Papers by Tao Xie
Generative-to-Discriminative Test-Time Adaptation via Manifold-Aware Diffusion and Bayesian Distillation (2026.findings-acl)
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Boyun Zhang, Zequn Xie, Fangming Feng, Zihan Zhang, Yongbo He, Chuxin Wang, Sihang Cai, Tao Jin, Qifei Zhang
| Challenge: | Existing discriminative approaches suffer from "confident but wrong" failure mode, blindly adapting to OOD noise leading to error accumulation. |
| Approach: | They propose a TTA framework that harmonizes the robustness of generative diffusion models with the efficiency of discriminative regression networks via Bayesian Diffusion Distillation (BDD). |
| Outcome: | The proposed framework reduces MAE from 0.6872 to 0.5673 and boosts binary accuracy by 5.81 percentage points (reaching 57.33%) it also reduces the MAE of the MOSI to SIMS shift and achieves an 11.18-point gain over the baseline. |
Lil: Less is Less When Applying Post-Training Sparse-Attention Algorithms in Long-Decode Stage (2026.findings-acl)
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Junhao Hu, Fangze Li, Mingtao Xu, Feifan Meng, Shiju Zhao, Tiancheng Hu, Ting Peng, Anmin Liu, Wenrui Huang, Chenxu Liu, Ziyue Hua, Tao Xie
| Challenge: | Prior work typically decomposes inference into prefill and decode stages, with the decode stage dominating total latency. |
| Approach: | They propose an algorithm that detects threshold where information loss exceeds information gain during sparse decoding to reduce token consumption by up to 90% and a marginal accuracy degradation of less than 2%. |
| Outcome: | The proposed algorithm reduces token consumption by 90% with a marginal accuracy degradation of less than 2% across reasoning-intensive benchmarks. |
Privacy-Preserving News Recommendation Model Learning (2020.findings-emnlp)
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| Challenge: | Existing news recommendation methods rely on centralized storage of user behavior data for model training, which may lead to privacy concerns and risks due to the privacy-sensitive nature of user behaviors. |
| Approach: | They propose a privacy-preserving method where user behavior data is locally stored on user devices to train accurate news recommendation models. |
| Outcome: | The proposed method can train accurate news recommendation models without centralized storage of user behavior data. |
SAC: Neural Speech Codec with Semantic-Acoustic Dual-Stream Quantization (2026.acl-long)
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Wenxi Chen, Ruiqi Yan, Yushen Chen, Zhikang Niu, Ziyang Ma, Xiquan Li, Yuzhe Liang, null Wenhanlin, Shunshun Yin, Ming Tao, Xinsheng Wang, Xie Chen
| Challenge: | Existing speech codecs struggle to balance high-quality reconstruction with semantically rich representations, limiting their effectiveness in both generative and understanding tasks. |
| Approach: | They propose a neural speech codec with semantic-acoustic dual-stream quantization that disentangles semantic and acousian modeling into two dedicated streams. |
| Outcome: | The proposed codec outperforms state-of-the-art speech tokenizers in auto-propagating text-to-speech models. |
Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models (2026.findings-acl)
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Hengyuan Zhang, Zhihao Zhang, Ercong Nie, Mingyang Wang, Zunhai Su, Yiwei Wang, Qianli Wang, Shuzhou Yuan, Xufeng Duan, Qibo Xue, Zeping Yu, Chenming Shang, Xiao Liang, Jing Xiong, Hui Shen, Chaofan Tao, Zhengwu Liu, Senjie Jin, Zhiheng Xi, Dongdong Zhang, Sophia Ananiadou, Tao Gui, Ruobing Xie, Hayden Kwok-Hay So, Hinrich Schuetze, Xuanjing Huang, Qi Zhang, Ngai Wong
| Challenge: | Existing literature on mechanistic interpretation (MI) treats it as an observational science, leaving practical applications underexplored. |
| Approach: | They propose a survey structured around the pipeline to identify and improve MI models. |
| Outcome: | The proposed framework enables tangible improvements in Alignment, Capability, and Efficiency. |
MATO: A Model-Agnostic Training Optimization for Aspect Sentiment Triplet Extraction (2025.naacl-long)
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| Challenge: | Existing models with strong in-house performance may struggle to generalize to diverse expressions. |
