Papers by Jing Lu

HYRR: Hybrid Infused Reranking for Passage Retrieval (2024.lrec-main)

Challenge:	Existing methods for supplementing Large Language Models (LLMs) with knowledge graphs often introduce noise in the retrieval and reasoning pipeline, hindering their ability to integrate external knowledge for complex multi-hop question answering.
Approach:	They propose a framework to enhance LLMs' reasoning capabilities through reflective engagement with knowledge graphs by Query Decoupling, LLM-Driven Knowledge Graph Exploration, and Inference with Knowledge Reconstruction.
Outcome:	The proposed framework integrates external knowledge into LLMs and trains them to leverage this knowledge for answering questions.

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Jing Lu, Keith Hall, Ji Ma, Jianmo Ni

Information Retrieval

Challenge:	Existing passage retrieval systems typically adopt a two-stage retrieve-then-rerank pipeline.
Approach:	They propose a framework for training robust reranking models using hybrid retrievers . they propose HYRR framework that allows users to select training data using hybrids .
Outcome:	The proposed framework is robust to different first-stage retrieval settings.

HyperCL: A Contrastive Learning Framework for Hyper-Relational Knowledge Graph Embedding with Hierarchical Ontology (2024.findings-acl)

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Yuhuan Lu, Weijian Yu, Xin Jing, Dingqi Yang

Dynamic Online Conversation Recommendation (2020.acl-main)

Challenge:	Existing studies neglect the ontology of knowledge Graph (KG) embeddings and suffer from the dominance issue of facts over ontologies.
Approach:	They propose a framework for hyper-relational KG embeddings that captures the hierarchical ontology and a concept-aware contrastive loss to alleviate the dominance issue.
Outcome:	The proposed framework improves on three real-world datasets and shows that it can integrate with other embedding methods and improve link prediction performance.

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Xingshan Zeng, Jing Li, Lu Wang, Zhiming Mao, Kam-Fai Wong

EmoHarbor: Evaluating Personalized Emotional Support by Simulating the User’s Internal World (2026.acl-long)

Challenge:	Existing models that assume static user interests are unable to capture the temporal aspects of user interactions and interest changes over time.
Approach:	They propose a neural architecture to exploit changes of user interactions and interests over time to predict which discussions they are likely to enter.
Outcome:	The proposed model outperforms state-of-the-art models that assume static user interests and handle future conversations that are unseen during training time.

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Jing Ye, Lu Xiang, Yaping Zhang, Chengqing Zong

Natural Language Interfaces

Challenge:	EmoHarbor is an evaluation framework that rewards generic empathetic responses but fails to assess whether the support is genuinely personalized to users’ unique psychological profiles and contextual needs.
Approach:	They propose an automated evaluation framework that adopts a User-as-a-Judge paradigm by simulating the user's inner world.
Outcome:	The proposed framework decomposes users' internal processes into three specialized roles and defines 10 evaluation dimensions of personalized support quality.

SynC-LLM: Generation of Large-Scale Synthetic Circuit Code with Hierarchical Language Models (2025.emnlp-main)

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Shang Liu, Yao Lu, Wenji Fang, Jing Wang, Zhiyao Xie

UNComp: Can Matrix Entropy Uncover Sparsity? — A Compressor Design from an Uncertainty-Aware Perspective (2025.emnlp-main)

Challenge:	Recent years, AI-assisted integrated circuit design methods have shown great potential in boosting IC design efficiency. however, this emerging technique is limited by the serious scarcity of publicly accessible large-scale circuit design data, which are mostly private IPs owned by semiconductor companies.
Approach:	They propose a hierarchical framework that exploits LLM's ability to generate new large-scale synthetic digital circuits by learning sequential logic skeletons and annotating function descriptions.
Outcome:	The proposed framework generates large-scale synthetic circuits that are valid and fully functional, and can significantly improve AI models’ performance in multiple IC design tasks.

