GRAPH NEURAL NETWORKS

Multiplex Graph Neural Network for Extractive Text Summarization (2021.emnlp-main)

Challenge:	Existing methods for extracting attribute value from product descriptions are limited in their accuracy.
Approach:	They propose a method for extracting product attribute value from product description using graphs and neural networks.
Outcome:	The proposed method improves product description attribute value extraction accuracy compared to baseline methods.

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Baoyu Jing, Zeyu You, Tao Yang, Wei Fan, Hanghang Tong

Multimodal Graph Transformer for Multimodal Question Answering (2023.eacl-main)

Challenge:	Existing methods for extractive text summarization do not consider multiple types of inter-sentential relationships, nor model intra-sententential relationships.
Approach:	They propose a novel method to combine different types of relationships among sentences and words to model sentence embedding.
Outcome:	The proposed model is compared with existing methods on CNN/DailyMail benchmark dataset to demonstrate its effectiveness.

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Xuehai He, Xin Wang

PerKGQA: Question Answering over Personalized Knowledge Graphs (2022.findings-naacl)

Challenge:	a myriad of complex tasks require both prior knowledge and reasoning intelligence.
Approach:	They propose a plug-and-play quasi-attention mechanism to integrate multimodal graph information to vanilla self-attention as effective prior.
Outcome:	The proposed model is able to perform reasoning across multiple modalities.

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Ritam Dutt, Kasturi Bhattacharjee, Rashmi Gangadharaiah, Dan Roth, Carolyn Rose

Graph Enhanced Cross-Domain Text-to-SQL Generation (D19-53)

Challenge:	Existing methods for question answering over knowledge graphs have focused on generalizable or generic knowledge, which assumes there is a predefined global KG for all queries.
Approach:	They propose to use a non-parametric technique that employs case-based reasoning and a parametric approach using graph neural networks to query a predefined knowledge graph (KG)
Outcome:	The proposed methods outperform strong baselines on an academic and an internal dataset by 6.5% and 10.5%.

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Siyu Huo, Tengfei Ma, Jie Chen, Maria Chang, Lingfei Wu, Michael Witbrock

CRSLab: An Open-Source Toolkit for Building Conversational Recommender System (2021.acl-demo)

Challenge:	Existing deep learning approaches for semantic parsing do not generalize to unseen data sets . existing benchmarks have shown text-to-SQL parsers do not generally perform well to unsen SQL queries.
Approach:	They propose a new cross-domain learning scheme to perform text-to-SQL translation . they demonstrate its use on a large-scale cross- domain text- to-Sql data set Spider .
Outcome:	The proposed learning scheme improves on a large-scale text-to-SQL data set.

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Kun Zhou, Xiaolei Wang, Yuanhang Zhou, Chenzhan Shang, Yuan Cheng, Wayne Xin Zhao, Yaliang Li, Ji-Rong Wen

Natural Language Interfaces

Challenge:	Existing studies on conversational recommender systems lack a unified and standardized implementation or comparison.
Approach:	They propose to use a unified framework and highly-decoupled modules to develop CRSs.
Outcome:	The proposed framework collects 6 commonly used human-annotated CRS datasets and implements 19 models that include advanced techniques such as graph neural networks and pre-training models.

XRec: Large Language Models for Explainable Recommendation (2024.findings-emnlp)

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Qiyao Ma, Xubin Ren, Chao Huang

Explainability & Interpretability in NLP

Challenge:	Collaborative filtering (CF) is a widely adopted approach, but lacks the ability to provide explanations for the recommended items.
Approach:	They propose a model-agnostic framework that enables large language models to provide comprehensive explanations for user behaviors in recommender systems.
Outcome:	The proposed framework outperforms baseline approaches in explainable recommender systems.

Mixture of Length and Pruning Experts for Knowledge Graphs Reasoning (2025.emnlp-main)

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Enjun Du, Siyi Liu, Yongqi Zhang

Marrying LLMs with Dynamic Forecasting: A Graph Mixture-of-expert Perspective (2025.findings-naacl)

Challenge:	Existing graph neural networks (GNNs) adopt rigid, query-agnostic path-exploration strategies limiting their ability to adapt to diverse linguistic contexts and semantic nuances.
Approach:	They propose a mixture-of-experts framework that personalizes path exploration . framework uses length experts that adaptively selects and weights candidate paths . it also uses pruning experts that evaluates candidate path from a complementary perspective .
Outcome:	The proposed framework shows superior performance on a diverse benchmark . it uses a mixture of experts that weights and selects path lengths according to query complexity .

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Dapeng Jiang, Xiao Luo

Enhancing Future Link Prediction in Quantum Computing Semantic Networks through LLM-Initiated Node Features (2025.coling-industry)

Challenge:	Recent data-driven approaches often use graph neural networks (GNNs) to learn relationships in dynamical systems.
Approach:	They propose a framework which leverages large language models to enhance generalization capabilities of dynamical system modeling.
Outcome:	The proposed framework improves on existing methods and compares to baselines.

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Gilchan Park, Paul Baity, Byung-Jun Yoon, Adolfy Hoisie

Few-Shot Structured Policy Learning for Multi-Domain and Multi-Task Dialogues (2023.findings-eacl)

Challenge:	Quantum computing is rapidly evolving in both physics and computer science due to its potential to solve complex quantum physics problems and accelerate computational processes.
Approach:	They propose to initialize node features using LLMs to enhance node representations for link prediction tasks in graph neural networks.
Outcome:	The proposed method compared to traditional node embedding techniques on a quantum computing semantic network and demonstrated efficacy compared with other methods.

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Thibault Cordier, Tanguy Urvoy, Fabrice Lefèvre, Lina M. Rojas Barahona

GNN-encoder: Learning a Dual-encoder Architecture via Graph Neural Networks for Dense Passage Retrieval (2022.findings-emnlp)

Challenge:	Reinforcement learning is widely adopted to model dialogue managers in task-oriented dialogues, but the user simulator provided by state-of-the-art dialogue frameworks are only rough approximations of human behaviour.
Approach:	They propose to use structured policies to improve sample efficiency when learning on multi-domain and multi-task environments.
Outcome:	The proposed policies improve sample efficiency and performance on multi-domain and multi-task environments.

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Jiduan Liu, Jiahao Liu, Yang Yang, Jingang Wang, Wei Wu, Dongyan Zhao, Rui Yan

Information Retrieval

Challenge:	Existing approaches to perform large-scale query-passage retrieval are term-based, but they lose interaction between query-pastage pairs.
Approach:	They propose to fuse query (passage) information into query representations via graph neural networks that are constructed by queries and their top retrieved passages.
Outcome:	The proposed model outperforms existing models on MSMARCO, Natural Questions and TriviaQA datasets and achieves the new state-of-the-art on these datasets.

A Decade of Scholarly Research on Open Knowledge Graphs (2024.lrec-main)

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Houcemeddine Turki, Abraham Toluwase Owodunni, Mohamed Ali Hadj Taieb, René Fabrice Bile, Mohamed Ben Aouicha

Modeling Ideological Salience and Framing in Polarized Online Groups with Graph Neural Networks and Structured Sparsity (2022.findings-naacl)

Challenge:	Several literature surveys have been done to understand how open knowledge graphs are constructed, evaluated, and integrated.
Approach:	They analyze 4445 scholarly articles retrieved from Scopus and analyze their results to identify trends, patterns, and impact of research in this field.
Outcome:	The results reveal an ever-increasing number of publications on open knowledge graphs published every year, especially in developed countries (+50 per year).

