Papers by Jin Guo

SQLForge: Synthesizing Reliable and Diverse Data to Enhance Text-to-SQL Reasoning in LLMs (2025.findings-acl)

Challenge:	Temporal Knowledge Graphs (TKGs) are used in many different areas of research.
Approach:	They propose to use a beam search policy to induce multiple clues from historical facts . they propose to adopt a graph convolution network based sequence method to deduce answers from clues .
Outcome:	The proposed model can predict future facts in two stages, Clue Searching and Temporal Reasoning.

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Yu Guo, Dong Jin, Shenghao Ye, Shuangwu Chen, Jianyang Jianyang, Xiaobin Tan

Programming Languages in NLP

Challenge:	Existing closed-source LLMs have a performance gap in text-to-SQL reasoning tasks.
Approach:	They propose a SQL-based approach to synthesize reliable data to enhance text-to-SQL reasoning in LLMs.
Outcome:	The proposed model achieves state-of-the-art accuracy on the widely recognized Spider and BIRD benchmarks, significantly narrowing the performance gap with closed-source methods.

Temporal Knowledge Graph Reasoning Based on N-tuple Modeling (2023.findings-emnlp)

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Zhongni Hou, Xiaolong Jin, Zixuan Li, Long Bai, Saiping Guan, Yutao Zeng, Jiafeng Guo, Xueqi Cheng

Omni-Chart-600K: A Comprehensive Dataset of Chart Types for Chart Understanding (2025.findings-naacl)

Challenge:	Existing Temporal Knowledge Graphs (TKGs) only contain their core entities and form them as quadruples.
Approach:	They propose to describe a temporal fact more accurately as an n-tuple . they propose to use a neural network to learn evolutional representations of entities .
Outcome:	The proposed model oversimplifies and causes information loss on two datasets.

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Shulei Wang, Shuai Yang, Wang Lin, Zirun Guo, Sihang Cai, Hai Huang, Ye Wang, Jingyuan Chen, Tao Jin

Neural Retrieval for Question Answering with Cross-Attention Supervised Data Augmentation (2021.acl-short)

Challenge:	Existing chart-related training methods lack capabilities in information extraction, mathematical reasoning, and understanding of multiple chart types.
Approach:	They propose a two-stage training strategy and method for jointly training a vision encoder tailored for multi-type charts to address the deficiencies in chart types and limited scope of chart tasks in existing datasets.
Outcome:	The proposed dataset includes 21 diverse chart types and tasks, including data retrieval and mathematical reasoning.

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Yinfei Yang, Ning Jin, Kuo Lin, Mandy Guo, Daniel Cer

Information Retrieval

Challenge:	Early fusion models with cross-attention have shown better-than-human performance on some question answer benchmarks, while it is a poor fit for retrieval since it prevents pre-computation of the answer representations.
Approach:	They propose a supervised data mining method to train an efficient late fusion retrieval model by using cross-attention models with cross-references.
Outcome:	The proposed model outperforms retrieval models trained with gold annotations on Precision at N (P@N) and Mean Reciprocal Rank (MRR).

MPRF: Interpretable Stance Detection through Multi-Path Reasoning Framework (2025.emnlp-main)

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ZhaoDan Zhang, Jin Zhang, Hui Xu, Jiafeng Guo, Xueqi Cheng

UniLG: A Unified Structure-aware Framework for Lyrics Generation (2023.acl-long)

Challenge:	Existing stance detection methods treat the task as a classification problem, where models output a stance label without providing interpretable reasoning paths.
Approach:	They propose a framework that generates, evaluates, and integrates multiple reasoning paths to improve accuracy, robustness, and transparency in stance detection.
Outcome:	The proposed framework outperforms existing models on the SEM16, VAST, and PStance datasets and is highly interpretable and reliable.

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Tao Qian, Fan Lou, Jiatong Shi, Yuning Wu, Shuai Guo, Xiang Yin, Qin Jin

CausalCite: A Causal Formulation of Paper Citations (2024.findings-acl)

Challenge:	Existing works ignore musical attributes hidden behind lyrics and structure of lyrics . existing works ignore structure of generated lyrics and do not consider structure of songs .
Approach:	They propose a framework for conditional lyrics generation that considers structure and relationship between lyrics and music.
Outcome:	The proposed framework improves the structure modeling and unifies different conditions for different types of lyrics generation.

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Ishan Agrawal, Zhijing Jin, Ehsan Mokhtarian, Siyuan Guo, Yuen Chen, Mrinmaya Sachan, Bernhard Schölkopf

Representation Learning

Challenge:	citation counts are often criticized for failing to accurately reflect the true impact of a paper.
Approach:	They propose a method to measure the impact of a paper on follow-up papers by comparing similar papers by cosine similarity.
Outcome:	The proposed method is based on a new causal inference method, TextMatch.

RouteRAG: Efficient Retrieval-Augmented Generation from Text and Graph via Reinforcement Learning (2026.findings-acl)

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Yucan Guo, Miao Su, Saiping Guan, Zihao Sun, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng

NeuInfer: Knowledge Inference on N-ary Facts (2020.acl-main)

Challenge:	Existing graph-based or hybrid systems lack the ability to integrate supplementary evidence as reasoning unfolds.
Approach:	They propose a framework that integrates non-parametric knowledge into Large Language Models . they use a RL-based framework to optimize the entire generation process via RL .
Outcome:	The proposed framework outperforms existing RAG frameworks in five question answering benchmarks.

