset is the largest public dataset to date and contains over 5000 events from medical case reports and biomedical literature.

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John Dougrez-Lewis, Mahmud Elahi Akhter, Federico Ruggeri, Sebastian Löbbers, Yulan He, Maria Liakata

Cascading Large Language Models for Salient Event Graph Generation (2025.naacl-long)

Challenge:	Large Language Models (LLMs) have shown remarkable proficiency in complex tasks where reasoning capabilities are paramount.
Approach:	They propose a framework to break down claims into atomic reasoning types needed for verification.
Outcome:	The proposed framework breaks down claims into atomic reasoning types needed for verification.

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Xingwei Tan, Yuxiang Zhou, Gabriele Pergola, Yulan He

Drift: Enhancing LLM Faithfulness in Rationale Generation via Dual-Reward Probabilistic Inference (2025.acl-long)

Challenge:	Existing studies on event graph generation rely on distant supervision for event graphs .
Approach:	They propose a CAscading Large Language Model framework for SAlient Event graph generation which leverages the capabilities of LLMs and eliminates the need for costly human annotations.
Outcome:	The proposed method outperforms baseline models on a human-annotated test set.

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Jiazheng Li, Hanqi Yan, Yulan He

Content-Based Conflict of Interest Detection on Wikipedia (L18-1)

Challenge:	Existing approaches to improving LLM faithfulness rely on superficial calibration methods or costly retraining.
Approach:	They propose a probabilistic inference paradigm that leverages task-specific and lookahead rewards to ensure that LLM-generated rationales are more faithful to model decisions.
Outcome:	The proposed model improves both accuracy and faithfulness of Large Language Models (LLMs) on three reasoning tasks.

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Udochukwu Orizu, Yulan He

Text Classification

Challenge:	Conflict-of-Interest (CoI) editing is a problem on Wikipedia that is highly subjective . a key feature of Wiki sites is to allow people from all over the world to add or modify articles anonymously and without consequence.
Approach:	They frame CoI detection as a binary classification problem and explore features for it . they find that stylometric features outperform other types of features and give an F-measure of 0.63 .
Outcome:	The proposed method outperforms other features and gives an F-measure of 0.63 . the proposed method is not certain that the set of non-CoI articles contains any CoI articles .

Knowledge Graphs for Real-World Rumour Verification (2024.lrec-main)

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John Dougrez-Lewis, Elena Kochkina, Maria Liakata, Yulan He

Knowledge Representation

Challenge:	Recent advances in automated rumour verification have limited results in real-world scenarios.
Approach:	They propose to use Twitter responses to construct knowledge graphs based on the PHEME dataset to identify discrepancies between the evidence retrieved and PHE ME’s labels.
Outcome:	The proposed model outperforms the state-of-the-art on PHEME and has superior generisability when evaluated on a temporally distant rumour verification dataset.

Set-Aligning Framework for Auto-Regressive Event Temporal Graph Generation (2024.naacl-long)

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Xingwei Tan, Yuxiang Zhou, Gabriele Pergola, Yulan He

Multimodality

Challenge:	Existing methods for constructing event temporal graphs have been suboptimal . authors propose a set-aligning framework for the effective utilisation of Large Language Models .
Approach:	They propose a set-aligning framework for the effective utilisation of Large Language Models to alleviate text generation loss penalties.
Outcome:	The proposed framework surpasses existing baselines for event temporal graph generation.

Boosting Low-Resource Biomedical QA via Entity-Aware Masking Strategies (2021.eacl-main)

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Gabriele Pergola, Elena Kochkina, Lin Gui, Maria Liakata, Yulan He

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

Challenge:	Biomedical question-answering (QA) provides users with high-quality information from a vast scientific literature.
Approach:	They propose to use a biomedical entity-aware masking strategy to fine-tune masked language models to their domains.
Outcome:	The proposed approach is an adaptation process for masked LMs, not memory or components.

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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.

Rehearse With User: Personalized Opinion Summarization via Role-Playing based on Large Language Models (2025.findings-acl)

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Yanyue Zhang, Yulan He, Deyu Zhou

Summarization

Challenge:	Recent studies show that large language models can achieve stateof-the-art performance on standard summarization benchmarks without the need for large-scale training data.
Approach:	They propose a personalized opinion summarization framework via LLM-based role-playing to better understand the user's personalized needs.
Outcome:	The proposed framework can improve the level of personalization in large model-generated summaries by taking into account user characteristics and interests while summarizing multiple product reviews.