| Approach: | They propose a model-agnostic t**raining method to improve ASTE model inference . they propose to compute the violation rate (VR) on each element of one triplet . |
| Outcome: | The proposed method can improve aspect sentiment triplet extraction models consistent with expected results facing triplet element diversity. |
Bridging the Pose-Semantic Gap: A Cascade Framework for Text-Based Person Anomaly Search (2026.findings-acl)
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| Challenge: | Existing methods for person anomaly search fail to address the complexities of real-world security, authors say . Existing approaches fail to detect subtle semantic distinctions, authors argue . |
| Approach: | They propose a framework that decouples retrieval into two stages . structure-aware coarse retrieval and detective squad interaction are proposed . |
| Outcome: | The proposed framework achieves state-of-the-art performance by balancing efficiency and semantic reasoning. |
Uni-FedRec: A Unified Privacy-Preserving News Recommendation Framework for Model Training and Online Serving (2021.findings-emnlp)
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| Challenge: | Existing news recommendation methods rely on user behavior data to model user interests and user interests. |
| Approach: | They propose a unified news recommendation framework that uses user data locally stored in user clients to train models and serve users in a privacy-preserving way. |
| Outcome: | The proposed framework outperforms baseline methods and effectively protects user privacy. |
Decomposition for Enhancing Attention: Improving LLM-based Text-to-SQL through Workflow Paradigm (2024.findings-acl)
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Yuanzhen Xie, Xinzhou Jin, Tao Xie, Matrixmxlin Matrixmxlin, Liang Chen, Chenyun Yu, Cheng Lei, Chengxiang Zhuo, Bo Hu, Zang Li
| Challenge: | In-context learning of large-language models has achieved remarkable success in the field of natural language processing . however, the single-step chain-of-thought prompting approach faces challenges such as attention diffusion and inadequate performance in complex tasks like text-to-SQL. |
| Approach: | They propose a workflow paradigm method to enhance the attention and problem-solving scope of large-language models through decomposition. |
| Outcome: | The proposed method outperforms existing methods on three datasets and improves the upper limit of LLM-based approaches. |
Reviews Meet Graphs: Enhancing User and Item Representations for Recommendation with Hierarchical Attentive Graph Neural Network (D19-1)
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| Challenge: | Existing methods to learn user and item representations from review texts do not take into account the user-user and item-item relatedness of the user. |
| Approach: | They propose to use review content and user-item graphs to integrate them as different views. |
| Outcome: | The proposed approach can learn user and item representations from review content and user-item graphs. |
Towards General Agentic Intelligence via Environment Scaling (2026.findings-acl)
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Runnan Fang, Shihao Cai, Baixuan Li, Jialong Wu, Guangyu Li, Wenbiao Yin, Xinyu Wang, Xiaobin Wang, Liangcai Su, Zhen Zhang, Shibin Wu, Zhengwei Tao, Yong Jiang, Pengjun Xie, Ningyu Zhang, Fei Huang, Wentao Zhang, Jingren Zhou
| Challenge: | Diverse real-world APIs require precise, robust function-calling intelligence, which needs agents to develop these capabilities through interaction in varied environments. |
| Approach: | They propose a framework that scales up environments to enable agentic intelligence . they use a two-phase agent fine-tuning strategy to first endow agents with basic agentic capabilities, then specializing them for domain-specific contexts. |
| Outcome: | Experiments on -bench, -Bench, and ACEBench show that the model significantly enhances the models’ function-calling capability. |
Benchmarking Large Language Models Under Data Contamination: A Survey from Static to Dynamic Evaluation (2025.emnlp-main)
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Simin Chen, Yiming Chen, Zexin Li, Yifan Jiang, Zhongwei Wan, Yixin He, Dezhi Ran, Tianle Gu, Haizhou Li, Tao Xie, Baishakhi Ray
| Challenge: | In the era of evaluating large language models, data contamination is an increasingly prominent concern . static benchmarking has been used for evaluation, but there are limitations of *dynamic* benchmarks . |