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Valentin Hofmann, Xiaowen Dong, Janet Pierrehumbert, Hinrich Schuetze

Hierarchical Attention Graph for Scientific Document Summarization in Global and Local Level (2024.findings-naacl)

Challenge:	Existing methods to detect ideological divides in social media rely on knowing in advance the political orientation of text . fascist and mainstream are among the most polarized concepts in reddit in 2019 .
Approach:	They propose a minimally supervised method that leverages the network structure of online discussion forums to detect polarized concepts.
Outcome:	The proposed framework captures temporal ideological dynamics such as right-wing and left-wing radicalization using graph neural networks and sparsity learning.

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Chenlong Zhao, Xiwen Zhou, Xiaopeng Xie, Yong Zhang

Classification of hierarchical text using geometric deep learning: the case of clinical trials corpus (2021.emnlp-main)

Challenge:	Existing methods for document summarization focus on one type of relation, neglecting the simultaneous effective modeling of both relations.
Approach:	They propose a graph neural network-based approach to local and global document summarization using hierarchical discourses.
Outcome:	The proposed approach improves on two benchmark datasets and shows that hierarchical structures are important for document summarization.

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Sohrab Ferdowsi, Nikolay Borissov, Julien Knafou, Poorya Amini, Douglas Teodoro

How Do Large Language Models Perform in Dynamical System Modeling (2025.findings-naacl)

Challenge:	Fig. 1 shows a simplified CT protocol.
Approach:	They propose to use geometric deep learning to classify hierarchical documents into different categories by using a selective graph pooling operation that arises from the fact that some parts of the hierarchy are invariable across different documents.
Outcome:	The proposed model achieves f1-scores around 0.85 on a publicly available large scale CT registry of around 360K protocols.

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Xiao Luo, Binqi Chen, Haixin Wang, Zhiping Xiao, Ming Zhang, Yizhou Sun

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

Challenge:	Recent data-driven methods often use graph neural networks (GNNs) to learn interactions between objects.
Approach:	They propose prompting techniques for dynamical system modeling and evaluate their performance . they find that large language models demonstrate competitive performance without training .
Outcome:	The proposed methods show competitive performance without training compared to state-of-the-art methods in dynamical system modeling.

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

Can LLMs Convert Graphs to Text-Attributed Graphs? (2025.naacl-long)

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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Zehong Wang, Sidney Liu, Zheyuan Zhang, Tianyi Ma, Chuxu Zhang, Yanfang Ye

Line Graph Enhanced AMR-to-Text Generation with Mix-Order Graph Attention Networks (2020.acl-main)

Challenge:	Existing approaches to model graph-structured data are limited by the availability of text-attributed graph data.
Approach:	They propose a method to convert existing graphs into text-attributed graphs using large language models.
Outcome:	The proposed method outperforms existing approaches that manually design node features on text-free graphs.

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Yanbin Zhao, Lu Chen, Zhi Chen, Ruisheng Cao, Su Zhu, Kai Yu

Hierarchical Information Matters: Text Classification via Tree Based Graph Neural Network (2022.coling-1)

Challenge:	Existing graph-to-sequence approaches use graph neural networks as encoders, but they lack the structure information needed to translate AMR into the graph-based data.
Approach:	They propose a graph-to-sequence task which aims to recover natural language from Abstract Meaning Representations (AMR) they adopt graph attention networks with higher-order neighborhood information to explore the edge relations in AMR graphs.
Outcome:	The proposed framework achieves state-of-the-art performance on English AMR benchmark datasets and is able to translate the AMR semantics into the natural language.

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Chong Zhang, He Zhu, Xingyu Peng, Junran Wu, Ke Xu

Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering (2020.emnlp-main)

Challenge:	Text classification is a primary task in natural language processing (NLP).
Approach:	They propose a graph neural network (HINT) that makes full use of hierarchical information contained in the text for the task of text classification.
Outcome:	The proposed method outperforms the state-of-the-art methods on popular benchmarks while having a simple structure and few parameters.

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Yanlin Feng, Xinyue Chen, Bill Yuchen Lin, Peifeng Wang, Jun Yan, Xiang Ren

Question Answering

Challenge:	Existing work on augmenting question answering models with external knowledge (e.g., knowledge graphs) lacks transparency into the model’s prediction rationale.
Approach:	They propose a knowledge-aware approach that equips pre-trained language models with a multi-hop relational reasoning module that performs multi-relational reasoning over subgraphs extracted from external knowledge graphs.
Outcome:	The proposed model performs multi-hop, multi-relational reasoning over subgraphs extracted from external knowledge graphs.

Structured Dialogue Policy with Graph Neural Networks (C18-1)

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Lu Chen, Bowen Tan, Sishan Long, Kai Yu

Natural Language Interfaces

Challenge:	Recent advances focus on improving DRL-based dialogue policy optimization.
Approach:	They propose to design a graph neural network structure that is better suited for dialogue management.
Outcome:	The proposed approach outperforms state-of-the-art approaches in 18 tasks of the PyDial benchmark.

Graph Neural Networks for Multiparallel Word Alignment (2022.findings-acl)

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Ayyoob Imani, Lütfi Kerem Senel, Masoud Jalili Sabet, François Yvon, Hinrich Schuetze

Biomedical Event Extraction with Hierarchical Knowledge Graphs (2020.findings-emnlp)

Challenge:	Generally, word alignment algorithms only use bitext and do not make use of the fact that many parallel corpora are multiparallel.
Approach:	They propose a multiparallel word alignment graph and graph neural networks to exploit it . they add and remove edges from the initial alignments and generalize the model .
Outcome:	The proposed method outperforms previous work on three word alignment datasets and on a downstream task.

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Kung-Hsiang Huang, Mu Yang, Nanyun Peng

An Edge-Enhanced Hierarchical Graph-to-Tree Network for Math Word Problem Solving (2021.findings-emnlp)

Challenge:	Biomedical event extraction is critical in understanding biomolecular interactions described in scientific corpus.
Approach:	They propose to integrate domain knowledge from Unified Medical Language System (UMLS) to a pre-trained language model using Graph Edge-conditioned Attention Networks and hierarchical graph representation.
Outcome:	The proposed approach achieves 1.41% F1 and 3.19% F1 improvements on the BioNLP 2011 GENIA Event Extraction task.

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Qinzhuo Wu, Qi Zhang, Zhongyu Wei

Numerical Reasoning

Challenge:	Existing work on graph neural networks to capture word relationships neglects the rest of the problem.
Approach:	They propose an edge-enhanced hierarchical graph encoder to incorporate edge label information.
Outcome:	The proposed model can improve performance on the MAWPS and Math23K datasets compared with state-of-the-art methods.

Cross-lingual Transfer for Text Classification with Dictionary-based Heterogeneous Graph (2021.findings-emnlp)

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Nuttapong Chairatanakul, Noppayut Sriwatanasakdi, Nontawat Charoenphakdee, Xin Liu, Tsuyoshi Murata

Information Retrieval

Challenge:	Existing approaches to cross-lingual text classification require task-specific training data in high-resource sources . labeling cost, task characteristics, and privacy concerns can hinder the use of cross-linguistic training .
Approach:	They propose a dictionary-based heterogeneous graph (DHGNet) that uses bilingual dictionaries for task-independent word embeddings.
Outcome:	The proposed method outperforms pretrained models even though it does not access to large corpora.

A Neural Multi-digraph Model for Chinese NER with Gazetteers (P19-1)

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Ruixue Ding, Pengjun Xie, Xiaoyan Zhang, Wei Lu, Linlin Li, Luo Si

PM2F2N: Patient Multi-view Multi-modal Feature Fusion Networks for Clinical Outcome Prediction (2022.findings-emnlp)

Challenge:	Existing approaches to incorporating gazetteers into NER systems rely on manually defined selection strategies or handcrafted templates, which may not lead to optimal effectiveness.
Approach:	They propose to use graph neural networks to automatically learn how to incorporate multiple gazetteers into an NER system by capturing the information that the gazetteer offers.
Outcome:	The proposed model outperforms existing methods on Chinese NER datasets while incorporating rich gazetteer information while resolving ambiguities.