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Jing Xiong, Jianghan Shen, Fanghua Ye, Chaofan Tao, Zhongwei Wan, Jianqiao Lu, Xun Wu, Chuanyang Zheng, Zhijiang Guo, Min Yang, Lingpeng Kong, Ngai Wong

Reasoning

Challenge:	Deploying large language models (LLMs) for long-context inference remains challenging due to their substantial memory and computational demands.
Approach:	They propose an uncertainty-aware framework that leverages truncated matrix entropy to identify areas of low information content.
Outcome:	The proposed framework reduces the KV cache size to 4.74% of the original and achieves a 6% speedup.

Trajectory2Task: Training Robust Tool-Calling Agents with Synthesized Yet Verifiable Data for Complex User Intents (2026.acl-long)

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Ziyi Wang, Yuxuan Lu, Yimeng Zhang, Pei Chen, Ziwei Dong, Jing Huang, Jiri Gesi, Xianfeng Tang, Chen Luo, Qun Liu, Yisi Sang, Hanqing Lu, Manling Li, Jin Lai, Dakuo Wang

From Generic Empathy to Personalized Emotional Support: A Self-Evolution Framework for User Preference Alignment (2025.findings-emnlp)

Challenge:	Tool-calling agents are increasingly deployed in real-world customer-facing workflows . but most studies on tool-callers focus on idealized settings with general, fixed, and well-specified tasks.
Approach:	They propose a tool-calling agent-based data pipeline that converts trajectories into user-facing tasks with controlled intent adaptations.
Outcome:	The proposed pipeline can be used to study tool use under three scenarios.

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Jing Ye, Lu Xiang, Yaping Zhang, Chengqing Zong

Challenge:	Large Language Models (LLMs) show great potential for expressing empathy, but often deliver generic responses that fail to address users’ specific needs.
Approach:	They propose a self-evolution framework to help LLMs improve their responses to better align with users’ implicit preferences concerning personality, emotional state, and specific context.
Outcome:	The proposed model significantly improves the model's performance in emotional support, reducing unhelpful responses and minimizing discrepancies between user preferences and model outputs.

Value Compass Benchmarks: A Comprehensive, Generative and Self-Evolving Platform for LLMs’ Value Evaluation (2025.acl-demo)

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Jing Yao, Xiaoyuan Yi, Shitong Duan, Jindong Wang, Yuzhuo Bai, Muhua Huang, Yang Ou, Scarlett Li, Peng Zhang, Tun Lu, Zhicheng Dou, Maosong Sun, James Evans, Xing Xie

Challenge:	Current evaluation methods for large language models face two key challenges: 1. evaluation validity and 2. Result interpretation reduce the pluralistic and incommensurable values to one-dimensional scores.
Approach:	They propose a platform for comprehensive value diagnosis of large language models (LLMs) that provides a generative evaluation paradigm that automatically creates real-world test items co-evolving with ever-advancing LLMs.
Outcome:	The proposed platform provides a framework for comprehensive value diagnosis of large language models (LLMs) with fine-grained scores and case studies across 27 value dimensions for 33 leading LLMs, customized comparisons, and visualized analysis of LLM’s alignment with cultural values.

Adaptive Detoxification: Safeguarding General Capabilities of LLMs through Toxicity-Aware Knowledge Editing (2025.findings-acl)

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Yifan Lu, Jing Li, Yigeng Zhou, Yihui Zhang, Wenya Wang, Xiucheng Li, Meishan Zhang, Fangming Liu, Jun Yu, Min Zhang

Challenge:	Existing knowledge editing methods for large language models (LLMs) suffer from over-editing, where detoxified models reject legitimate queries, compromising overall performance.
Approach:	They propose a toxicity-aware knowledge editing approach that dynamically detects toxic activation patterns during forward propagation and then routes computations through adaptive inter-layer pathways to mitigate toxicity effectively.
Outcome:	The proposed method outperforms existing methods on large language models and enhances the SafeEdit benchmark.