| Approach: | They propose a series of optimal design principles for *dynamic* benchmarking and analyze the limitations of existing *static* benchmarks. |
| Outcome: | The proposed benchmarks highlight a critical gap in the evaluation of LLMs. |
Neural News Recommendation with Heterogeneous User Behavior (D19-1)
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| Challenge: | Existing news recommendation methods rely on news click history to model user interest, but data sparsity is a problem . other kinds of user behaviors such as webpage browsing and search queries can provide useful clues of users’ news reading interest. |
| Approach: | They propose to exploit heterogeneous user behaviors to learn news representations from their titles via CNN networks and apply attention networks to select important words. |
| Outcome: | The proposed approach exploits heterogeneous user behaviors on a real-world dataset. |
MMAC: A Multilingual, Multimodal Alignment Framework for Cultural Grounding Evaluation (2026.acl-long)
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Weihua Zheng, Zhengyuan Liu, Tanmoy Chakraborty, Weiwen Xu, Xiaoxue Gao, Bryan Chen Zhengyu Tan, Bowei Zou, Chang Liu, Yujia Hu, Xing Xie, Xiaoyuan Yi, Jing Yao, Chaojun Wang, Long Li, Rui Liu, Huiyao Liu, Koji Inoue, Ryuichi Sumida, Tatsuya Kawahara, Fan Xu, Lingyu Ye, Wei Tian, Dongjun Kim, Jimin Jung, Jaehyung Seo, Nadya Yuki Wangsajaya, Pham Minh Duc, Ojasva Saxena, Palash Nandi, Xiyan Tao, Wiwik Karlina, Tuan Luong, Keertana Arun Vasan, Roy Ka-Wei Lee, Nancy F. Chen
| Challenge: | Existing models lack cultural alignment across modalities and languages . a new framework to assess cultural awareness across linguistics and languages is needed . |
| Approach: | They propose a framework that integrates tri-modally aligned cultural benchmarks and a five-dimensional evaluation protocol to assess cross-country awareness disparities. |
| Outcome: | The proposed framework assesses cultural awareness disparities across modalities and languages . it is the first dataset aligned at the input level across text, image, and speech . |
MolXPT: Wrapping Molecules with Text for Generative Pre-training (2023.acl-short)
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| Challenge: | Experimental results show that Generative pre-trained Transformers (GPT) have great success in natural language processing. |
| Approach: | They propose a unified language model of text and molecules pre-trained on SMILES wrapped by text. |
| Outcome: | The proposed model outperforms strong baselines of molecular property prediction on MoleculeNet and performs comparably to the best model in text-molecule translation while using less than half of its parameters. |
GS-Quant: Granular Semantic and Generative Structural Quantization for Knowledge Graph Completion (2026.acl-long)
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| Challenge: | Existing quantization-based approaches to knowledge Graph Completion (KGC) are incomplete. |
| Approach: | They propose a framework that generates semantically coherent discrete codes for KG entities . they introduce a Granular Semantic Enhancement module that injects hierarchical knowledge into the codebook . |
| Outcome: | The proposed framework outperforms existing text-based and embedding-based baselines in the KGC domain. |
Chain-of-Thought Prompting Obscures Hallucination Cues in Large Language Models: An Empirical Evaluation (2025.findings-emnlp)
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| Challenge: | Chain-of-Thought (CoT) prompting can mitigate hallucinations by encouraging step-by-step reasoning, but its impact on halluciation detection remains underexplored. |
| Approach: | They conduct an empirical evaluation of CoT prompting in Large Language Models (LLMs) to examine their impact on hallucination detection methods. |
| Outcome: | The proposed method significantly affects the internal states and token probability distributions of the LLM. |
VulLibGen: Generating Names of Vulnerability-Affected Packages via a Large Language Model (2024.acl-long)
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Tianyu Chen, Lin Li, ZhuLiuchuan ZhuLiuchuan, Zongyang Li, Xueqing Liu, Guangtai Liang, Qianxiang Wang, Tao Xie
| Challenge: | Existing work on affected package identification is limited by large language models . a recent study shows that 84% third-party packages contain security vulnerabilities . |