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Saiping Guan, Xiaolong Jin, Jiafeng Guo, Yuanzhuo Wang, Xueqi Cheng

MIND: From Passive Mimicry to Active Reasoning through Capability-Aware Multi-Perspective CoT Distillation (2026.acl-long)

Challenge:	Existing studies on knowledge inference on binary facts have focused on finding out connotative valid facts.
Approach:	They propose a neural network model, NeuInfer, for knowledge inference on n-ary facts.
Outcome:	The proposed model can cope with the task to infer an unknown element in a whole fact, while ignoring the binary facts.

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Jin Cui, Jiaqi Guo, Jiepeng Zhou, Ruixuan Yang, Jiayi Lu, Jiajun Xu, Jiangcheng Song, Boran Zhao, Pengju Ren

HiSMatch: Historical Structure Matching based Temporal Knowledge Graph Reasoning (2022.findings-emnlp)

Challenge:	Existing approaches restrict students to following a single golden rationale and treat different reasoning paths independently, causing suboptimal performance.
Approach:	They propose a capability-adaptive framework that transitions distillation from passive mimicry to active cognitive construction and employ a feedback-driven inertia calibration mechanism to align supervision with the student’s current adaptability.
Outcome:	Experiments show that the proposed framework achieves state-of-the-art performance on both in-distribution and out-of distribution benchmarks.

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Zixuan Li, Zhongni Hou, Saiping Guan, Xiaolong Jin, Weihua Peng, Long Bai, Yajuan Lyu, Wei Li, Jiafeng Guo, Xueqi Cheng

Multimodal Prompt Learning with Missing Modalities for Sentiment Analysis and Emotion Recognition (2024.acl-long)

Challenge:	Temporal Knowledge Graphs (TKGs) store facts as triples in the form of subject, relation, object, timestamps.
Approach:	They propose a Temporal Knowledge Graph (TKG) model that extends each triple with a timestamp to describe dynamic facts.
Outcome:	The proposed model improves on six benchmark datasets with up to 5.6% performance improvement compared to the state-of-the-art models.

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Zirun Guo, Tao Jin, Zhou Zhao

Guide the Many-to-One Assignment: Open Information Extraction via IoU-aware Optimal Transport (2023.acl-long)

Challenge:	Existing methods for multimodal sentiment analysis often fail due to equipment failure, data corruption, privacy issues and the like.
Approach:	They propose a multimodal Transformer framework using prompt learning to address the issue of missing modalities.
Outcome:	The proposed framework outperforms existing methods significantly across evaluation metrics.

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Kaiwen Wei, Yiran Yang, Li Jin, Xian Sun, Zequn Zhang, Jingyuan Zhang, Xiao Li, Linhao Zhang, Jintao Liu, Guo Zhi

RSA-Bench: Benchmarking Audio Large Models in Real-World Acoustic Scenarios (2026.findings-acl)

Challenge:	Open Information Extraction (OIE) aims to extract structured information from text without the limitations of close ontology.
Approach:	They propose a method to assign ground truth labels to parallelly generated tuple proposals . they leverage intersection-over-union (IoU) as assignment quality measurement .
Outcome:	The proposed method outperforms the state-of-the-art models on three benchmarks.

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Yibo Zhang, Kaiwen Luo, Liang Lin, Shilinlu Yan, Jin Wang, Yaoqi Guo, Yitian Chen, Yalan Qin, Zhenhong Zhou, Kun Wang, Li Sun

Towards Robust Universal Information Extraction: Dataset, Evaluation, and Solution (2025.acl-long)

Challenge:	Existing evaluations rely on synthetic Gaussian noise or simplistic single-source interference, failing to capture the intricate, multi-layered acoustic dynamics that characterize authentic physical environments.
Approach:	They propose a robustness benchmark to stress-test Audio Large Models (ALLMs) using high-fidelity auditory scene simulations.
Outcome:	The proposed model performs well on a wide range of tasks, including automatic speech recognition, speech translation, and audio-based reasoning.

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Jizhao Zhu, Akang Shi, Zixuan Li, Long Bai, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng

Robustness in NLP

Challenge:	Existing robust benchmark datasets generate only a limited range of perturbations for a single Information Extraction (UIE) task, which fails to evaluate the robustness of UIE models effectively.
Approach:	They propose a new benchmark dataset that utilizes Large Language Models to generate more diverse and realistic perturbations across different IE tasks.
Outcome:	The proposed model performs better with only 15% of the data and is more robust with other models.

