Papers with Structured Data in NLP
Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13) (D19-53)
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| Challenge: | TextGraphs is a workshop on graph-based methods for natural language processing . the workshop is being organized in conjunction with the 9th International Joint Conference on Natural Language Processing . |
| Approach: | TextGraphs is the 13th edition of the Workshop on Graph-Based Methods for Natural Language Processing . the workshop promotes synergy between GT and natural language processing . |
| Outcome: | the 2013 edition of TextGraphs is being held in conjunction with the 9th International Joint Conference on Natural Language Processing in Hong Kong. |
Generative Knowledge Graph Construction: A Review (2022.emnlp-main)
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| Challenge: | Knowledge Graphs (KGs) are a form of structured knowledge that rely almost exclusively on human-curated structured or semi-structured data. |
| Approach: | They propose to use the sequence-to-sequence framework to build knowledge graphs. |
| Outcome: | The proposed methods have been compared with existing methods and are promising for the future. |
Human-in-the-loop Schema Induction (2023.acl-demo)
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Tianyi Zhang, Isaac Tham, Zhaoyi Hou, Jiaxuan Ren, Leon Zhou, Hainiu Xu, Li Zhang, Lara Martin, Rotem Dror, Sha Li, Heng Ji, Martha Palmer, Susan Windisch Brown, Reece Suchocki, Chris Callison-Burch
| Challenge: | Existing approaches to event-centric natural language understanding (NLU) have been limited to linear and temporal ones. |
| Approach: | They propose a human-in-the-loop schema induction system powered by GPT-3 . they show that it transfers to new domains more easily than previous approaches . |
| Outcome: | The proposed system transfers to new domains more easily than previous approaches and reduces human curation. |
Relation Prediction for Unseen-Entities Using Entity-Word Graphs (D19-53)
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Yuki Tagawa, Motoki Taniguchi, Yasuhide Miura, Tomoki Taniguchi, Tomoko Ohkuma, Takayuki Yamamoto, Keiichi Nemoto
| Challenge: | Knowledge graphs (KGs) are incomplete and miss some information. |
| Approach: | They propose to learn entity representations via a graph structure that uses Seen-entities, Unseen-Entities and words as nodes created from the descriptions of all entities. |
| Outcome: | The proposed method improves relation prediction for the entity pairs containing Unseen-entities. |
DART: A Lightweight Quality-Suggestive Data-to-Text Annotation Tool (2020.coling-demos)
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| Challenge: | Neural data-to-text generation systems require large-scale labeled data to generate sentences. |
| Approach: | They propose to create an interactive annotation tool that iteratively analyzes annotated structured data to better sample unlabeled data. |
| Outcome: | The proposed tool reduces the number of annotations needed with active learning and automatically suggests relevant labels. |
Cross-Task Instance Representation Interactions and Label Dependencies for Joint Information Extraction with Graph Convolutional Networks (2021.naacl-main)
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| Challenge: | Existing work on information extraction (IE) has solved the four main tasks separately, thus failing to benefit from inter-dependencies between tasks. |
| Approach: | They propose a model to solve four IE tasks in a single model that captures inter-dependencies between tasks. |
| Outcome: | The proposed model achieves state-of-the-art performance on monolingual and multilingual learning settings with three different languages. |
A Graph Representation of Semi-structured Data for Web Question Answering (2020.coling-main)
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| Challenge: | Existing studies treat semi-structured data as flat documents with pieces of text . semi-structural data is more effective to represent rich relational information . question answering is an important feature in most search engines . |
| Approach: | They propose a graph representation of Web tables and lists based on categorization of components and their relations . they also develop reasoning techniques on the graph model for the question answering task . |
| Outcome: | The proposed graph improves F1 score by 3.90 points over the state-of-the-art baselines on real datasets. |
Acceptability Judgements via Examining the Topology of Attention Maps (2022.findings-emnlp)
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Daniil Cherniavskii, Eduard Tulchinskii, Vladislav Mikhailov, Irina Proskurina, Laida Kushnareva, Ekaterina Artemova, Serguei Barannikov, Irina Piontkovskaya, Dmitri Piontkovski, Evgeny Burnaev
| Challenge: | Acceptability judgments are a key component of generative linguistics, but their ability to judge grammatical acceptability has not been explored. |
| Approach: | They propose to exploit the geometric properties of the attention graph to evaluate the grammatical acceptability of sentences using topological data analysis. |