| Approach: | They propose a method to use LLM to generate the affected package . they propose supervised fine-tuning, retrieval augmented generation and a local search algorithm . |
| Outcome: | The proposed method has an average precision of 0.806 for identifying vulnerable packages in four most popular ecosystems in GitHub Advisory. |
In-Context Learning for Few-Shot Dialogue State Tracking (2022.findings-emnlp)
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| Challenge: | Existing methods for zero-shot and few-shot learning dialogue state tracking are hard and expensive. |
| Approach: | They propose an in-context learning framework for zero-shot and few-shot learning dialogue state tracking (DST) a large pretrained language model takes a test instance and a few exemplars as input and directly decodes the dialogue state . |
| Outcome: | The proposed framework outperforms state-of-the-art models in few-shot settings . it is flexible and scalable, and requires less data to adapt to new domains and scenarios . |
Making Parameter-efficient Tuning More Efficient: A Unified Framework for Classification Tasks (2022.coling-1)
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Xin Zhou, Ruotian Ma, Yicheng Zou, Xuanting Chen, Tao Gui, Qi Zhang, Xuanjing Huang, Rui Xie, Wei Wu
| Challenge: | Large pre-trained language models (PLMs) have demonstrated superior performance in industrial applications. |
| Approach: | They propose a framework that re-uses existing parameter-efficient methods with a unified classifier. |
| Outcome: | The proposed framework improves the efficiency of existing parameter-efficient methods with a unified classifier. |
Exploring the Secrets Behind the Learning Difficulty of Meaning Representations for Semantic Parsing (2022.emnlp-main)
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| Challenge: | Existing studies show that the design of Meaning Representation (MR) greatly influences the final model performance of a neural semantic parser. |
| Approach: | They propose a data-aware metric called ISS to measure the final performance of MRs. |
| Outcome: | The proposed metric denoting incremental structural stability (ISS) of MRs can be used as an indicator for MR design to avoid the costly training-testing process. |
RaaS: Reasoning-Aware Attention Sparsity for Efficient LLM Reasoning (2025.findings-acl)
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Junhao Hu, Wenrui Huang, Weidong Wang, Zhenwen Li, Tiancheng Hu, Zhixia Liu, Xusheng Chen, Tao Xie, Yizhou Shan
| Challenge: | Large Language Models (LLMs) have demonstrated strong capabilities across various domains, but their large-scale deployment faces a major obstacle: the high computational cost of long-sequence inference. |
| Approach: | They propose an algorithm that retains key-value vectors until they are no longer needed to solve reasoning tasks. |
| Outcome: | The proposed algorithm achieves high accuracy with O(L) time but O(N) memory complexities. |
When to Trust LLMs: Aligning Confidence with Response Quality (2024.findings-acl)
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Shuchang Tao, Liuyi Yao, Hanxing Ding, Yuexiang Xie, Qi Cao, Fei Sun, Jinyang Gao, Huawei Shen, Bolin Ding
| Challenge: | Existing methods express reliability by confidence level, but lack objective guidance . Existing approaches express reliability but lack guidance on when to trust LLMs . |
| Approach: | They propose a reward-based approach to align confidence with quality to ensure reliability . they propose 'conqORD' to help model to verbalize greater confidence for higher quality responses . |
| Outcome: | Experiments show that CONQORD significantly improves confidence and response accuracy . the proposed approach can be used to determine reliability of large language models . |
DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population (2022.emnlp-demos)
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Ningyu Zhang, Xin Xu, Liankuan Tao, Haiyang Yu, Hongbin Ye, Shuofei Qiao, Xin Xie, Xiang Chen, Zhoubo Li, Lei Li
| Challenge: | Existing knowledge extraction tools are not complete due to emerging entities and relations in real-world applications. |
| Approach: | They propose an open-source knowledge extraction toolkit DeepKE that supports low-resource, document-level and multimodal scenarios in the knowledge base population. |