KnowCoder: Coding Structured Knowledge into LLMs for Universal Information Extraction (2024.acl-long)

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Zixuan Li, Yutao Zeng, Yuxin Zuo, Weicheng Ren, Wenxuan Liu, Miao Su, Yucan Guo, Yantao Liu, Lixiang Lixiang, Zhilei Hu, Long Bai, Wei Li, Yidan Liu, Pan Yang, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng

Covariance Matrix-Driven Image Channel Allocation for Multimodal Fake News Detection (2026.findings-acl)

Challenge:	None. None.. None!
Approach:	None. None.. None!
Outcome:	None. None. No. :

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Zongliang Han, Wenyu Guo, Guoqing Jin, Yang Liu, Yan Song, Dong Yu, Wang Min

KnowDR-REC: Auditing Knowledge-Conditioned Visual Grounding in Referring Expression Comprehension (2026.findings-acl)

Challenge:	Existing methods focus on designing efficient multimodal fusion frameworks to bridge the semantic gap between images and texts.
Approach:	They propose a covariance matrix-driven image channel allocation method that expands the number of original channel maps and assigns importance scores to the expanded channel maps.
Outcome:	The proposed method achieves state-of-the-art on three public multimodal fake news detection benchmark datasets.

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Guanghao Jin, Jingpei Wu, Tianpei Guo, Yiyi Niu, Weidong Zhou, Linyi Yang, Guoyang Liu

Knowledge-Enhanced Self-Supervised Prototypical Network for Few-Shot Event Detection (2022.findings-emnlp)

Challenge:	Existing evaluation metrics suggest that Multimodal large language models have acquired fine-grained visual grounding capabilities.
Approach:	They propose a benchmark to assess Referring Expression Comprehension (REC) that uses intra-image visual cues to localize target objects and a controllable evaluation mechanism to test sensitivity to fine-grained factual changes.
Outcome:	The proposed benchmarks show that multimodal large language models have a high level of performance on the RefCOCO family of benchmarks.

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Kailin Zhao, Xiaolong Jin, Long Bai, Jiafeng Guo, Xueqi Cheng

Event Detection with Multi-Order Graph Convolution and Aggregated Attention (D19-1)

Challenge:	Existing methods for few-shot event detection are inaccurate and lack a prototype representation module.
Approach:	They propose a Knowledge-Enhanced self-supervised prototypical network for few-shot event detection . it adopts hybrid rules which align event types to FrameNet and introduces knowledge to obtain more instances .
Outcome:	The proposed network improves few-shot event detection performance on three benchmark datasets.

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Haoran Yan, Xiaolong Jin, Xiangbin Meng, Jiafeng Guo, Xueqi Cheng

Syntactic Text Processing

Challenge:	Existing methods for event detection use first-order syntactic relations to identify trigger words.
Approach:	They propose a dependency tree-based method to model and aggregate multi-order syntactic representations in sentences.
Outcome:	The proposed method outperforms existing methods on a benchmark dataset . it uses a dependency tree based graph convolution network with aggregative attention .

KnowCoder-X: Boosting Multilingual Information Extraction via Code (2025.findings-acl)

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Yuxin Zuo, Wenxuan Jiang, Wenxuan Liu, Zixuan Li, Long Bai, Hanbin Wang, Yutao Zeng, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng

Multilinguality

Challenge:	Empirical evidence indicates that Large Language Models exhibit spontaneous cross-lingual alignment in Information Extraction (IE) however, a significant imbalance across languages persists, highlighting an underlying deficiency.
Approach:	They propose a code LLM with advanced cross-lingual and multilingual capabilities for universal IE that standardizes the representation of multilingual schemas using Python classes and conducts IE alignment instruction tuning on translated instance prediction task.
Outcome:	The proposed model surpasses ChatGPT and SoTA by 30.17% without training in 29 unseen languages and significantly improves cross-lingual IE transferability.

GenWiki: A Dataset of 1.3 Million Content-Sharing Text and Graphs for Unsupervised Graph-to-Text Generation (2020.coling-main)

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Zhijing Jin, Qipeng Guo, Xipeng Qiu, Zheng Zhang

Structured Data in NLP

Challenge:	a large-scale, general-domain dataset is needed for knowledge graph-to-text generation . data collection is expensive and data-intensive, making it difficult to get good annotation .
Approach:	They propose to use a large-scale, general-domain dataset to generate unsupervised text from knowledge graphs.
Outcome:	The proposed dataset has 1.3M text and graph examples, and is a benchmark for future research . good annotation is expensive and difficult to get, and it's difficult to check quality .

Selective Temporal Knowledge Graph Reasoning (2024.lrec-main)

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Zhongni Hou, Xiaolong Jin, Zixuan Li, Long Bai, Jiafeng Guo, Xueqi Cheng

Sailor: Open Language Models for South-East Asia (2024.emnlp-demo)

Challenge:	Existing models cannot abstain from uncertain predictions, which will bring risks in real-world applications.
Approach:	They propose to abstain from uncertain future facts by using a confidence estimator . they take both the certainty of the current prediction and the accuracy of historical predictions into account .
Outcome:	The proposed abstention mechanism helps existing models make selective predictions instead of indiscriminate ones.

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Longxu Dou, Qian Liu, Guangtao Zeng, Jia Guo, Jiahui Zhou, Xin Mao, Ziqi Jin, Wei Lu, Min Lin

Analyzing and Mitigating Inconsistency in Discrete Speech Tokens for Neural Codec Language Models (2025.acl-long)

Challenge:	Large language models (LLMs) rely on English data for training, but are often not comparable across other languages.
Approach:	They propose to develop a family of open language models for SEA languages . they use BPE dropout, aggressive data cleaning and deduplication to improve model robustness .
Outcome:	The proposed models perform well across four benchmarks, including commonsense reasoning, question answering, reading comprehension and examination.