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Ying Zhang, Baohang Zhou, Kehui Song, Xuhui Sui, Guoqing Zhao, Ning Jiang, Xiaojie Yuan

BiSyn-GAT+: Bi-Syntax Aware Graph Attention Network for Aspect-based Sentiment Analysis (2022.findings-acl)

Challenge:	Existing methods focused on time series data but ignored clinical notes . fusion of multi-modal features of patients from different views is not feasible due to the time series and clinical notes data being stored as time series.
Approach:	They propose to combine time series and clinical notes to fuse multi-modal features of patients from different perspectives using graph neural networks.
Outcome:	The proposed method is superior to existing models on MIMIC-III benchmark.

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Shuo Liang, Wei Wei, Xian-Ling Mao, Fei Wang, Zhiyong He

Discrete Opinion Tree Induction for Aspect-based Sentiment Analysis (2022.acl-long)

Challenge:	Aspect-based sentiment analysis is challenging because a sentence may contain multiple aspects or complicated relationships.
Approach:	They propose a bi-syntax aware Graph Attention Network to model the context of every aspect and sentiment relations across aspects for learning.
Outcome:	The proposed model outperforms the state-of-the-art methods on four benchmark datasets.

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Chenhua Chen, Zhiyang Teng, Zhongqing Wang, Yue Zhang

VISPool: Enhancing Transformer Encoders with Vector Visibility Graph Neural Networks (2024.findings-acl)

Challenge:	Dependency trees are used for aspect-based sentiment classification but are not optimized for aspect classification.
Approach:	They propose an aspect-specific and language-agnostic discrete latent opinion tree model as an alternative structure to explicit dependency trees.
Outcome:	The proposed model can achieve competitive performance and interpretability on six English benchmarks and one Chinese dataset.

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Tuna Alikaşifoğlu, Arda Aras, Aykut Koc

JobXMLC: EXtreme Multi-Label Classification of Job Skills with Graph Neural Networks (2023.findings-eacl)

Challenge:	Existing graph-based graph construction methods rely on static graphs and are not scalable with increasing document and word counts.
Approach:	They propose a dynamic graph construction method based on vector visibility graphs (VVGs) they propose scalable model architecture that integrates VVG convolutional networks into transformer pipelines.
Outcome:	The proposed model outperforms baseline models on the GLUE benchmark datasets.

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Nidhi Goyal, Jushaan Kalra, Charu Sharma, Raghava Mutharaju, Niharika Sachdeva, Ponnurangam Kumaraguru

DGLF: A Dual Graph-based Learning Framework for Multi-modal Sarcasm Detection (2024.emnlp-main)

Challenge:	Existing approaches to predict missing skills are limited to contextual modelling and do not exploit inter-relational structures like job-job and job-skill relationships.
Approach:	They propose a skill prediction framework that exploits structural relationships to predict missing skills using job descriptions.
Outcome:	The proposed framework outperforms the state-of-the-art approaches by 6% in precision and 3% in recall on real-world recruitment datasets.

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Zhihong Zhu, Kefan Shen, Zhaorun Chen, Yunyan Zhang, Yuyan Chen, Xiaoqi Jiao, Zhongwei Wan, Shaorong Xie, Wei Liu, Xian Wu, Yefeng Zheng

Taming Language Models for Text-attributed Graph Learning with Decoupled Aggregation (2025.acl-long)

Challenge:	Existing methods for multimodal sarcasm detection neglect high-order relationships and underestimate high-frequency messages.
Approach:	They propose a Dual Graph-based Learning Framework to capture inter-modal inconsistencies . they propose combining a hypergraph and a vanilla graph to achieve enhanced propagation .
Outcome:	The proposed model outperforms existing state-of-the-art methods on two benchmark datasets.

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Chuang Zhou, Zhu Wang, Shengyuan Chen, Jiahe Du, Qiyuan Zheng, Zhaozhuo Xu, Xiao Huang

UCGRec: User-Centric Graph Learning for LLM-based Sequential Recommendation (2026.findings-acl)

Challenge:	Existing approaches to learning text-attributed graphs neglect interaction between textual and structural information.
Approach:	They propose a framework that integrates textual and structural information into TAG learning . they propose combining semantic aggregation and structural aggregations to improve learning a .
Outcome:	The proposed framework outperforms state-of-the-art learning methods while requiring less resources.

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HanBeul Kim, CheolWon Na, Suyoung Bae, YunSeok Choi, Jee-Hyong Lee

ReaRev: Adaptive Reasoning for Question Answering over Knowledge Graphs (2022.findings-emnlp)

Challenge:	Existing methods for sequential recommendation rely primarily on item descriptions or utilize user preferences independently.
Approach:	They propose a method that integrates diverse user-relevant preference signals into a unified user-centric graph and injects the graph-based knowledge into the LLM through end-to-end training with graph neural networks.
Outcome:	The proposed method outperforms conventional and state-of-the-art methods on four widely used sequential real-world recommendation datasets.

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Costas Mavromatis, George Karypis

Open-Topic False Information Detection on Social Networks with Contrastive Adversarial Learning (2022.emnlp-main)

Challenge:	Knowledge Graph Question Answering (KGQA) involves retrieving entities as answers from a Knowledge Flow using natural language queries.
Approach:	They propose a method to decode a question into instructions that are dense question representations used to guide the KG traversals.
Outcome:	The proposed method improves instruction decoding and execution by using a KG-aware information to update the initial instructions.

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Guanghui Ma, Chunming Hu, Ling Ge, Hong Zhang

Responsible & Trustworthy NLP

Challenge:	Existing models for false information detection on social networks are too harsh for actual social networks that contain both seen and unseen topics simultaneously.
Approach:	They propose an open-topic scenario that assumes that all test data topics are seen or unseen by the model, but which is too harsh for actual social networks that contain both seen and unseened topics simultaneously.
Outcome:	The proposed model improves on two benchmark datasets and a variety of graph neural networks on two social networks and shows that it is more accurate than existing models.

Graph Reasoning for Question Answering with Triplet Retrieval (2023.findings-acl)

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Shiyang Li, Yifan Gao, Haoming Jiang, Qingyu Yin, Zheng Li, Xifeng Yan, Chao Zhang, Bing Yin

Graph Ensemble Learning over Multiple Dependency Trees for Aspect-level Sentiment Classification (2021.naacl-main)

Challenge:	Existing methods to answer complex questions require reasoning over knowledge graphs (KGs) state-of-the-art methods constrain retrieved knowledge in local subgraphs and discard more diverse triplets that are disconnected but useful for question answering.
Approach:	They propose a method to retrieve the most relevant triplets from KGs and then rerank them, which are then concatenated with questions to be fed into language models.
Outcome:	The proposed method outperforms state-of-the-art methods on commonsenseQA and OpenbookQA datasets with 4.6% absolute accuracy.

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Xiaochen Hou, Peng Qi, Guangtao Wang, Rex Ying, Jing Huang, Xiaodong He, Bowen Zhou

Text is All You Need: LLM-enhanced Incremental Social Event Detection (2025.acl-long)

Challenge:	Recent work on aspect-level sentiment classification has shown that syntactic information is effective in capturing long-range syntaktic relations that are obscure from the surface form.
Approach:	They propose a graph ensemble technique that integrates syntactic structures with GNNs to better leverage syntaktic information in the face of parsing errors.
Outcome:	The proposed model outperforms models with single dependency tree and beats other models without adding model parameters.