| Outcome: | The proposed toolkit supports low-resource, document-level and multimodal scenarios in the knowledge base population. |
EmoCharacter: Evaluating the Emotional Fidelity of Role-Playing Agents in Dialogues (2025.naacl-long)
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| Challenge: | EmoCharacter evaluates emotional fidelity of role-playing agents in dialogues . current evaluations focus on personality fidelity, tone imitation, and knowledge consistency . |
| Approach: | They propose a benchmark to assess emotional fidelity of role-playing agents in dialogues using large language models. |
| Outcome: | The proposed benchmark measures emotional fidelity of role-playing agents and the characters they portray. |
HieRec: Hierarchical User Interest Modeling for Personalized News Recommendation (2021.acl-long)
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| Challenge: | Existing news recommendation methods learn a single user embedding for each user from their previous behaviors to represent their overall interest. Existing methods only learn 'one' embeddable representation vectors to model user interest. |
| Approach: | They propose a news recommendation method with hierarchical user interest modeling that captures user interest in news rather than a single user embedding. |
| Outcome: | The proposed method can better capture multi-grained user interest in news. |
PlugAT: A Plug and Play Module to Defend against Textual Adversarial Attack (2022.coling-1)
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| Challenge: | Recent studies have shown that adversarial examples can be easily fooled by adversarially perturbed examples. |
| Approach: | They propose a pluggable defense module PlugAT to provide robust predictions by adding a few trainable parameters to the model inputs while keeping the original model frozen. |
| Outcome: | The proposed model improves robustness over several strong baselines whilst training only 9.1% parameters. |
Building Multilingual Machine Translation Systems That Serve Arbitrary XY Translations (2022.naacl-main)
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| Challenge: | Multilingual Neural Machine Translation (MNMT) systems are often limited to many-to-one directions and suffer from poor performance in one-to one directions. |
| Approach: | They propose to build multilingual machine translation systems that serve arbitrary X-Y directions while leveraging multilinguality with a two-stage training strategy of pretraining and finetuning. |
| Outcome: | The proposed system outperforms baseline bilingual models and pivot translation models in most directions without the need for architecture change or extra data collection. |
Nested Browser-Use Learning for Agentic Information Seeking (2026.acl-long)
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Baixuan Li, Jialong Wu, Wenbiao Yin, Kuan Li, Zhongwang Zhang, Huifeng Yin, Zhengwei Tao, Liwen Zhang, Pengjun Xie, Jingren Zhou, Yong Jiang, Wentao Zhang, Zhiqiang Gao
| Challenge: | Existing information-seeking (IS) agents rely on the web for their information acquisition. |
| Approach: | They propose a browser-action framework that decouples interaction control from page exploration through a nested structure. |
| Outcome: | Empirical results show that NestBrowse offers clear benefits in practice. |
Toward Human-Like Evaluation for Natural Language Generation with Error Analysis (2023.acl-long)
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| Challenge: | Pre-trained language models (PLMs) have been used to evaluate language generation tasks . pretrained error analysis can be used to refine the generated sentence toward higher confidence . |
| Approach: | They propose to combine pretrained language model based metrics with human-like error analysis to improve sentence confidence. |
| Outcome: | The proposed method outperforms top-scoring metrics in 19/25 settings. |
DPDV: Dual-Pathway and Dual-View Representation Learning for Bridging Information Asymmetry in Text-Video Retrieval (2026.acl-long)
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| Challenge: | Existing methods for text-based person anomaly search fail to address the pose-semantic gap . asymmetric cross-modal information poses a challenge to accurately establishing retrieval relationships . |