SymbolicThought: Integrating Language Models and Symbolic Reasoning for Consistent and Interpretable Human Relationship Understanding (2026.acl-demo)

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Runcong Zhao, Qinglin Zhu, Hainiu Xu, Bin Liang, Lin Gui, Yulan He

Towards Unified Task Embeddings Across Multiple Models: Bridging the Gap for Prompt-Based Large Language Models and Beyond (2024.findings-acl)

Challenge:	Existing methods for constructing character relationships from plain text are time-consuming and low in coverage.
Approach:	They propose a human-in-the-loop framework that combines LLM-based extraction with symbolic reasoning.
Outcome:	The proposed framework improves annotation accuracy and consistency while significantly reducing time cost.

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Xinyu Wang, Hainiu Xu, Lin Gui, Yulan He

Neural Temporal Opinion Modelling for Opinion Prediction on Twitter (2020.acl-main)

Challenge:	Existing task embedding methods rely on fine-tuned, task-specific language models, which hinders their adaptability to prompt-guided Large Language Models (LLMs).
Approach:	They propose a framework for unified task embedding that harmonizes task embeds from various models within a single vector space.
Outcome:	The proposed framework harmonizes task embeddings from various models within a single vector space.

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Lixing Zhu, Yulan He, Deyu Zhou

A Query-Driven Topic Model (2021.findings-acl)

Challenge:	Existing studies have used a manual segmentation of a tweet sequence into equallyspaced intervals based on either tweet counts or time duration.
Approach:	They propose to model users’ tweet posting behaviour as a temporal point process to jointly predict the posting time and the stance label of the next tweet given a user’s historical tweet sequence and tweets posted by their neighbours.
Outcome:	The proposed model predicts the posting time and the stance labels of future tweets more accurately compared to baselines.

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Zheng Fang, Yulan He, Rob Procter

Distinguishability Calibration to In-Context Learning (2023.findings-eacl)

Challenge:	Topic modeling is an unsupervised method for revealing the hidden semantic structure of a corpus.
Approach:	They propose a query-driven topic model that allows users to specify a simple query in words or phrases and return query-related topics.
Outcome:	The proposed model is particularly attractive when the query has a low occurrence in a text corpus, making it difficult for traditional topic models to identify relevant topics.

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Hongjing Li, Hanqi Yan, Yanran Li, Li Qian, Yulan He, Lin Gui

A Multi-label Multi-hop Relation Detection Model based on Relation-aware Sequence Generation (2021.findings-emnlp)

Challenge:	Recent studies have shown that pre-trained language models generate similar output embeddings which makes it difficult to discriminate for the prompt-based classifier.
Approach:	They propose a calibration method which rotates the embedding feature into a new metric space and adapts the ratio of each dimension to a uniform distribution.
Outcome:	The proposed method improves the distinguishability of learning embeddings on three datasets under various settings.

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Linhai Zhang, Deyu Zhou, Chao Lin, Yulan He

Knowledge Representation

Challenge:	Existing methods treat multi-label learning problem as a single label . Existing approaches focus on measuring semantic similarity of questions and candidate relations .
Approach:	They propose to solve multi-hop relation detection problem by generating sequences of hops and labels.
Outcome:	The proposed method is effective in KBQA, despite the unknown number of labels and hops.

RSTGen: Imbuing Fine-Grained Interpretable Control into Long-FormText Generators (2022.naacl-main)

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Rilwan Adewoyin, Ritabrata Dutta, Yulan He

A Disentangled Adversarial Neural Topic Model for Separating Opinions from Plots in User Reviews (2021.naacl-main)

Challenge:	Using a framework based on Rhetorical Structure Theory, we aim to improve the cohesion and coherence of long-form text generated by language models.
Approach:	They propose a framework that utilises Rhetorical Structure Theory to control the discourse structure, semantics and topics of generated text.
Outcome:	The proposed framework performs competitively against existing models while offering significantly more controls over generated text than alternative methods.

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Gabriele Pergola, Lin Gui, Yulan He

Robustness in NLP

Challenge:	Existing topic models may extract topics associated with writers’ subjective opinions mixed with those related to factual descriptions.
Approach:	They propose a neural topic model combined with adversarial training to disentangle opinion topics from plot and neutral ones.
Outcome:	The proposed model shows improved coherence and variety of topics, consistent disentanglement rate, and superior sentiment classification performance to other supervised topic models.