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Zitai Qiu, Congbo Ma, Jia Wu, Jian Yang

Graph-based Dependency Parsing with Graph Neural Networks (P19-1)

Challenge:	Existing state-of-the-art (SOTA) SED models rely on graph neural networks (GNNs) Existing SED frameworks rely heavily on GNNs, which require complex graph construction and time-consuming training processes.
Approach:	They propose a framework that leverages the rich background knowledge of large language models to formalize and disambiguate short texts by completing abbreviations and summarizing informal expressions.
Outcome:	The proposed framework outperforms existing models on two challenging real-world datasets.

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Tao Ji, Yuanbin Wu, Man Lan

Green & Sustainable NLP

Challenge:	In graph-based dependency parsers, learning representations is gaining in importance, and we use graph neural networks to learn the representations.
Approach:	They propose to use graph neural networks to learn dependency tree nodes and propose to add a new aggregation function to the system.
Outcome:	The proposed model achieves the best UAS and LAS on PTB (96.0%, 94.3%) without using external resources.

SiMFy: A Simple Yet Effective Approach for Temporal Knowledge Graph Reasoning (2023.findings-emnlp)

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Zhengtao Liu, Lei Tan, Mengfan Li, Yao Wan, Hai Jin, Xuanhua Shi

Enhancing Dialogue Generation via Dynamic Graph Knowledge Aggregation (2023.acl-long)

Challenge:	Existing models for temporal knowledge graph reasoning suffer from low training efficiency and insufficient generalization ability.
Approach:	They propose a temporal knowledge graph reasoning approach that uses multilayer perceptron to model the structural dependencies of events and adopts a fixed-frequency strategy to incorporate historical frequency during inference.
Outcome:	The proposed model achieves state-of-the-art performance with faster convergence speed and better generalization ability.

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Chen Tang, Hongbo Zhang, Tyler Loakman, Chenghua Lin, Frank Guerin

Rumor Detection on Social Media with Temporal Propagation Structure Optimization (2025.coling-main)

Challenge:	Existing graph neural networks (GNNs) teach message passing on a graph from text, resulting in a semantic gap between graph knowledge and text.
Approach:	They propose a framework to integrate external graph knowledge into chatbots by coagulating representations of both text and graph knowledge.
Outcome:	The proposed framework outperforms state-of-the-art (SOTA) baselines on dialogue generation.

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Xingyu Peng, Junran Wu, Ruomei Liu, Ke Xu

Challenge:	Existing methods for detecting rumors on social media neglect the temporal aspect of rumor propagation.
Approach:	They propose a method that incorporates temporal information by building a weighted propagation tree and a coding tree.
Outcome:	The proposed approach preserves essential structure of rumor propagation while reducing noise.

Learning to Update Knowledge Graphs by Reading News (D19-1)

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Jizhi Tang, Yansong Feng, Dongyan Zhao

Exploring the Potential of Large Language Models for Heterophilic Graphs (2025.naacl-long)

Challenge:	Existing methods to update knowledge graphs rely on elaborately designed IE systems and domain-specific rules.
Approach:	They propose a novel neural network method to update knowledge graphs (KGs) they use a text-based attention mechanism to guide updating messages through KGs .
Outcome:	The proposed method can effectively broadcast news information to KG structures and perform necessary link-adding or link-deleting operations to ensure the KG up-to-date according to news snippets.

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Yuxia Wu, Shujie Li, Yuan Fang, Chuan Shi

Better Feature Integration for Named Entity Recognition (2021.naacl-main)

Challenge:	Existing approaches for heterophilic graphs overlook rich textual data associated with nodes, which could unlock deeper insights into their heterophilistic contexts.
Approach:	They propose a two-stage framework to enhance node classification on heterophilic graphs by leveraging open-world knowledge encoded by large language models.
Outcome:	The proposed framework can be used to better characterize heterophilic graphs, where neighboring nodes often exhibit different labels.

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Lu Xu, Zhanming Jie, Wei Lu, Lidong Bing

Syntactic Text Processing

Challenge:	Existing approaches to named entity recognition (NER) focus on stacking the LSTM and graph neural networks (GCNs) however, the exact interaction mechanism between the two types of features is not clear and the performance gain is not significant.
Approach:	They propose a model that incorporates both types of features with a Synergized-LSTM which captures how the two types of feature interact.
Outcome:	The proposed model achieves better performance than previous approaches while requiring fewer parameters.

Towards Zero-shot Relation Extraction in Web Mining: A Multimodal Approach with Relative XML Path (2023.findings-emnlp)

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Zilong Wang, Jingbo Shang

Biomedical Entity Representation with Graph-Augmented Multi-Objective Transformer (2024.findings-naacl)

Challenge:	Existing methods for zero-shot relation extraction do not take into account relationships between text nodes within and across web pages.
Approach:	They propose a new approach for zero-shot relation extraction in web mining that encodes the shortest relative paths in the Document Object Model tree of the web page.
Outcome:	The proposed method outperforms the state-of-the-art methods on public benchmarks on semi-structured web pages.

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Andrey Sakhovskiy, Natalia Semenova, Artur Kadurin, Elena Tutubalina

MARK: Multi-agent Collaboration with Ranking Guidance for Text-attributed Graph Clustering (2025.findings-acl)

Challenge:	Modern biomedical concept representations are mostly trained on synonymous concept names from a biomedically knowledge base graph, ignoring the inter-concept interactions and a concept’s local neighborhood.
Approach:	They propose a Graph-Augmented Multi-Objective Transformer which captures both inter-concept and intra-conception interactions from the multilingual UMLS graph.
Outcome:	The proposed model captures inter- and intra-concept interactions from the multilingual UMLS graph using pre-trained language models and graph neural networks.

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Yiwei Fu, Yuxing Zhang, Chunchun Chen, JianwenMa JianwenMa, Quan Yuan, Rong-Cheng Tu, Xinli Huang, Wei Ye, Xiao Luo, Minghua Deng

Graph Enhanced Contrastive Learning for Radiology Findings Summarization (2022.acl-long)

Challenge:	Existing approaches to cluster graphs with GNNs are limited due to label scarcity.
Approach:	They propose to leverage large language models to enhance text-attributed graph clustering by using three LLMs as ranking-based supervision signals.
Outcome:	The proposed approach generates reliable guidance using collaboration of three LLM-based agents as ranking-based supervision signals.

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Jinpeng Hu, Zhuo Li, Zhihong Chen, Zhen Li, Xiang Wan, Tsung-Hui Chang

What Makes a Good Reasoning Chain? Uncovering Structural Patterns in Long Chain-of-Thought Reasoning (2025.emnlp-main)

Challenge:	Existing methods for automating impression generation have limited the relationship between extra knowledge and the original findings.
Approach:	They propose a framework for automating impression generation that exploits extra knowledge and original findings . they propose combining key words and their relations to extract critical information .
Outcome:	The proposed framework exploits extra knowledge and the original findings in an integrated way . the state-of-the-art results on two datasets confirm the effectiveness of the proposed method .

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Gangwei Jiang, Yahui Liu, Zhaoyi Li, Wei Bi, Fuzheng Zhang, Linqi Song, Ying Wei, Defu Lian

Reasoning

Challenge:	Recent advances in reasoning with large language models have popularized Long Chain-of-Thought (LCoT) a framework that converts sequential LCoTs into hierarchical tree structures enables deeper structural analysis of LLM reasoning.
Approach:	They propose a framework that converts sequential LCoTs into hierarchical tree structures and enables deeper structural analysis of LLM reasoning.
Outcome:	The proposed framework can be used to analyze LLM reasoning in a variety of tasks and models.