| Approach: | They propose a video retrieval framework that partitions visual features into two categories based on relevance to the text query and performs effective interaction. |
| Outcome: | The proposed framework achieves leading retrieval performance on five benchmark datasets. |
PTUM: Pre-training User Model from Unlabeled User Behaviors via Self-supervision (2020.findings-emnlp)
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| Challenge: | Existing methods for user modeling cannot exploit useful information in unlabeled data . Existing models only model task-specific user information and do not exploit universal user information encoded in user behaviors. |
| Approach: | They propose to pre-train user models from large-scale unlabeled user behavior data. |
| Outcome: | The proposed method can model relatedness between historical and future behaviors on two real-world datasets. |
Collective Human Opinions in Semantic Textual Similarity (2023.tacl-1)
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| Challenge: | Existing benchmarks for semantic textual similarity (STS) use averaged human ratings as gold standard. |
| Approach: | They propose to use a Chinese sentence-to-sentence dataset to study collective human opinions in semantic textual similarity (STS) neither a scalar nor a single Gaussian fits a set of observed judgments adequately, they argue . |
| Outcome: | The proposed dataset does not capture disagreements on individual instances, but rather the confidence over the aggregate dataset. |
Chat-Driven Text Generation and Interaction for Person Retrieval (2025.emnlp-main)
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| Challenge: | Text-based person search (TBPS) aims to retrieve images of a target individual from large-scale databases using natural language descriptions. |
| Approach: | They propose two modules to improve retrieval accuracy and usability . they generate rich pseudo-labels through simulated dialogues with MLLMs . |
| Outcome: | The proposed method achieves competitive or superior results while eliminating the need for manual captions. |
Neural News Recommendation with Multi-Head Self-Attention (D19-1)
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| Challenge: | Precisely modeling news and users is critical for news recommendation, and capturing the contexts of words and news is important to learn news and user representations. |
| Approach: | They propose a neural news recommendation approach with multi-head self-attention to model the interactions between words and news and use multi-headed self- attention to capture relatedness between the news. |
| Outcome: | The proposed approach can learn representations from news titles by modeling the interactions between words and users and capture relatedness between the news. |
UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models (2022.emnlp-main)
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Tianbao Xie, Chen Henry Wu, Peng Shi, Ruiqi Zhong, Torsten Scholak, Michihiro Yasunaga, Chien-Sheng Wu, Ming Zhong, Pengcheng Yin, Sida I. Wang, Victor Zhong, Bailin Wang, Chengzu Li, Connor Boyle, Ansong Ni, Ziyu Yao, Dragomir Radev, Caiming Xiong, Lingpeng Kong, Rui Zhang, Noah A. Smith, Luke Zettlemoyer, Tao Yu
| Challenge: | Structured knowledge grounding (SKG) uses structured knowledge to complete user requests . since inputs and outputs of SKG tasks are heterogeneous, they have been studied separately . |
| Approach: | They propose a framework that unifies 21 SKG tasks into a text-to-text format . they use unifiedSKG to benchmark T5 with different sizes . |
| Outcome: | The proposed framework unifies 21 SKG tasks into a text-to-text format . it achieves state-of-the-art performance on almost all of the 21 tasks, the authors show . |
Measuring Human Contribution in AI-Assisted Content Generation (2026.acl-long)
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Yueqi Xie, Tao Qi, Jingwei Yi, Xiyuan Yang, Ryan Whalen, Junming Huang, Qian Ding, Yu Xie, Xing Xie, Fangzhao Wu
| Challenge: | generative AI has created a new way to generate content with humans . varying degrees of human contribution in content generation poses significant challenges for the delineation of originality . |
| Approach: | They propose a framework to measure human contribution in AI-assisted content generation by calculating mutual information between human input and AI-aided output relative to self-information of AI-assist output. |