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Wenrui Liu, Zhifang Guo, Jin Xu, Yuanjun Lv, Yunfei Chu, Zemin Liu, Junyang Lin

Assist Non-native Viewers: Multimodal Cross-Lingual Summarization for How2 Videos (2022.emnlp-main)

Challenge:	Recent advances in Large Language Models (LLMs) have demonstrated significant strides in generating high-quality speech . discretizing speech by neural audio codecs often results in sequences that differ from text sequences .
Approach:	They quantitatively analyze the Discrete Representation Inconsistency phenomenon within popular audio tokenizers such as EnCodec.
Outcome:	The proposed method mitigates the DRI phenomenon within popular audio tokenizers such as EnCodec.

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Nayu Liu, Kaiwen Wei, Xian Sun, Hongfeng Yu, Fanglong Yao, Li Jin, Guo Zhi, Guangluan Xu

Rethinking Multiple-Choice Questions for RLVR: Unlocking Potential via Distractor Design (2026.findings-acl)

Challenge:	Existing multimodal summarization methods are limited to monolingual videos . a proposed task aims to generate cross-lingual summaries from multimodal inputs .
Approach:	They propose a task to generate cross-lingual summaries from multimodal inputs of videos . they propose fusion network that integrates multimodal and cross-linguistic information .
Outcome:	The proposed task outperforms existing methods on a reorganized How2 dataset on the reorganized How2 data set.

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Xu Guo, Qiming Ge, Jian Tong, Kedi Chen, Jin Zhang, Xiaogui Yang, Xuan Gao, Haijun Lv, Zhihui Lu, Yicheng Zou, Qipeng Guo

MdEval: Massively Multilingual Code Debugging (2026.findings-acl)

Challenge:	Existing approaches to RLVR use multiple-choice questions as verifiable rewards . however, not all tasks provide reliable verification .
Approach:	They propose a framework that actively constructs high-quality distractors to block elimination shortcuts and promote deep reasoning.
Outcome:	The proposed method significantly improves reasoning capabilities of Large Language Models.

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Shukai Liu, Linzheng Chai, Jian Yang, Jiajun Shi, He Zhu, Liran Wang, Jin Ke, Wei Zhang, Hualei Zhu, Shuyue Guo, Tao Sun, Jiaheng Liu, Yunlong Duan, Yu Hao, Liqun Yang, Guanglin Niu, Ge Zhang, Zhoujun Li

Event Coreference Resolution with their Paraphrases and Argument-aware Embeddings (2020.coling-main)

Challenge:	Existing benchmarks primarily focus on Python and are limited in terms of language diversity.
Approach:	They propose a multilingual debugging benchmark that includes 3.9K test samples of 20 programming languages and introduces the debug instruction corpora MdEval-Instruct by injecting bugs into the correct multilingual queries and solutions.
Outcome:	The proposed benchmark includes 3.9K test samples of 20 programming languages and covers the automated program repair task, bug localization task, and bug identification task.

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Yutao Zeng, Xiaolong Jin, Saiping Guan, Jiafeng Guo, Xueqi Cheng

Benchmarking Long-Context Language Models on Long Code Understanding (2025.acl-long)

Challenge:	Existing methods for event coreference resolution do not identify paraphrase relations between events.
Approach:	They propose a new event-specific paraphrase and argument-aware semantic Embedding model for event coreference resolution based on event-related paraphrases and argument embeddings . EPASE recognizes deep paraphrase relations in an event- specific context of sentences and can cover event paraphrase of more situations .
Outcome:	Experiments on within- and cross-document event coreference show it is superior compared to existing methods.

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Jia Li, Xuyuan Guo, Lei Li, Kechi Zhang, Ge Li, Jia Li, Zhengwei Tao, Fang Liu, Chongyang Tao, Yuqi Zhu, Zhi Jin

Few-shot Link Prediction on Hyper-relational Facts (2024.lrec-main)

Challenge:	Currently, long-context language models are limited by the lack of a rigorous evaluation framework for long code understanding.
Approach:	They propose to use a long code understanding benchmark LongCodeU to evaluate LCLMs' long code comprehension ability for practical applications.
Outcome:	The proposed benchmarks show that current LCLMs are limited in their long code understanding ability, particularly when the long code length is greater than 32K, falling far short of their claimed 128K to 1M context windows.

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Jiyao Wei, Saiping Guan, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng

View-R1: Asymmetric Policy Optimization for Difficulty-Aware Multimodal Reinforcement Learning (2026.findings-acl)

Challenge:	Existing methods to predict missing elements in hyper-relational facts require high-quality data.
Approach:	They propose a task to predict a missing entity in a hyper-relational fact with limited support instances.
Outcome:	The proposed model outperforms existing models on three datasets.