Towards Multi-Modal Sarcasm Detection via Hierarchical Congruity Modeling with Knowledge Enhancement (2022.emnlp-main)

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Hui Liu, Wenya Wang, Haoliang Li

Graph Neural Network Enhanced Retrieval for Question Answering of Large Language Models (2025.naacl-long)

Challenge:	Sarcasm is a linguistic phenomenon indicating a discrepancy between literal meanings and implied intentions.
Approach:	They propose a hierarchical framework for sarcasm detection by exploring atomic-level congruity and composition-level convergence.
Outcome:	The proposed model outperforms existing methods on a public sarcasm detection dataset based on Twitter .

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Zijian Li, Qingyan Guo, Jiawei Shao, Lei Song, Jiang Bian, Jun Zhang, Rui Wang

Natural Language Interfaces

Challenge:	Existing retrieval methods divide reference documents into passages, treating them in isolation. Existing methods only use contiguous passages or keywords.
Approach:	They propose a retrieval method that leverages graph neural networks to exploit relatedness between passages to enhance retrieval.
Outcome:	The proposed method improves retrieval by exploiting the relatedness between passages.

DynGL-SDP: Dynamic Graph Learning for Semantic Dependency Parsing (2022.coling-1)

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Bin Li, Miao Gao, Yunlong Fan, Yikemaiti Sataer, Zhiqiang Gao, Yaocheng Gui

G-Safeguard: A Topology-Guided Security Lens and Treatment on LLM-based Multi-agent Systems (2025.acl-long)

Challenge:	Existing parsers that learn graph representations based on static graphs are error-prone and disjointed . Graph-based parser can parse sentences efficiently but suffer from error propagation .
Approach:	They propose a dynamic graph learning framework to learn graph representations based on a static graph constructed by an existing parser.
Outcome:	The proposed parser outperforms the previous parsers on the SemEval-2015 task 18 dataset in three languages.

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Shilong Wang, Guibin Zhang, Miao Yu, Guancheng Wan, Fanci Meng, Chongye Guo, Kun Wang, Yang Wang

Robustness in NLP

Challenge:	Large Language Model (LLM)-based Multi-agent Systems (MAS) have demonstrated remarkable capabilities in various complex tasks, but their vulnerability to adversarial attacks, misinformation propagation, and unintended behaviors have raised significant concerns.
Approach:	They propose a topology-guided security lens and treatment for robust LLM-MAS that leverages graph neural networks to detect anomalies on the multi-agent utterance graph and employ topological intervention for attack remediation.
Outcome:	Experiments show that the proposed security lens recovers 40% of the performance under various attack strategies and integrates with mainstream MAS with security guarantees.

Knowledge Graph Pooling and Unpooling for Concept Abstraction (2025.coling-main)

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Juan Li, Wen Zhang, Zhiqiang Liu, Mingchen Tu, Mingyang Chen, Ningyu Zhang, Shijian Li

SSEGCN: Syntactic and Semantic Enhanced Graph Convolutional Network for Aspect-based Sentiment Analysis (2022.naacl-main)

Challenge:	Knowledge graph embedding (KGE) aims to embed entities and relations as vectors in a continuous space.
Approach:	They propose a framework with KG Pooling and unpooling and Contrastive Learning to abstract and encode latent concepts for better KG prediction.
Outcome:	The proposed framework outperforms baselines on link prediction task.

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Zheng Zhang, Zili Zhou, Yanna Wang

AGRec: Adapting Autoregressive Decoders with Graph Reasoning for LLM-based Sequential Recommendation (2025.findings-acl)

Challenge:	Aspect-based Sentiment Analysis (ABSA) aims to predict sentiment polarity towards aspects in sentences . a novel model for ABSA is proposed, but how to harness it is still a challenge .
Approach:	They propose a syntactic and semantic enhanced Graph Convolutional Network (SSEGCN) model for ABSA task using aspect-aware attention mechanism and self-attention.
Outcome:	The proposed model outperforms state-of-the-art methods on benchmark datasets.

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Xinfeng Wang, Jin Cui, Fumiyo Fukumoto, Yoshimi Suzuki

Curriculum Learning for Graph Neural Networks: A Multiview Competence-based Approach (2023.acl-long)

Challenge:	Autoregressive decoders in large language models excel at capturing sequential behaviors for generative recommendations, but they lack graph-structured user-item interactions, which are widely recognized as beneficial.
Approach:	They propose a novel algorithm that adapts LLMs’ decoders with graph reasoning for recommendation by augmenting the decoding logits with an auxiliary GNN model to optimize token generation.
Outcome:	The proposed model outperforms state-of-the-art models in sequential recommendations.

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Nidhi Vakil, Hadi Amiri

TimeR4 : Time-aware Retrieval-Augmented Large Language Models for Temporal Knowledge Graph Question Answering (2024.emnlp-main)

Challenge:	Existing curriculum learning approaches often employ a single criterion of difficulty in their training paradigms.
Approach:	They propose a new approach that builds on graph complexity formalisms and model competence during training.
Outcome:	The proposed approach improves learning efficiency on real-world link prediction and node classification tasks.

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Xinying Qian, Ying Zhang, Yu Zhao, Baohang Zhou, Xuhui Sui, Li Zhang, Kehui Song

Be More with Less: Hypergraph Attention Networks for Inductive Text Classification (2020.emnlp-main)

Challenge:	Temporal Knowledge Graph Question Answering (TKGQA) aims to answer temporal questions using knowledge in Temporal knowledge graphs (TKTs).
Approach:	They propose a Time-aware retrieve-rewrite-retrieve-rerank framework to integrate temporal knowledge from TKGs into Large Language Models (LLMs) to reduce temporal hallucination, they propose rewrite module to rew questions using background knowledge stored in TKG's, then implement a retrieve-rank module to retrieve semantically and temporally relevant facts from Tkgs and rerank them according to temporal constraints.
Outcome:	The proposed approach achieves relative gains of 47.8% and 22.5% on two datasets, underscoring its effectiveness in boosting the temporal reasoning abilities of LLMs.

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Kaize Ding, Jianling Wang, Jundong Li, Dingcheng Li, Huan Liu

TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion (2020.emnlp-main)

Challenge:	Text classification is a critical research topic with broad applications in natural language processing. graph neural networks (GNNs) have received increasing attention but their performance is jeopardized in practice.
Approach:	They propose a model which captures long-distance interactions between words and a graph-based model which can be used to perform text classification.
Outcome:	The proposed model can achieve more expressive power with less computational consumption on the text classification task.

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Jiapeng Wu, Meng Cao, Jackie Chi Kit Cheung, William L. Hamilton

MADNet: Maximizing Addressee Deduction Expectation for Multi-Party Conversation Generation (2023.emnlp-main)

Challenge:	Existing methods for static knowledge graphs do not explicitly leverage multi-hop structural information and temporal facts from recent time steps to enhance their predictions.
Approach:	They propose a framework to leverage time-dependent temporal information to infer missing facts in temporal knowledge graphs.
Outcome:	The proposed framework achieves 10.7% improvement in Hits@10 across three standard benchmarks.

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Jia-Chen Gu, Chao-Hong Tan, Caiyuan Chu, Zhen-Hua Ling, Chongyang Tao, Quan Liu, Cong Liu

RECAL: Sample-Relation Guided Confidence Calibration over Tabular Data (2023.findings-emnlp)

Challenge:	Existing methods for multi-party conversations rely on addressee labels and can only be applied to an ideal setting where addresses are missing.
Approach:	They propose a method that maximizes addressee deduction expectation in heterogeneous graph neural networks for MPC generation.
Outcome:	The proposed method outperforms baseline models on Ubuntu IRC channel benchmarks on the task of MPC generation under a common and challenging setting where addressee labels are missing.