| Outcome: | The proposed measure discriminates between varying degrees of human contribution across multiple creative domains and is validated in real-world applications. |
UniDrop: A Simple yet Effective Technique to Improve Transformer without Extra Cost (2021.naacl-main)
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| Challenge: | Existing approaches to improve the performance of natural language processing models are over-parameterized and overfitted. |
| Approach: | They propose an approach to integrate dropout techniques into the training of Transformer models. |
| Outcome: | The proposed approach can achieve 1.5 BLEU improvement on IWSLT14 translation tasks and better accuracy for the classification even using strong pre-trained RoBERTa as backbone. |
HookMoE: A learnable performance compensation strategy of Mixture-of-Experts for LLM inference acceleration (2025.emnlp-main)
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| Challenge: | Mixture of Experts (MoE) models have been a promising paradigm for scaling model capacity through top-k routing mechanisms. |
| Approach: | They propose a plug-and-play single-layer compensation framework that strategically inserts a lightweight trainable Hook module immediately preceding selected transformer blocks. |
| Outcome: | The proposed framework reduces the number of activated experts by more than 50% and achieves a 1.42 inference speed-up during the prefill stage. |
WebCoderBench: Benchmarking Web Application Generation with Comprehensive and Interpretable Evaluation Metrics (2026.acl-long)
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| Challenge: | Web applications (web apps) are a key arena for large language models to demonstrate their code generation capabilities and commercial potential. |
| Approach: | a new benchmark for large language models (LLMs) is designed to provide real-world user requirements and generalizable evaluation metrics. |
| Outcome: | a new benchmark for large language models (LLMs) provides a real-world, generalizable, and interpretable evaluation score . the benchmark measures user requirements, expression styles and human-preference-aligned weights . a web application can be used to demonstrate its commercial potential, authors say . |
MIND: A Large-scale Dataset for News Recommendation (2020.acl-main)
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Fangzhao Wu, Ying Qiao, Jiun-Hung Chen, Chuhan Wu, Tao Qi, Jianxun Lian, Danyang Liu, Xing Xie, Jianfeng Gao, Winnie Wu, Ming Zhou
| Challenge: | Personalized news recommendation is an important technique for personalized news service. |
| Approach: | They propose to build a large-scale news recommendation dataset from Microsoft News . they demonstrate that news recommendation relies on the quality of news content understanding . |
| Outcome: | The proposed dataset contains 1 million users and more than 160k English news articles, each of which has rich textual content such as title, abstract and body. |
SemRegex: A Semantics-Based Approach for Generating Regular Expressions from Natural Language Specifications (D18-1)
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| Challenge: | Existing approaches to generate programs from natural language do not address program aliasing . semantically equivalent programs may have many syntactically different forms . |
| Approach: | They propose a semantics-based approach to generate regular expressions from natural language. |
| Outcome: | The proposed approach improves on three public datasets. |
Structured Self-Supervised Pretraining for Commonsense Knowledge Graph Completion (2021.tacl-1)
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| Challenge: | Existing approaches focus on generating concepts that have direct and obvious relationships with existing concepts and lack an ability to generate unobvious concepts. |
| Approach: | They propose a general graph-to-paths pretraining framework that leverages high-order structures in CKGs to capture high-level relationships between concepts. |
| Outcome: | The proposed framework can capture high-order relationships between concepts in four special cases: long path, path-to-path, router, and graph-node-path. |
Find Your Optimal Teacher: Personalized Data Synthesis via Router-Guided Multi-Teacher Distillation (2026.acl-long)