SweetieChat: A Strategy-Enhanced Role-playing Framework for Diverse Scenarios Handling Emotional Support Agent (2025.coling-main)

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Jing Ye, Lu Xiang, Yaping Zhang, Chengqing Zong

Improving Event Coreference Resolution by Learning Argument Compatibility from Unlabeled Data (N19-1)

Challenge:	Large Language Models (LLMs) have demonstrated promising potential in providing empathetic support during interactions, but their responses are often verbose or overly formulaic, failing to adequately address the diverse emotional support needs of real-world scenarios.
Approach:	They propose a strategy-enhanced role-playing framework that emulates real-world interactions and a dataset that is used to develop an emotional support agent.
Outcome:	The proposed framework emulates real-world interactions and promotes a broader range of dialogues and Emotional Support Agent training.

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Yin Jou Huang, Jing Lu, Sadao Kurohashi, Vincent Ng

Towards Universal Debiasing for Language Models-based Tabular Data Generation (2025.findings-emnlp)

Challenge:	Argument compatibility is a linguistic condition that is often used in event coreference resolution systems.
Approach:	They propose a transfer learning framework that uses unlabeled data to learn argument compatibility of event mentions.
Outcome:	The proposed model improves the performance of the overall event coreference model on the English dataset.

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Tianchun Li, Tianci Liu, Xingchen Wang, Rongzhe Wei, Pan Li, Lu Su, Jing Gao

Can LLM Agents Simulate Multi-Turn Human Behavior? Evidence from Real Online Customer Behavior Data (2026.acl-long)

Challenge:	Existing large language models have exacerbated fairness issues in tabular data generation . inherent historical biases in tabulated data cause LLMs to exacerbate fairness problems .
Approach:	They propose a universal debiasing framework that minimizes group-level dependencies . it leverages the autoregressive structure and analytic sampling distributions of LLM-based tabular data generators .
Outcome:	The proposed framework minimizes group-level dependencies while reducing mutual information between advantaged and protected attributes.

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Yuxuan Lu, Jing Huang, Yan Han, Bingsheng Yao, Sisong Bei, Yaochen Xie, Yisi Sang, Qi He, Dakuo Wang

Doctor Recommendation in Online Health Forums via Expertise Learning (2022.acl-long)

Challenge:	Recent research shows that LLM Agents can generate “believable” human behaviors via prompt-only methods, leaving open questions of whether they can accurately generate step-by-step actions in multi-turn interaction tasks.
Approach:	They propose to use shopping data to evaluate LLMs' ability to accurately generate step-by-step actions in a multi-turn interaction task.
Outcome:	The proposed model achieves 17.26% action generation accuracy and 33.86% F1 score on final purchase prediction, representing improvements of 5.4% and 13.85% over baselines.

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Xiaoxin Lu, Yubo Zhang, Jing Li, Shi Zong

Discrete Argument Representation Learning for Interactive Argument Pair Identification (2021.naacl-main)

Challenge:	Currently, manual doctor allocations are used to handle large volumes of queries, limiting the efficiency to help patients in sheer quantities.
Approach:	They propose to use patient queries to model doctor recommendation using their profiles and past dialogues to estimate their capabilities.
Outcome:	The proposed model outperforms baseline models on a Chinese online health forum, outperforming baseline models.

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Lu Ji, Zhongyu Wei, Jing Li, Qi Zhang, Xuanjing Huang

Representation Learning

Challenge:	Existing research on monological argumentation covers claims generation, argument structure prediction, and essay scoring.
Approach:	They propose to identify argument pairs from two posts with opposite stances to a certain topic.
Outcome:	The proposed framework outperforms competing models on a large-scale dataset . it also proves that it is useful for analyzing argument pairs from two posts .

OPeRA: A Dataset of Observation, Persona, Rationale, and Action for Evaluating LLMs on Human Online Shopping Behavior Simulation (2026.acl-long)

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Ziyi Wang, Yuxuan Lu, Wenbo Li, Amirali Amini, Bo Sun, Yakov Bart, Weimin Lyu, Jiri Gesi, Tian Wang, Jing Huang, Yu Su, Upol Ehsan, Malihe Alikhani, Toby Jia-Jun Li, Lydia Chilton, Dakuo Wang

Challenge:	evaluating LLMs' ability to mimic real user behavior remains an open challenge due to the lack of high-quality, publicly available datasets that capture both the observable actions and the internal reasoning of an actual user.
Approach:	They propose a dataset of Observation, Persona, Rationale, and Action collected from real human participants during online shopping sessions.
Outcome:	The proposed dataset is the first to evaluate how well current LLMs can accurately simulate the next web action of a specific user.