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Wang HaoTian, Zhen Zhang, Mengting Hu, Qichao Wang, Liang Chen, Yatao Bian, Bingzhe Wu

Pre-trained Language Model with Prompts for Temporal Knowledge Graph Completion (2023.findings-acl)

Challenge:	Various machine learning methods for tabular data lack accurate confidence estimation, which is needed for high-risk sensitive applications such as credit modeling and financial fraud detection.
Approach:	They propose a general post-training confidence calibration framework to calibrate the confidence of current machine learning models by employing graph neural networks to model the relationships between different samples.
Outcome:	The proposed framework improves the confidence estimation on tabular datasets by using graph neural networks to model the relationships between different samples.

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Wenjie Xu, Ben Liu, Miao Peng, Xu Jia, Min Peng

Dual Graph Convolutional Networks for Aspect-based Sentiment Analysis (2021.acl-long)

Challenge:	Existing methods for temporal knowledge graph completion (TKGC) focus on extracting information from timestamps and insufficiently utilizing implied information in relations.
Approach:	They propose a temporal knowledge graph completion model with prompts that converts quadruples into pre-trained language inputs and prompts to make coherent sentences with implicit semantic information.
Outcome:	The proposed model can make coherent sentences with implicit semantic information.

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Ruifan Li, Hao Chen, Fangxiang Feng, Zhanyu Ma, Xiaojie Wang, Eduard Hovy

GREENER: Graph Neural Networks for News Media Profiling (2022.emnlp-main)

Challenge:	Existing methods to model relationships between aspects and opinion words are inefficient due to informal expressions and complexity of online reviews.
Approach:	They propose a dual graph convolutional networks model that considers complementarity of syntax structures and semantic correlations simultaneously.
Outcome:	The proposed model outperforms state-of-the-art methods on three public datasets and validates it.

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Panayot Panayotov, Utsav Shukla, Husrev Taha Sencar, Mohamed Nabeel, Preslav Nakov

SALMON: A Structure-Aware Language Model with logicality and densification strategy for Temporal Knowledge Graph Reasoning (2024.findings-emnlp)

Challenge:	a new method for profiling news media on the Web addresses the factuality of reporting and bias problem . a recent study has focused on text features but has focused primarily on text .
Approach:	They propose a model that models the similarity between media outlets based on their audience overlap . they propose GREENER, which builds a graph of inter-media connections based upon audience overlap.
Outcome:	The proposed model improves on state-of-the-art models on two datasets.

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Fu Zhang, Jinghao Lin, Jingwei Cheng

A Survey of Automatic Text Summarization Using Graph Neural Networks (2022.coling-1)

Challenge:	Temporal knowledge graph reasoning (TKGR) is a crucial task that involves reasoning at known timestamps to complete the future facts.
Approach:	They propose a temporal knowledge graph reasoning model with logicality and densification strategy that captures temporal evolving pattern and structural information in TKGs.
Outcome:	The proposed model outperforms the state-of-the-art models and is based on a structure-aware language model with logicality and densification strategy.

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Marco Ferdinand Salchner, Adam Jatowt

Distilling Structured Knowledge for Text-Based Relational Reasoning (2020.emnlp-main)

Challenge:	Abstractive ATS involves generating factually correct and fluent sentences.
Approach:	They provide an overview of the use of graph neural networks (GNNs) for automatic text summarization.
Outcome:	The proposed model is based on a set of graph neural networks (GNNs) that are used to generate a concise, correct and fluent summary of a given text.

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Jin Dong, Marc-Antoine Rondeau, William L. Hamilton

‘Hello, World!’: Making GNNs Talk with LLMs (2025.findings-emnlp)

Challenge:	Existing text-based relational reasoning models lack a symbolic representation of text . performance gap between NLP models and structured models remains .
Approach:	They first pre-train a GNN on a reasoning task using structured inputs and then incorporate its knowledge into an NLP model.
Outcome:	The proposed model improves on two state-of-the-art NLP models on 13 different inductive reasoning datasets from the CLUTRR benchmark.

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Sunwoo Kim, Soo Yong Lee, Jaemin Yoo, Kijung Shin

Unleashing the Power of Language Models in Text-Attributed Graph (2023.findings-emnlp)

Challenge:	graph neural networks have shown remarkable performance across diverse graph-related tasks, but their high-dimensional hidden representations render them black boxes.
Approach:	They propose a graph-based neural network with hidden representations in the form of human-readable text.
Outcome:	The proposed GNN outperforms existing LLM-based baseline methods on node classification and link prediction.

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Haoyu Kuang, Jiarong Xu, Haozhe Zhang, Zuyu Zhao, Qi Zhang, Xuanjing Huang, Zhongyu Wei

SIR-ABSC: Incorporating Syntax into RoBERTa-based Sentiment Analysis Models with a Special Aggregator Token (2023.findings-emnlp)

Challenge:	Existing studies on graph learning on text-attributed graphs have been limited by memory cost and underutilization of relationships between nodes and words.
Approach:	They propose a Node Representation Update Pre-training Architecture based on Co-modeling text and graph to learn representations of papers and words simultaneously.
Outcome:	The proposed model outperforms baselines on the ogbn-arxiv benchmark dataset.

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Ikhyun Cho, Yoonhwa Jung, Julia Hockenmaier

Social Bot-Aware Graph Neural Network for Early Rumor Detection (2022.coling-1)

Challenge:	Existing methods to integrate syntactic dependency information into language models capture syntax . aspect-based sentiment classification tasks require a complex model to handle different aspects of a sentence .
Approach:	They propose a method to incorporate syntactic dependency information directly into transformer-based language models for Aspect-Based Sentiment Classification.
Outcome:	The proposed model outperforms existing models for aspect-based sentiment analysis tasks.

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Zhen Huang, Zhilong Lv, Xiaoyun Han, Binyang Li, Menglong Lu, Dongsheng Li

Is Graph Structure Necessary for Multi-hop Question Answering? (2020.emnlp-main)

Challenge:	Existing models do not distinguish genuine users from social bots, and their failure in identifying rumors timely.
Approach:	They propose to account for social bots’ behavior and construct a Social Bot-Aware Graph Neural Network to model early propagation of posts and then use it to detect rumors.
Outcome:	The proposed method achieves significant improvements over baselines and identifies rumors within 3 hours while maintaining more than 90% accuracy.

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Nan Shao, Yiming Cui, Ting Liu, Shijin Wang, Guoping Hu

AutoParLLM: GNN-guided Context Generation for Zero-Shot Code Parallelization using LLMs (2025.naacl-long)

Challenge:	Existing studies focus on multi-hop question answering across multiple documents or paragraphs.
Approach:	They propose a graph neural network to deal with graph structure in textual multi-hop reasoning . they propose 'self-attention' and propose removing entire graph structure may not hurt the final results .
Outcome:	The proposed model shows that graph-attention or the entire graph structure can be replaced by self-attention . hotpotQA is a widely used benchmark for multi-hop question answering .

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Quazi Ishtiaque Mahmud, Ali TehraniJamsaz, Hung D Phan, Le Chen, Mihai Capotă, Theodore L. Willke, Nesreen K. Ahmed, Ali Jannesari

CACL: Community-Aware Heterogeneous Graph Contrastive Learning for Social Media Bot Detection (2024.findings-acl)

Challenge:	In-Context Learning (ICL) is a powerful technique to augment the capabilities of LLMs for a diverse range of tasks.
Approach:	They propose a way to generate context using guidance from graph neural networks to generate efficient parallel codes.
Outcome:	The proposed method improves state-of-the-art LLMs by 19.9% and 6.48% on NAS and rodinia benchmarks.