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Minjie Hong, Zirun Guo, Jiabao Zhang, Zehan Wang, Ziang Zhang, Tao Jin, Zhou Zhao

Nested Event Extraction upon Pivot Element Recognition (2024.lrec-main)

Challenge:	Multimodal Large Language Models (MLLMs) are powerful at integrating diverse data but struggle with complex reasoning.
Approach:	They propose a method which separates responses into positive and negative groups to stabilize training and preserve knowledge.
Outcome:	The proposed model View-R1 achieves a 10.55% improvement in reasoning and outperforms larger models while maintaining and improving performance on general tasks.

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Weicheng Ren, Zixuan Li, Xiaolong Jin, Long Bai, Miao Su, Yantao Liu, Saiping Guan, Jiafeng Guo, Xueqi Cheng

Implicit Discourse Relation Recognition using Neural Tensor Network with Interactive Attention and Sparse Learning (C18-1)

Challenge:	Nested Event Extraction (NEE) aims to extract complex event structures where an event contains other events as its arguments recursively.
Approach:	They propose a new model that extracts nested events mainly based on recognizing PEs.
Outcome:	The proposed model can extract nested events based on recognizing PEs . it incorporates information from both event types and argument roles to improve performance .

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Fengyu Guo, Ruifang He, Di Jin, Jianwu Dang, Longbiao Wang, Xiangang Li

Challenge:	Existing methods for implicit discourse relation recognition ignore bidirectional interactions between two arguments and sparsity of pair patterns.
Approach:	They propose a neural Tensor network framework with interactive attention and sparse learning for implicit discourse relation recognition.
Outcome:	The proposed framework is effective on PDTB and can be used in text summarization, conversation system and so on.

Trigger is Not Sufficient: Exploiting Frame-aware Knowledge for Implicit Event Argument Extraction (2021.acl-long)

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Kaiwen Wei, Xian Sun, Zequn Zhang, Jingyuan Zhang, Guo Zhi, Li Jin

Semantic Structure Enhanced Event Causality Identification (2023.acl-long)

Challenge:	Existing methods to extract event arguments focus on learning pair-wise information between arguments and the given trigger.
Approach:	They propose a framework to extract event-related arguments from a given event frame-level scope.
Outcome:	The proposed method achieves state-of-the-art on the RAMS dataset.

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Zhilei Hu, Zixuan Li, Xiaolong Jin, Long Bai, Saiping Guan, Jiafeng Guo, Xueqi Cheng

Challenge:	Existing methods for Event Causality Identification (ECI) capture implicit associations between events, which are difficult because they lack the ability to understand the associations between two events.
Approach:	They propose a model that captures the implicit associations between two events and integrates the event-centric structure information into a GNN-based event aggregator.
Outcome:	The proposed model improves on three widely used datasets showing that it integrates event-centric and event-associated semantic elements and captures event associations.

Towards Event Extraction with Massive Types: LLM-based Collaborative Annotation and Partitioning Extraction (2025.emnlp-main)

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Wenxuan Liu, Zixuan Li, Long Bai, Yuxin Zuo, Daozhu Xu, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng

Multi-Scale Spectral Selection and Entropy-Guided Uncertainty Fusion for Multimodal Rumor Detection (2026.findings-acl)

Challenge:	Event Extraction (EE) is a long-standing target, but lacks an efficient and effective annotation framework to construct the corresponding datasets.
Approach:	They propose an LLM-based collaborative annotation framework that refines annotations of triggers from distant supervision and carries out argument annotation.
Outcome:	The proposed framework outperforms state-of-the-art methods on the largest EE dataset to date . it achieves the F1 scores of 90% and 85.3% on the human-annotated test set .

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Zongliang Han, Wenyu Guo, Guoqing Jin, Yang Liu, Fan Li, Dong Yu, Yan Song, null Zhangfengzhen

Modeling Human-Like Cognition for Stance Detection: Integrating Intuitive Judgment and Analytical Reasoning (2026.acl-long)

Challenge:	Existing methods for multimodal content detection fail to capture cross-modal semantic inconsistencies and ignore inherent noise in multimodal features.
Approach:	They propose a multimodal rumor detection method based on a frequency domain spectral selection method and entropy-guided uncertainty fusion method to capture cross-modal semantic inconsistencies.
Outcome:	The proposed method outperforms state-of-the-art methods in multimodal rumor detection . it shows stronger detection capability and robustness on multiple datasets .

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Zhaodan Zhang, Jin Zhang, Jiafeng Guo, Xueqi Cheng

Linguistics & Cognitive NLP

Challenge:	Recent advances in large language models (LLMs) have revolutionized stance detection, enabling complex reasoning strategies such as chain-of-thought prompting.
Approach:	They propose Cognitive-Driven Stance Detection (CDSD) that integrates fast intuitive judgment and analytical reasoning enhanced by three key modules: attention-based cognitive alignment to compare system focus, uncertainty-aware belief update using Bayesian inference, and self-doubt-triggered counterfactual reasoning for re-evaluation under low consistency or high uncertainty.
Outcome:	The proposed method outperforms state-of-the-art methods on SEM16, P-Stance, and VAST.