Beyond Meta-Reasoning: Metacognitive Consolidation for Self-Improving LLM Reasoning (2026.acl-long)

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Ziqing Zhuang, Linhai Zhang, Jiasheng Si, Deyu Zhou, Yulan He

Are NLP Models Good at Tracing Thoughts: An Overview of Narrative Understanding (2023.findings-emnlp)

Challenge:	Existing approaches for improving LLM reasoning remain episodic and lack reusable meta-reasoning skills.
Approach:	They propose a framework that consolidates metacognitive experience from past reasoning episodes into reusable knowledge that improves future meta-reasoning.
Outcome:	The proposed framework consolidates metacognitive experience from past reasoning episodes into reusable knowledge that improves future meta-reasoning.

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Lixing Zhu, Runcong Zhao, Lin Gui, Yulan He

Challenge:	Large language models (LLMs) excel in generating coherent texts, but their ability to comprehend the author’s thoughts remains uncertain.
Approach:	They conduct a comprehensive survey of narrative understanding tasks, examining their key features, definitions, taxonomy, associated datasets, evaluation metrics, and limitations.
Outcome:	The proposed framework could be extended to address novel narrative understanding tasks.

Learning Disentangled Latent Topics for Twitter Rumour Veracity Classification (2021.findings-acl)

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John Dougrez-Lewis, Maria Liakata, Elena Kochkina, Yulan He

Text Classification

Challenge:	Existing approaches to rumour veracity classification relied on feature engineering.
Approach:	They propose a model which disentangles the informational content of a tweet from the manner in which it is written.
Outcome:	The proposed model disentangles the informational content of a tweet from the manner in which the information is written.

PANACEA: An Automated Misinformation Detection System on COVID-19 (2023.eacl-demo)

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Runcong Zhao, Miguel Arana-catania, Lixing Zhu, Elena Kochkina, Lin Gui, Arkaitz Zubiaga, Rob Procter, Maria Liakata, Yulan He

LASS: A Novel and Economical Data Augmentation Framework Based on Language Models for Debiasing Opinion Summarization (2025.coling-main)

Challenge:	Using social media and fact-checking to detect misinformation is not enough to prevent the spread of false information.
Approach:	They propose a web-based misinformation detection system PANACEA which has two modules, fact-checking and rumour detection.
Outcome:	The system outperforms state-of-the-art methods and adapts graph convolutional networks model to detect rumours based on tweets rather than knowledge bases.

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Yanyue Zhang, Pengfei Li, Yilong Lai, Yulan He, Deyu Zhou

Opinions Are Not Always Positive: Debiasing Opinion Summarization with Model-Specific and Model-Agnostic Methods (2024.lrec-main)

Challenge:	Existing methods to generate negative summaries are expensive and lack the capacity to generate large data sets.
Approach:	They propose a data augmentation framework based on LArge and Small language models for debiaSing opinion summarization that generates a small number of synthesized negative reviews by rewriting the positive text via a large language model.
Outcome:	The proposed framework can generate large numbers of negative reviews by rewriting the positive text using a large language model and training a disentangle reconstruction model based on the generated data.

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Yanyue Zhang, Yilong Lai, Zhenglin Wang, Pengfei Li, Deyu Zhou, Yulan He

CHIME: Cross-passage Hierarchical Memory Network for Generative Review Question Answering (2020.coling-main)

Challenge:	Existing opinion summarization frameworks are reluctant to generate negative summaries given input of negative opinions.
Approach:	They propose to disentangle input into sentiment-relevant and sentiment-irrelevant components through adversarial loss.
Outcome:	The proposed approaches reduce sentiment bias in the existing opinion summarization dataset . the proposed approaches generate better summaries with a more balanced emotional polarity distribution .

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Junru Lu, Gabriele Pergola, Lin Gui, Binyang Li, Yulan He

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

Challenge:	Existing QA systems deal with factoid questions and assume a simplified setup such as multiple-choice questions, retrieving spans of text from given documents, and filling in the blanks.
Approach:	They propose a cross-passage hierarchical memory network for question answering via text generation that extends XLNet by adding an auxiliary memory module to the context memory and answer memory.
Outcome:	The proposed architecture outperforms the state-of-the-art generative QA framework with better syntactically well-formed answers and increased precision on the AmazonQA review dataset.

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

Multimodality

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.

Neural Topic Modeling with Bidirectional Adversarial Training (2020.acl-main)

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Rui Wang, Xuemeng Hu, Deyu Zhou, Yulan He, Yuxuan Xiong, Chenchen Ye, Haiyang Xu