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Sirry Chen, Shuo Feng, Liang Songsong, Chen-Chen Zong, Jing Li, Piji Li

Deep Attention Diffusion Graph Neural Networks for Text Classification (2021.emnlp-main)

Challenge:	Existing methods for social media bot detection neglect community structure and poor model generalization due to the relatively small scale of the dataset.
Approach:	They propose a framework that constructs social networks as heterogeneous graphs and uses community-aware modules to mine hard positive and hard negative samples for supervised graph contrastive learning.
Outcome:	The proposed framework outperforms baselines on three social media bot benchmarks.

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Yonghao Liu, Renchu Guan, Fausto Giunchiglia, Yanchun Liang, Xiaoyue Feng

GCNet: Global-and-Context Collaborative Learning for Aspect-Based Sentiment Analysis (2024.lrec-main)

Challenge:	Existing methods for text classification based on graph neural networks (GNNs) consider only one-hop neighborhoods and low-frequency information within texts, which suffer from over-smoothing issues if many graph layers are stacked.
Approach:	They propose a deep attention diffusion Graph Neural Network model to learn text representations by bridging the chasm of interaction difficulties between a word and its distant neighbors.
Outcome:	The proposed model outperforms existing methods on standard benchmark datasets on a set of textual features.

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Ting Zhou, Ying Shen, Yinghui Li

GALLa: Graph Aligned Large Language Models for Improved Source Code Understanding (2025.acl-long)

Challenge:	Existing methods for analyzing aspect terms are focused on extracting semantic information inherent within the sentence.
Approach:	They propose a GCNet that explicitly leverages global semantic information to guide context encoding.
Outcome:	The proposed model outperforms state-of-the-art methods on three public datasets.

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Ziyin Zhang, Hang Yu, Sage Lee, Peng Di, Jianguo Li, Rui Wang

STaCK: Sentence Ordering with Temporal Commonsense Knowledge (2021.emnlp-main)

Challenge:	Programming languages have rich semantics that are represented by graphs and not available from the surface form of source code.
Approach:	They propose to use graph neural networks and cross-modal alignment technologies to inject structural information of code into LLMs as an auxiliary task during finetuning.
Outcome:	The proposed framework improves on five code tasks with six different baseline LLMs, while incurring no cost at inference time.

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Deepanway Ghosal, Navonil Majumder, Rada Mihalcea, Soujanya Poria

Reasoning

Challenge:	Existing approaches to sentence order prediction ignore the importance of document level global information, i.e., while predicting relative order of two sentences (s i , s j) other sentences sk from the same document do not play any role.
Approach:	They propose a framework based on graph neural networks and temporal commonsense knowledge to model global information and predict relative order of sentences.
Outcome:	The proposed method is naturally suitable for order prediction on five different datasets and has potential applications in the evaluation of the quality of machinegenerated documents.

Lifelong Model Editing with Graph-Based External Memory (2025.findings-acl)

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Yash Kumar Atri, Ahmed Alaa, Thomas Hartvigsen

An AMR-based Link Prediction Approach for Document-level Event Argument Extraction (2023.acl-long)

Challenge:	Existing methods for post-training model editing suffer from overfitting and catastrophic forgetting.
Approach:	They propose a framework that leverages hyperbolic geometry and graph neural networks for precise and stable model edits.
Outcome:	Experiments on CounterFact, CounterFACT+, and MQuAKE with GPT2-XL and GPT-J show that HYPE significantly enhances edit stability, factual accuracy, and multi-hop reasoning.

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Yuqing Yang, Qipeng Guo, Xiangkun Hu, Yue Zhang, Xipeng Qiu, Zheng Zhang

Representation Learning

Challenge:	Recent work has introduced Abstract Meaning Representation (AMR) for Document-level Event Argument Extraction (Doc-level EAE) however, in these works AMR is used only implicitly, for instance, as additional features or training signals.
Approach:	They propose a novel AMR-based graph structure which uses graph neural networks to find event arguments from unstructured text.
Outcome:	The proposed graph structure outperforms the state-of-the-art models by 3.63pt and 2.33pt F1 and reduces inference time by 56%.

S2GSL: Incorporating Segment to Syntactic Enhanced Graph Structure Learning for Aspect-based Sentiment Analysis (2024.acl-long)

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Bingfeng Chen, Qihan Ouyang, Yongqi Luo, Boyan Xu, Ruichu Cai, Zhifeng Hao

Hypergraph-Based Session Modeling: A Multi-Collaborative Self-Supervised Approach for Enhanced Recommender Systems (2024.lrec-main)

Challenge:	Existing graph-based approaches to learn static structures and dynamic latent trees are lacking in incorporating semantic and syntactic information simultaneously within complex global structures.
Approach:	They propose a graph-based framework that incorporates semantic and syntactic information simultaneously within global structures.
Outcome:	The proposed framework removes irrelevant contexts and syntactic dependencies and achieves complementarity across diverse structures.

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Xiangping Zheng, Bo Wu, Alex X. Zhang, Wei Li

Responsible & Trustworthy NLP

Challenge:	Presently, graph-based recommendations are limited by session dependencies and data sparsity in real-world scenarios.
Approach:	They propose a method which uses multi-collaborative self-supervised learning in hypergraph neural networks to model item transitions and to mitigate the challenges of data sparsity.
Outcome:	The proposed method outperforms existing methods in a number of domains and consistently outperformed existing methods.

DiSCoMaT: Distantly Supervised Composition Extraction from Tables in Materials Science Articles (2023.acl-long)

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Tanishq Gupta, Mohd Zaki, Devanshi Khatsuriya, Kausik Hira, N M Anoop Krishnan, null Mausam

Dynamic Heterogeneous-Graph Reasoning with Language Models and Knowledge Representation Learning for Commonsense Question Answering (2023.acl-long)

Challenge:	Advanced knowledge of a science or engineering domain is typically found in domain-specific research papers.
Approach:	They propose a task of extracting compositions of materials from tables in materials science papers to facilitate research in this direction.
Outcome:	The proposed model outperforms previous table processing architectures by significant margins.

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Yujie Wang, Hu Zhang, Jiye Liang, Ru Li

Error Detection for Text-to-SQL Semantic Parsing (2023.findings-emnlp)

Challenge:	Existing methods for QA use knowledge graphs, but they ignore subgraph optimization and subgraph deepening.
Approach:	They propose a dynamic heterogeneous-graph reasoning method with LMs and knowledge representation learning that optimizes the structure and knowledge representing of the HKG using a two-stage pruning strategy and knowledge-representation learning.
Outcome:	The proposed method improves on existing methods at CommonsenseQA and OpenBookQA.

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Shijie Chen, Ziru Chen, Huan Sun, Yu Su

Programming Languages in NLP

Challenge:	Existing text-to-SQL parsers are often over-confident, thus casting doubt on their trustworthiness when deployed for real use.
Approach:	They propose a parser-independent error detection model for text-to-SQL semantic parsing . they use a language model of code as its bedrock and graph neural networks to learn structural features of queries .
Outcome:	The proposed model outperforms parser-dependent uncertainty metrics on three strong parsers . it could improve the performance and usability of text-to-SQL semantic parsing, it is shown .

GNN-SL: Sequence Labeling Based on Nearest Examples via GNN (2023.findings-acl)

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Shuhe Wang, Yuxian Meng, Rongbin Ouyang, Jiwei Li, Tianwei Zhang, Lingjuan Lyu, Guoyin Wang

Intrinsic Subgraph Generation for Interpretable Graph Based Visual Question Answering (2024.lrec-main)

Challenge:	Existing sequence labeling algorithms can be decomposed into two parts .
Approach:	They propose a graph neural networks sequence labeling (GNN-SL) that augments the vanilla SL model output with similar tagging examples retrieved from the whole training set.
Outcome:	The proposed model performs well on three sequence labeling tasks.