G2S: A General-to-Specific Learning Framework for Temporal Knowledge Graph Forecasting with Large Language Models (2025.findings-acl)

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Long Bai, Zixuan Li, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng, Tat-Seng Chua

Beyond Dialogue Time: Temporal Semantic Memory for Personalized LLM Agents (2026.findings-acl)

Challenge:	Recent studies have introduced Large Language Models (LLMs) for this task to enhance the models’ generalization abilities.
Approach:	They propose a General-to-Specific learning framework that disentangles the learning processes of two kinds of knowledge in a temporal temporal structure.
Outcome:	The proposed framework disentangles the learning processes of the above two kinds of knowledge and improves their generalization abilities.

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Miao Su, Yucan Guo, Zhongni Hou, Long Bai, Zixuan Li, Yufei Zhang, Guojun Yin, Wei Lin, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng

Dialogue Systems & Conversational Agents

Challenge:	Existing methods focus on point-wise memory, losing durative information that captures persistent states and evolving patterns.
Approach:	They propose a memory framework that models semantic time for point-wise memory and supports the construction and utilization of durative memory.
Outcome:	Experiments on LongMemEval and LoCoMo show that the proposed method outperforms existing methods and achieves up to 12.2% improvement in accuracy.

Integrating Deep Event-Level and Script-Level Information for Script Event Prediction (2021.emnlp-main)

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Long Bai, Saiping Guan, Jiafeng Guo, Zixuan Li, Xiaolong Jin, Xueqi Cheng

Text Classification by Contrastive Learning and Cross-lingual Data Augmentation for Alzheimer’s Disease Detection (2020.coling-main)

Challenge:	Existing studies only consider a single event sequence corresponding to one common protagonist.
Approach:	They propose a Transformer-based model which integrates deep event-level and script-level information for script event prediction.
Outcome:	The proposed model is superior to existing models on the New York Times corpus . it utilizes rich information in the text to obtain more comprehensive representations .

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Zhiqiang Guo, Zhaoci Liu, Zhenhua Ling, Shijin Wang, Lingjing Jin, Yunxia Li

Class-Incremental Few-Shot Event Detection (2024.lrec-main)

Challenge:	Existing methods for AD detection are too expensive and time-consuming to cover all potential patients.
Approach:	They propose a contrastive learning method to obtain effective text representations based on monolingual embeddings of BERT and a cross-lingual data augmentation method by building autoencoders to learn the text representation shared by both languages.
Outcome:	The proposed method outperforms other methods on a Mandarin AD corpus and achieves 81.6% detection accuracy.

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Kailin Zhao, Xiaolong Jin, Long Bai, Jiafeng Guo, Xueqi Cheng

Rethinking Table Pruning in TableQA: From Sequential Revisions to Gold Trajectory-Supervised Parallel Search (2026.acl-long)

Challenge:	Existing methods to deal with new class of events with only a few labeled instances are challenging . old knowledge forgetting and new class overfitting are two problems in this task.
Approach:	They propose a task called class-incremental few-shot event detection to solve old knowledge forgetting and new class overfitting problems.
Outcome:	The proposed method reduces old knowledge forgetting and new class overfitting problems on two benchmark datasets.

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Yu Guo, Shenghao Ye, Shuangwu Chen, Zijian Wen, Tao Zhang, Bai Qirui, Dong Jin, Yunpeng Hou, Huasen He, null Jianyang, Xiaobin Tan

TCSinger 2: Customizable Multilingual Zero-shot Singing Voice Synthesis (2025.findings-acl)

Challenge:	Existing pruning methods rely on sequential revisions and unreliable critique signals . Existing methods fail to detect the loss of answer-critical data .
Approach:	They propose a table pruning framework which transforms table pruning to gold trajectory-supervised parallel search.
Outcome:	The proposed framework outperforms the strongest baseline pruning framework by 3.2% on various tabular reasoning tasks.

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Yu Zhang, Wenxiang Guo, Changhao Pan, Dongyu Yao, Zhiyuan Zhu, Ziyue Jiang, Yuhan Wang, Tao Jin, Zhou Zhao

Large Language Model-Based Event Relation Extraction with Rationales (2025.coling-main)

Challenge:	Existing zero-shot singing voice synthesis models depend on phoneme and note boundary annotations, limiting their robustness and producing poor transitions between phonemes and notes.
Approach:	They propose a multi-task multilingual zero-shot SVS model with style transfer and style control based on various prompts.
Outcome:	Experimental results show that TCSinger 2 outperforms baseline models in subjective and objective metrics across multiple related tasks.

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Zhilei Hu, Zixuan Li, Xiaolong Jin, Long Bai, Jiafeng Guo, Xueqi Cheng

DVD: Dynamic Contrastive Decoding for Knowledge Amplification in Multi-Document Question Answering (2024.emnlp-main)

Challenge:	Existing methods for ERE rely on large language models, but they face limitations.
Approach:	They propose an LLM-based approach with rationales for the ERE task . LLMERE transforms ERE into a question-and-answer task that may have multiple answers .
Outcome:	Experimental results show that LLMERE improves over existing methods.

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Jing Jin, Houfeng Wang, Hao Zhang, Xiaoguang Li, Zhijiang Guo

Information Retrieval

Challenge:	Large language models (LLMs) generate information with hallucinations due to uneven retrieval quality and irrelevant contents.
Approach:	They propose a decoding strategy which dynamically amplifies knowledge from selected documents during the generation phase.
Outcome:	The proposed method outperforms other decoding strategies on ALCE-ASQA, NQ, TQA and PopQA benchmarks.