StereoRel: Relational Triple Extraction from a Stereoscopic Perspective (2021.acl-long)

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Xuetao Tian, Liping Jing, Lu He, Feng Liu

Event Coreference Resolution with Non-Local Information (2020.aacl-main)

Challenge:	Existing methods for relational triple extraction still face challenges, including information loss and error propagation.
Approach:	They propose a model which maps relational triples to a three-dimensional space and leverages three decoders to extract them.
Outcome:	The proposed model outperforms the baselines on five public datasets.

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Jing Lu, Vincent Ng

LLMs know their vulnerabilities: Uncover Safety Gaps through Natural Distribution Shifts (2025.acl-long)

Challenge:	Existing joint models for event coreference resolution are understudied and underexploited . current models only learn trigger detection and event coreference from annotated training data .
Approach:	They propose to add a topic-based trigger detection module and a preprocessing module to improve event coreference.
Outcome:	The proposed model yields the best results on the KBP 2017 English and Chinese datasets.

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Qibing Ren, Hao Li, Dongrui Liu, Zhanxu Xie, Xiaoya Lu, Yu Qiao, Lei Sha, Junchi Yan, Lizhuang Ma, Jing Shao

Layer-Aware Representation Filtering: Purifying Finetuning Data to Preserve LLM Safety Alignment (2025.emnlp-main)

Challenge:	Current safety training focuses on teaching models to reject harmful queries, but recent research shows that adversarial attacks or jailbreak methods bypass these safety mechanisms.
Approach:	They propose to use a new attack method to craft multi-turn toxic prompts that gradually lead LLMs to reveal unsafe content.
Outcome:	The proposed method outperforms existing methods in diversity, effectiveness, and efficiency across aligned LLMs.

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Hao Li, Lijun Li, Zhenghao Lu, Xianyi Wei, Rui Li, Jing Shao, Lei Sha

Microblog Conversation Recommendation via Joint Modeling of Topics and Discourse (N18-1)

Challenge:	Recent studies show that fine-tuning with benign data can compromise safety of aligned LLMs.
Approach:	They propose a Layer-Aware Representation Filtering method that detects safety-degrading layers within the LLM and leverages their representations to detect them.
Outcome:	The proposed method can detect safety-degrading features in benign data and remove them from the model.

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Xingshan Zeng, Jing Li, Lu Wang, Nicholas Beauchamp, Sarah Shugars, Kam-Fai Wong

Large Dual Encoders Are Generalizable Retrievers (2022.emnlp-main)

Challenge:	Existing methods for recommendation focus on content of individual posts, but we exploit both context and user content and behavior preferences.
Approach:	They propose a method that captures conversational context and user content and behavior preferences.
Outcome:	The proposed method outperforms methods that only model content without considering discourse on two Twitter datasets.

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Jianmo Ni, Chen Qu, Jing Lu, Zhuyun Dai, Gustavo Hernandez Abrego, Ji Ma, Vincent Zhao, Yi Luan, Keith Hall, Ming-Wei Chang, Yinfei Yang

X-Boundary: Establishing Exact Safety Boundary to Shield LLMs from Jailbreak Attacks without Compromising Usability (2025.findings-emnlp)

Challenge:	Experimental results show that dual encoders outperform sparse and dense retrievers on the BEIR dataset significantly.
Approach:	They challenge belief that bottleneck layer is too limited for out-of-domain generalization . they scale up the model while keeping bottleneck as a single dot-product with a fixed size .
Outcome:	The proposed model outperforms sparse and dense retrievers on the BEIR dataset significantly.