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Pascal Tilli, Ngoc Thang Vu

TRN-R1-Zero: Text-rich Network Reasoning via LLMs with Reinforcement Learning Only (2026.acl-long)

Challenge:	Visual Question Answering (VQA) is acknowledged as a challenging multi-modal task for Machine Learning (ML).
Approach:	They propose an interpretable approach for graph-based Visual Question Answering . their model is designed to intrinsically produce a subgraph during the question-answering process as its explanation .
Outcome:	The proposed model outperforms existing explainable methods on a graph-based VQA dataset.

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Yilun Liu, Ruihong Qiu, Zi Huang

Reasoning

Challenge:	Recent large language model-based approaches often overlook graph context or depend on distillation from larger models, limiting generalisation.
Approach:	They propose a framework for zero-shot reasoning on text-rich networks . they use a Neighbour-aware Group Relative Policy Optimisation objective .
Outcome:	The proposed framework optimises base LLMs using a Neighbour-aware group relative policy optimisation objective based on a novel margin gain metric for the informativeness of neighbouring signals .

Enhancing Emotion Recognition in Conversation via Multi-view Feature Alignment and Memorization (2023.findings-emnlp)

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Guiyang Hou, Yongliang Shen, Wenqi Zhang, Wei Xue, Weiming Lu

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

Challenge:	Emotion recognition in conversation (ERC) is an advanced capability of conversational AI systems.
Approach:	They propose a semi-parametric paradigm for Emotion Recognition in conversation that uses supervised contrastive learning to align semantic-view and context-view features.
Outcome:	The proposed model achieves state-of-the-art on four widely used benchmarks.

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

Question Answering

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

Graphically Speaking: Unmasking Abuse in Social Media with Conversation Insights (2025.acl-long)

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Célia Nouri, Chloé Clavel, Jean-Philippe Cointet

Linking Adaptive Structure Induction and Neuron Filtering: A Spectral Perspective for Aspect-based Sentiment Analysis (2024.lrec-main)

Challenge:	Existing approaches to detect abusive language often ignore conversational context, leading to inconsistent and sometimes inconclusive results.
Approach:	They propose a graph neural network approach that uses conversational context to model social media conversations as graphs, where nodes represent comments and edges capture reply structures.
Outcome:	The proposed model outperforms baseline and linear context-aware methods and achieves significant improvements in F1 scores.

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Hao Niu, Maoyi Wang, Yun Xiong, Biao Yang, Xing Jia, Zhonglei Guo

GAMIC: Graph-Aligned Molecular In-context Learning for Molecule Analysis via LLMs (2025.findings-emnlp)

Challenge:	incorporating structure information can improve the performance of aspect-based sentiment analysis.
Approach:	They propose a method to conduct neuron-level manipulations on word representations in the frequency domain.
Outcome:	The proposed method can achieve or come close to state-of-the-art in ABSA.

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Ali Al Lawati, Jason S Lucas, Zhiwei Zhang, Prasenjit Mitra, Suhang Wang

Generative Subgraph Retrieval for Knowledge Graph–Grounded Dialog Generation (2024.emnlp-main)

Challenge:	Current methods for retrieving large language models rely on molecule feature similarity, such as Morgan fingerprints, which do not adequately capture the global molecular and atom-binding relationships.
Approach:	They propose a self-supervised learning technique that embeds demonstration examples into the input prompt.
Outcome:	The proposed technique outperforms simple Morgan-based retrieval methods across tasks by up to 45%.

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Jinyoung Park, Minseok Joo, Joo-Kyung Kim, Hyunwoo Kim

RG-VQA: Leveraging Retriever-Generator Pipelines for Knowledge Intensive Visual Question Answering (2025.findings-emnlp)

Challenge:	Existing methods for knowledge graph–grounded dialog generation fail to leverage the rich knowledge of pretrained language models.
Approach:	They propose a method for dialog generation that integrates dialog history with a knowledge graph.
Outcome:	The proposed method achieves state-of-the-art in knowledge graph–grounded dialog generation on OpenDialKG and KOMODIS datasets.

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Settaluri Lakshmi Sravanthi, Pulkit Agarwal, Debjyoti Mondal, Rituraj Singh, Subhadarshi Panda, Ankit Mishra, Kiran Pradeep, Srihari K B, Godawari Sudhakar Rao, Pushpak Bhattacharyya

StructAM: Enhancing Address Matching through Semantic Understanding of Structure-aware Information (2024.lrec-main)

Challenge:	Existing methods to improve the reasoning capabilities of VQA systems are limited due to complexity of graph neural networks and end-to-end training.
Approach:	They propose a method to integrate Dense Passage Retrievers with Vision Language Models to boost the reasoning capabilities of VQA systems.
Outcome:	The proposed method outperforms human accuracy and GPT-4 in the ScienceQA dataset.

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Zhaoqi Zhang, Pasquale Balsebre, Siqiang Luo, Zhen Hai, Jiangping Huang

Topics as Entity Clusters: Entity-based Topics from Large Language Models and Graph Neural Networks (2024.lrec-main)

Challenge:	Existing approaches to address matching rely on string-based similarity matching or manually-designed rules.
Approach:	They propose a method to match unstructured addresses to standard ones in a database using pre-trained language models and graph neural networks.
Outcome:	The proposed method outperforms state-of-the-art methods on real-world addresses . it incorporates spatial coordinates and contextual information from the surrounding area as auxiliary guidance.

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Manuel V. Loureiro, Steven Derby, Tri Kurniawan Wijaya

MAKI: Multi-layer Aligned Knowledge Injection for Structure-aware Knowledge Graph Completion with Large Language Models (2026.findings-acl)

Challenge:	Topic models aim to reveal latent structures within corpus of text through term-frequency statistics over bag-of-words representations.
Approach:	They propose to use bimodal vector representations of entities to extract latent representations from large language models and graph neural networks trained on symbolic relations to derive the most salient aspects of these conceptual units.
Outcome:	The proposed approach is better suited to working with entities than state-of-the-art models.

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Zhiwen Xie, Xin Wang, Guangyou Zhou, Derek F. Wong

Towards Few-shot Entity Recognition in Document Images: A Graph Neural Network Approach Robust to Image Manipulation (2024.lrec-main)

Challenge:	Existing knowledge graph completion methods struggle to capture structural information in knowledge graphs (KGs) Existing approaches for KGC focus on learning representations of entities and relations through observed structural patterns.
Approach:	They propose a multi-layer Aligned Knowledge Injection model that tightly integrates structured KG information into LLMs through multi-layered alignment.
Outcome:	The proposed method outperforms state-of-the-art methods on benchmark datasets.

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Prashant Krishnan, Zilong Wang, Yangkun Wang, Jingbo Shang

TRUST: Towards Robust Social Bot Detection via Uncertainty-Guided Pseudo-Labeling and Graph Structure Purification (2026.findings-acl)

Challenge:	Existing methods for named entity recognition from document images are limited in few-shot settings.
Approach:	They propose a framework which leverages the topological adjacency relationship among tokens by learning layout information with graph neural networks.
Outcome:	The proposed framework outperforms baselines under different few-shot settings and shows better performance to image manipulations.

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Ruixuan Xu, Mengting Hu, Zhunheng Wang, Ming Jiang, Rui Ying, Zhen Zhang, Hang Gao, Shuaipeng Liu, Renhong Cheng

Responsible & Trustworthy NLP

Challenge:	Existing graph-based detection models are vulnerable to deceptive message propagation, where bots deliberately interact with legitimate users.
Approach:	They propose a framework to mitigate deceptive message propagation by node-level uncertainty estimation and graph structure purification.
Outcome:	The proposed framework improves on three benchmark datasets and six GNN backbones on real-world social bots.

Graph-Based Multi-Trait Essay Scoring (2025.emnlp-main)

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Shengjie Li, Vincent Ng