KCVR: Knowledge-Centric Video Reconstruction for Structured Pedagogical Summarization via Dynamic Graph Planning (2026.acl-long)

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Jingjiang Liu, Jia Zhu, Hanghui Guo, Weijie Shi, Yue Cui, Xiaokang Jin, Yilin Wang, Qingyu Niu, Jiawei Shen, Guoqing Ma, Yidan Liang, Shimin Di, Jiajie Xu

Inductive Link Prediction in N-ary Knowledge Graphs (2025.coling-main)

Challenge:	Existing summarization methods compress content for gist browsing, but they break prerequisite logic in instructional videos.
Approach:	They propose a framework that decouples epistemic planning from content generation.
Outcome:	The proposed framework outperforms strong end-to-end baselines on Knowledge Progression Consistency and Learning Objective Coverage.

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Jiyao Wei, Saiping Guan, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng

Reviving Cultural Heritage: A Novel Approach for Comprehensive Historical Document Restoration (2025.acl-long)

Challenge:	Existing methods to predict missing elements in NKGs are fixed and therefore cannot be used in real-world situations.
Approach:	They propose a task to predict missing elements in unseen facts involving unseent entities and roles in emerging NKGs by embedding unseense entities and role-encoding neural networks.
Outcome:	The proposed task outperforms representative models across all datasets.

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Yuyi Zhang, Peirong Zhang, Zhenhua Yang, Pengyu Yan, Yongxin Shi, Pengwei Liu, Fengjun Guo, Lianwen Jin

MetaSLRCL: A Self-Adaptive Learning Rate and Curriculum Learning Based Framework for Few-Shot Text Classification (2022.coling-1)

Challenge:	Existing methods for historical document restoration focus on single modality or limited-size restoration, failing to meet practical needs.
Approach:	They propose a full-page HDR dataset and an automated HDR solution to replace manual restoration methods.
Outcome:	The proposed solution improves OCR accuracy from 46.83% to 84.05% when processing severely damaged documents, with enhancement to 94.25% through human-machine collaboration.

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Kailin Zhao, Xiaolong Jin, Saiping Guan, Jiafeng Guo, Xueqi Cheng

Event Causality Extraction via Implicit Cause-Effect Interactions (2023.emnlp-main)

Challenge:	Existing few-shot text classification methods lack labeled data in many scenarios.
Approach:	They propose a meta learning framework that obtains different learning rates for different tasks and neural network layers to enable the meta learner to quickly adapt to new training data.
Outcome:	The proposed framework can obtain different learning rates for different tasks and neural network layers so as to enable the meta learner to quickly adapt to new tasks.

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Jintao Liu, Zequn Zhang, Kaiwen Wei, Zhi Guo, Xian Sun, Li Jin, Xiaoyu Li

CS2W: A Chinese Spoken-to-Written Style Conversion Dataset with Multiple Conversion Types (2023.emnlp-main)

Challenge:	Existing studies have not exploited the interactions between the cause and effect event that could provide crucial clues for causality reasoning.
Approach:	They propose an Implicit Cause-Effect interaction framework which captures the implicit intra- and inter-event interactions by incorporating the privileged information for reasoning.
Outcome:	The proposed framework captures the implicit intra- and inter-event interactions by incorporating the privileged information (ground truth event types and arguments) for reasoning.

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Zishan Guo, Linhao Yu, Minghui Xu, Renren Jin, Deyi Xiong

DCR: Quantifying Data Contamination in LLMs Evaluation (2025.emnlp-main)

Challenge:	Existing datasets focus on a single type of spoken style, such as disfluencies.
Approach:	They propose a Chinese Spoken-to-Written style conversion dataset with 7,237 spoken sentences extracted from transcribed conversational texts.
Outcome:	The proposed dataset covers four major conversion problems corresponding to the majority of spoken styles.

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Cheng Xu, Nan Yan, Shuhao Guan, Changhong Jin, Yuke Mei, Yibing Guo, Tahar Kechadi

Challenge:	Large language models (LLMs) memorize evaluation data during training, inflating performance metrics and undermining genuine generalization assessment.
Approach:	They propose a framework to detect and quantify benchmark data contamination (BDC) by synthesizing contamination scores via a fuzzy inference system.
Outcome:	The proposed framework detects and quantifies BDC risk across semantic, informational, data, and label levels.

Efficient Prompting for Continual Adaptation to Missing Modalities (2025.naacl-long)

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Zirun Guo, Shulei Wang, Wang Lin, Weicai Yan, Yangyang Wu, Tao Jin

InSerter: Speech Instruction Following with Unsupervised Interleaved Pre-training (2025.acl-long)

Challenge:	Existing methods combine various missing cases to train recovery modules or align multimodal features, resulting in suboptimal performance, high computational costs, and catastrophic forgetting.
Approach:	They propose a continual multimodal missing modality task that uses prompts to learn modalities . existing methods often aggregate various missing cases to train recovery modules . authors conduct extensive experiments on three public datasets .
Outcome:	The proposed method consistently outperforms state-of-the-art methods on three public datasets.