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Hengyuan Zhang, Shiping Yang, Xiao Liang, Chenming Shang, Yuxuan Jiang, Chaofan Tao, Jing Xiong, Hayden Kwok-Hay So, Ruobing Xie, Angel X. Chang, Ngai Wong
| Challenge: | Existing studies show that stronger models are not always optimal teachers, suggesting a mismatch between the teacher’s output and the student’s learning ability. |
| Approach: | They propose a method that routes each prompt to its optimal teacher via a query-level router that jointly considers the student models’ learnability and teacher models’ response quality. |
| Outcome: | The proposed method outperforms baselines on six benchmarks including instruct tuning and math reasoning settings. |
Rectifying the Emotional Flow: Aligning Priors and Dynamic Guidance for High-Arousal Text-to-Speech (2026.acl-long)
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| Challenge: | Existing systems suffer from linguistic collapse when pursuing high intensity or fail to meet target emotional levels. |
| Approach: | They propose an inference framework that introduces a neutral prosody bias and a uniform Classifier-Free Guidance that distorts the acoustic manifold, leading to artifacts. |
| Outcome: | The proposed framework achieves superior linguistic accuracy and expressiveness without model retraining. |
Descriptive Prompt Paraphrasing for Target-Oriented Multimodal Sentiment Classification (2023.findings-emnlp)
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| Challenge: | Current researches mainly work on either of two types of targets in a decentralized manner. |
| Approach: | They propose a model to perform sentiment polarity on a target jointly considering its corresponding multiple modalities including text, image, and others. |
| Outcome: | The proposed model performs well on four datasets spanning the above two target types and is prompt-based language modelling. |
Extract and Attend: Improving Entity Translation in Neural Machine Translation (2023.findings-acl)
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| Challenge: | Existing methods to improve entity translation in Neural machine translation still suffer from inaccurate translation of entities due to the lack of entity training instances. |
| Approach: | They propose an extract-and-tend approach to enhance entity translation in NMT by extracting entities from a dictionary and attending to them with a prefix. |
| Outcome: | Experiments on En-Zh and En-Ru show that the proposed approach improves translation accuracy and translation quality. |
BioT5+: Towards Generalized Biological Understanding with IUPAC Integration and Multi-task Tuning (2024.findings-acl)
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Qizhi Pei, Lijun Wu, Kaiyuan Gao, Xiaozhuan Liang, Yin Fang, Jinhua Zhu, Shufang Xie, Tao Qin, Rui Yan
| Challenge: | BioT5+ is an extension of the BioT5, but lacked a nuanced understanding of molecular structures. |
| Approach: | They propose a new bio-entity modeling framework, BioT5+, which integrates IUPAC names and molecule data. |
| Outcome: | The proposed model bridges the gap between molecular representations and textual descriptions and improves the grounded reasoning of bio-text and bio-sequences. |
CoDA: Restoring Contextual Dominance via Copy-Encouraged Attention Intervention for Mitigating RAG Hallucinations (2026.findings-acl)
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| Challenge: | Retrieval-augmented generation reduces hallucination by grounding outputs in external evidence. |
| Approach: | They propose a lightweight inference-time attention intervention that amplifies evidence-aligned value states to enhance contextual faithfulness and reduce hallucination. |
| Outcome: | The proposed model reduces hallucination by grounding model outputs in external evidence. |
Benchmarking Meaning Representations in Neural Semantic Parsing (2020.emnlp-main)
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| Challenge: | Existing work on meaning representations is not comprehensively evaluated due to the lack of readily-available execution engines. |
| Approach: | They propose a unified benchmark on meaning representations by integrating existing semantic parsing datasets, completing the missing logical forms, and implementing the missing execution engines. |
| Outcome: | The proposed benchmark combines existing parsing datasets, completes missing logical forms, and implements missing execution engines. |