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Xiaoya Lu, Dongrui Liu, Yi Yu, Luxin Xu, Jing Shao

Neural Conversation Recommendation with Online Interaction Modeling (D19-1)

Challenge:	Existing methods for enhancing LLM security compromise usability, study finds . boundary-safe representations close to harmful representations are disrupted, resulting in usability decline .
Approach:	They propose a method to push harmful representations away from boundary-safe representations and obtain an exact distinction boundary.
Outcome:	The proposed method reduces over-refusal rate and maintains general capability . it pushes harmful representations away from boundary-safe representations, thereby reducing usability.

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Xingshan Zeng, Jing Li, Lu Wang, Kam-Fai Wong

HyperFM: Fact-Centric Multimodal Fusion for Link Prediction over Hyper-Relational Knowledge Graphs (2025.acl-long)

Challenge:	Existing models that only use lexical features and ignore past user interactions in online conversations are inadequate to identify and engage in online discussions.
Approach:	They propose a framework that automatically recommends conversations based on user's prior conversation behaviors by exploring deep semantic features that measure how a user’s preferences match an ongoing conversation’s context.
Outcome:	The proposed model outperforms state-of-the-art models on two large-scale datasets from Twitter and Reddit showing that it incorporates deep semantic features that measure how a user’s preferences match an ongoing conversation’s context.

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Yuhuan Lu, Weijian Yu, Xin Jing, Dingqi Yang

Multimodality

Challenge:	Existing link prediction techniques focus on learning the complex relationships between entities and relations while ignoring the multimodal information.
Approach:	They propose a fact-centric fusion technique that captures complex interactions between different data modalities while accommodating the hyper-relational structure of the KG in a facts-centric manner.
Outcome:	The proposed technique improves on two real-world KG datasets by 6.0-6.8% over baselines.

LLMs Deceive Unintentionally: Emergent Misalignment in Dishonesty from Misaligned Samples to Biased Human-AI Interactions (2026.findings-acl)

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Xuhao Hu, Wang Peng, Xiaoya Lu, Dongrui Liu, Xuanjing Huang, Jing Shao

Optimizing Native Sparse Attention with Latent Attention and Local Global Alternating Strategies (2026.findings-acl)

Challenge:	Existing studies have shown that LLMs finetuned on incorrect completions can exhibit harmful behaviors, which is called emergent misalignment.
Approach:	They investigate whether LLMs finetuned on incorrect completions can exhibit harmful behaviors . they find that 1% of misalignment data is sufficient to decrease honest behavior .
Outcome:	The proposed model can be misaligned on errors within narrow domains to exhibit harmful behaviors . the proposed model is able to exhibit dishonest behavior with only 10% biased user population .

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Yuxuan Hu, Jianchao Tan, Jiaqi Zhang, Wen Zan, Pingwei Sun, Yifan Lu, Xunliang Cai, Jing Zhang

Reasoning

Challenge:	Existing research has proposed a variety of training-free and post-training methods for selecting critical key-value pairs at each generation step.
Approach:	They propose to use local (sliding-window) and global (compression/selective) attention across layers to enlarge long-context modeling.
Outcome:	Experiments on models from 340M to 1.3B parameters show that the proposed method matches or exceeds full attention and native sparse attention in both common-sense reasoning and long-context understanding tasks.

API Is Enough: Conformal Prediction for Large Language Models Without Logit-Access (2024.findings-emnlp)

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Jiayuan Su, Jing Luo, Hongwei Wang, Lu Cheng

Constrained Multi-Task Learning for Event Coreference Resolution (2021.naacl-main)

Challenge:	Existing methods for quantifying uncertainty in large language models with black-box API access are limited due to the complex data distributions and inner model mechanism.
Approach:	They propose a conformal prediction method that minimizes the size of prediction sets and ensures a statistical guarantee of the user-defined coverage.
Outcome:	The proposed method outperforms existing methods on close-ended and open-ended questions.

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Jing Lu, Vincent Ng

Multi-Agent-as-Judge: Aligning LLM-Agent-Based Automated Evaluation with Multi-Dimensional Human Evaluation (2026.acl-long)

Challenge:	a neural event coreference model is based on a task of determining whether two event mentions refer to the same event . event coreferent tasks require nontrivial tasks such as identifying potential arguments and linking arguments to their event mention.
Approach:	They propose a neural event coreference model in which event coreference is jointly trained with five tasks.
Outcome:	The proposed model achieves state-of-the-art on the KBP 2017 event coreference dataset.