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Dingdong Wang, Jin Xu, Ruihang Chu, Zhifang Guo, Xiong Wang, Jincenzi Wu, Dongchao Yang, Shengpeng Ji, Junyang Lin

GraphIE: A Graph-Based Framework for Information Extraction (N19-1)

Challenge:	Recent advances in speech large language models exhibit suboptimal performance in adhering to speech instructions.
Approach:	They propose a method to pre-train large-scale unsupervised speech-text sequences . they use text-to-speech conversion to generate textual continuations corresponding to provided speech segments .
Outcome:	The proposed model achieves superior or competitive results across diverse speech processing tasks.

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Yujie Qian, Enrico Santus, Zhijing Jin, Jiang Guo, Regina Barzilay

The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation (2023.findings-emnlp)

Challenge:	Most modern Information Extraction (IE) systems are implemented as sequential taggers and model local dependencies.
Approach:	They propose a framework that operates over a graph representing a broad set of dependencies between textual units.
Outcome:	The proposed framework outperforms the state-of-the-art sequence tagging model on three different tasks.

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Dung Nguyen, Le Nam, Anh Dau, Anh Nguyen, Khanh Nghiem, Jin Guo, Nghi Bui

Dynamics of Cognitive Heterogeneity: Investigating Behavioral Biases in Multi-Stage Supply Chains with LLM-Based Simulation (2026.acl-long)

Challenge:	Open-source dataset of code-text pairs for training large language models to understand code is outperforms other datasets for code generation and understanding tasks.
Approach:	They propose to extract high-quality code-text pairs from a dataset of 43 million pairs . they use rules and deep learning to ensure that the code-sampled samples contain high-quality pairs a .
Outcome:	The Vault dataset outperforms existing models on common coding tasks . authors hope the results will propel AI research and software development forward .

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Jiuyun Jiang, Yuecheng Hong, Bo Yang, Jin Yang, Guangxin Jiang, Xiaomeng Guo, Guang Xiao

Spotlight and Shadow: Attention-Guided Dual-Anchor Introspective Decoding for MLLM Hallucination Mitigation (2026.findings-acl)

Challenge:	Recent advances in Large Language Models (LLMs) allow repeatable experiments in which individual characteristics can be precisely defined.
Approach:	They propose a scalable experimental paradigm using Large Language Models to simulate multi-stage supply chain dynamics.
Outcome:	The proposed model systematically replicates and validates the results of a behavioral simulation on agents in multi-stage supply chain dynamics.

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Yebo Wu, Han Jin, Zhijiang Guo, Li Li

MDIT-Bench: Evaluating the Dual-Implicit Toxicity in Large Multimodal Models (2025.findings-acl)

Challenge:	Multimodal Large Language Models (MLLMs) excel in tasks ranging from image captioning to complex reasoning.
Approach:	They propose a contrastive decoding framework that dynamically calibrates each token generation by mining the model’s internal perceptual discrepancies.
Outcome:	The proposed framework mitigates hallucination while enhancing general reasoning capabilities.

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Bohan Jin, Shuhan Qi, Kehai Chen, Xinyi Guo, Xuan Wang

A Survey of Link Prediction in N-ary Knowledge Graphs (2025.emnlp-main)

Challenge:	Large Multimodal Models (LMMs) have raised concerns about model toxicity.
Approach:	They propose a model to measure the toxicity gap between models and their hard level to determine whether they can handle dual-implicit toxicity.
Outcome:	The proposed model can handle dual-implicit toxicity effectively on 13 prominent LMMs, but its performance drops significantly in hard level.

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Jiyao Wei, Saiping Guan, Da Li, Zhongni Hou, Miao Su, Yucan Guo, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng

Profiler: Black-box AI-generated Text Origin Detection via Context-aware Inference Pattern Analysis (2025.emnlp-main)

Challenge:	N-ary Knowledge Graphs (NKGs) capture n-ary facts containing more than two entities.
Approach:	They present the first comprehensive survey of link prediction in NKGs . they provide an overview of the field and analyze their performance and application scenarios .
Outcome:	The proposed methods provide an overview of the field and analyze performance and application scenarios.

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Hanxi Guo, Siyuan Cheng, Xiaolong Jin, Zhuo Zhang, Guangyu Shen, Kaiyuan Zhang, Shengwei An, Guanhong Tao, Xiangyu Zhang

Challenge:	Existing methods to identify the origin of AI-generated texts fail to identify origin due to the high similarity of different LLMs.
Approach:	They propose a black-box AI-generated text origin detection method which accurately predicts the origin of an input text by extracting distinct context inference patterns.
Outcome:	The proposed method outperforms 10 state-of-the-art baselines and achieves a 25% increase in AUC score on average across natural language and code datasets.

When TableQA Meets Noise: A Dual Denoising Framework for Complex Questions and Large-scale Tables (2026.acl-long)

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Shenghao Ye, Yu Guo, Dong Jin, Yuxiang Wang, Yikai Shen, Yunpeng Hou, Shuangwu Chen, null Jianyang, Xiaofeng Jiang