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Jiaju Chen, Yuxuan Lu, Xiaojie Wang, Huimin Zeng, Jing Huang, Jiri Gesi, Ying Xu, Bingsheng Yao, Dakuo Wang

Challenge:	Existing "LLM-as-a-judge" evaluation frameworks are limited by persona descriptions and are not generalizable to other tasks.
Approach:	They propose a framework that can automatically construct multiple evaluator personas with distinct dimensions from relevant text documents and instantiate LLM agents with the persona.
Outcome:	The proposed framework can believably simulate human evaluators . it extracts stakeholders' diverse perspectives from the provided research papers and constructs personas for the agents .

Conundrums in Entity Coreference Resolution: Making Sense of the State of the Art (2020.emnlp-main)

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Jing Lu, Vincent Ng

Self-Evaluation of Large Language Model based on Glass-box Features (2024.findings-emnlp)

Challenge:	despite significant progress on entity coreference resolution, there is a general lack of understanding of what has been improved.
Approach:	They present an empirical analysis of entity coreference resolvers to provide an understanding of what has been improved.
Outcome:	The proposed model improves the performance of entity coreference resolvers.

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Hui Huang, Yingqi Qu, Jing Liu, Muyun Yang, Bing Xu, Tiejun Zhao, Wenpeng Lu

ViPE: Visual Perception in Parameter Space for Efficient Video-Language Understanding (2025.emnlp-main)

Challenge:	Existing evaluation methods rely on external evaluators, focusing on training and prompting strategies, but model-aware glass-box features are overlooked.
Approach:	They propose to use model-aware glass-box features to evaluate an LLM's output.
Outcome:	The proposed model-aware features are reliable quality indicators for self-evaluation on public benchmarks.

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Shichen Lu, Tongtian Yue, Longteng Guo, Handong Li, Xingjian He, Si Liu, Jing Liu

Visual Data in NLP

Challenge:	Existing video-language models rely on concatenating visual tokens with textual inputs for joint modeling, but this method suffers from significant inefficiency when scaling to long videos with dense visual inputs.
Approach:	They propose a video-to-parameter efficiency paradigm called ViPE that transforms video content into visual perceptual weights, which are directly injected into the LLM’s parameters.
Outcome:	The proposed model reduces FLOPs by 85% and inference time by up to 65% while reducing FLOP and FLOP inference times by up-to-65%.

Beyond Query Bias: Candidate-Aware Iterative Refinement for Zero-Shot Composed Image Retrieval (2026.findings-acl)

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Nan Sun, Jing Tang, Lei Sun, Rui Chen, Yuxing Lu, Xiangxiang Chu, Hefei Ling, Yujun Cai

Multimodality

Challenge:	Existing methods to retrieve target images suffer from inherent cognitive bias due to unknown candidate distribution.
Approach:	They propose a training-free framework that reframes ZS-CIR as a self-correcting process . they propose to use retrieved results as feedback to perceive the candidate distribution .
Outcome:	Experiments on public benchmarks show that CoRR outperforms other SOTA methods.

Pre-trained Semantic Interaction based Inductive Graph Neural Networks for Text Classification (2025.coling-main)

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Shiyu Wang, Gang Zhou, Jicang Lu, Jing Chen, Ningbo Huang

Improving Visual-Semantic Embedding with Adaptive Pooling and Optimization Objective (2023.eacl-main)

Challenge:	Existing methods for text classification have vanishing or exploding gradients when dealing with long sequences, making it difficult to handle long-distance dependencies.
Approach:	They propose a graph neural network based on pre-trained semantic interaction called PaSIG . they construct a text-word heterogeneity graph and use context representation capability .
Outcome:	The proposed model outperforms existing methods on five datasets and achieves state-of-the-art performance.

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Zijian Zhang, Chang Shu, Ya Xiao, Yuan Shen, Di Zhu, Youxin Chen, Jing Xiao, Jey Han Lau, Qian Zhang, Zheng Lu

Visual Data in NLP

Challenge:	Recent VSE models combine simple pooling methods with hard triplet loss to improve performance.
Approach:	They propose an adaptive pooling strategy that allows the model to learn how to aggregate features through a combination of simple pooling methods.
Outcome:	The proposed strategy outperforms current state-of-the-art systems on image-to-text and text-toimage retrieval.

ED2LM: Encoder-Decoder to Language Model for Faster Document Re-ranking Inference (2022.findings-acl)

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Kai Hui, Honglei Zhuang, Tao Chen, Zhen Qin, Jing Lu, Dara Bahri, Ji Ma, Jai Gupta, Cicero Nogueira dos Santos, Yi Tay, Donald Metzler

The BQ Corpus: A Large-scale Domain-specific Chinese Corpus For Sentence Semantic Equivalence Identification (D18-1)

Challenge:	State-of-the-art neural models typically encode document-query pairs using cross-attention for re-ranking.
Approach:	They propose to fine tune a pretrained encoder-decoder model using document to query generation.
Outcome:	The proposed model achieves comparable results to more expensive approaches while being 6.8X faster.

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Jing Chen, Qingcai Chen, Xin Liu, Haijun Yang, Daohe Lu, Buzhou Tang

Joint Effects of Context and User History for Predicting Online Conversation Re-entries (P19-1)

Challenge:	Bank Question corpus is a corpus for sentence semantic equivalence identification (SSEI) because of rich expressions in natural languages, SSEI is really a challenging task.
Approach:	They propose to cluster 120,000 question pairs from 1-year online bank custom service logs into stacks by the Word Mover’s Distance (WMD) based Affinity Propagation algorithm to achieve questions with the same intent.
Outcome:	The proposed method achieves questions with the same intent by clustering deduplicated questions into stacks by the Word Mover’s Distance (WMD) based Affinity Propagation (AP) algorithm.

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Xingshan Zeng, Jing Li, Lu Wang, Kam-Fai Wong

Engage the Public: Poll Question Generation for Social Media Posts (2021.acl-long)

Challenge:	Existing methods for predicting online conversation re-entry focus on modeling engagement patterns in ongoing conversations or ignoring the rich information in users' previous chatting history.
Approach:	They propose a neural framework with three main layers to model the conversation context and user history and their interactions with Twitter and Reddit to predict whether a user will return to a conversation they once participated in.
Outcome:	The proposed framework outperforms the state-of-the-art methods on two large-scale Twitter and Reddit conversations, and achieves an F1 score of 61.1 on Twitter conversations.

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Zexin Lu, Keyang Ding, Yuji Zhang, Jing Li, Baolin Peng, Lemao Liu

Multi-stage Training with Improved Negative Contrast for Neural Passage Retrieval (2021.emnlp-main)

Challenge:	a novel application to generate poll questions for social media posts offers an easy way to hear the public's voice . for the silent majority, they tend to read others' messages instead of voicing their opinions with words .
Approach:	They propose to encode user comments and discover latent topics therein as contexts to generate poll questions for social media posts.
Outcome:	The proposed model outperforms popular models without exploiting topics from comments . human evaluations show it can generate high-quality polls useful to draw user engagements .

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Jing Lu, Gustavo Hernandez Abrego, Ji Ma, Jianmo Ni, Yinfei Yang

Information Retrieval

Challenge:	Existing neural firststage retrieval models overcome lexical gap issue by projecting query and document to a shared dense space.
Approach:	They propose a multi-stage framework for neural passage retrieval using synthetic data, negative sampling, and fusion techniques.
Outcome:	The proposed framework improves retrieval accuracy and enhances the negative contrast in both stages.

Tomato, Tomahto, Tomate: Do Multilingual Language Models Understand Based on Subword-Level Semantic Concepts? (2025.findings-naacl)

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Crystina Zhang, Jing Lu, Vinh Q. Tran, Tal Schuster, Donald Metzler, Jimmy Lin