| Outcome: | The proposed approach outperforms nine statistical and Transformer LM baselines on the BLiMP benchmark and the human-level performance on the same benchmark. |
Scalable Knowledge Graph Construction from Text Collections (D19-66)
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| Challenge: | Existing open-source solutions for analyzing unstructured text are lacking in the field of knowledge graph construction. |
| Approach: | They propose a scalable open-source platform that "distills" a text collection into a knowledge graph . they scale out the Stanford CoreNLP toolkit via Apache Spark integration . |
| Outcome: | The proposed platform scales out the Stanford CoreNLP toolkit via Apache Spark integration . it extracts mentions and relations from documents and then ingests them into a knowledge graph . |
T2-RAGBench: Text-and-Table Benchmark for Evaluating Retrieval-Augmented Generation (2026.eacl-long)
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| Challenge: | Existing QA datasets containing text-and-table data typically contain context-dependent questions, which may yield multiple correct answers depending on the provided context. |
| Approach: | They propose a benchmark to evaluate RAG methods on text-and-table data. |
| Outcome: | The proposed method evaluates RAG methods on real-world text-and-table data. |
HiDE: a Tool for Unrestricted Literature Based Discovery (C18-2)
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| Challenge: | Literature based discovery (LBD) is an automatic technique that infers as yet unobserved connections from literature. |
| Approach: | They propose a literature based discovery tool which allows fast access to hidden connections generated from all abstracts in Medline. |
| Outcome: | The tool allows users to explore the full range of hidden connections generated by an LBD system. |
COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation (2021.naacl-demos)
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Qingyun Wang, Manling Li, Xuan Wang, Nikolaus Parulian, Guangxing Han, Jiawei Ma, Jingxuan Tu, Ying Lin, Ranran Haoran Zhang, Weili Liu, Aabhas Chauhan, Yingjun Guan, Bangzheng Li, Ruisong Li, Xiangchen Song, Yi Fung, Heng Ji, Jiawei Han, Shih-Fu Chang, James Pustejovsky, Jasmine Rah, David Liem, Ahmed ELsayed, Martha Palmer, Clare Voss, Cynthia Schneider, Boyan Onyshkevych
| Challenge: | a new framework to digest relevant biomedical knowledge is needed to combat COVID-19 . quantity of research results is a bottleneck, and false information promoted in publications . |
| Approach: | a team of researchers has developed a framework to extract multimedia knowledge elements from scientific literature to combat COVID-19. |
| Outcome: | a new framework extracts fine-grained multimedia knowledge elements from scientific literature . it provides detailed contextual sentences, subfigures, and knowledge subgraphs as evidence . the framework is based on a case study of drug repurposing . |
Logical Inference for Counting on Semi-structured Tables (2022.acl-srw)
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| Challenge: | Natural Language Inference (NLI) tasks require numerical understanding to perform a numerical type of inference, such as counting. |
| Approach: | They propose a logical inference system for reasoning between semi-structured tables and texts that uses logical representations as meaning representations and model checking to handle a numerical type of inference. |
| Outcome: | The proposed system can perform inference with numerical comparatives with tables and texts in English. |
InsertGNN: A Hierarchical Graph Neural Network for the TOEFL Sentence Insertion Problem (2024.findings-emnlp)
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| Challenge: | Existing methods that focus on sentence arrangement, textual consistency, and question answering have been shown to be inadequate in addressing this issue. |
| Approach: | They propose a method which conceptualizes the problem as a graph and employs a hierarchical Graph Neural Network (GNN) to comprehend the interplay between sentences. |
| Outcome: | The proposed approach outperforms existing methods on a TOEFL dataset and on the arXiv dataset. |
EASE: Entity-Aware Sub-table Generation for Real-world Multi-table QA (2026.acl-long)
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Myunghoon Kang, Dahyun Jung, Suhyune Son, Seonmin Koo, Changwoo Chun, Daniel Rim, Haeyoung Kwon, Yuna Hur, Heuiseok Lim
| Challenge: | Table-based question answering (table QA) is a powerful tool for analyzing large language models. |
| Approach: | They propose to use noisy multi-table sets to generate sub-tables for table-based question answering. |
| Outcome: | The proposed framework efficiently filters out irrelevant information while incorporating pertinent table values. |
Construction of the Literature Graph in Semantic Scholar (N18-3)
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Waleed Ammar, Dirk Groeneveld, Chandra Bhagavatula, Iz Beltagy, Miles Crawford, Doug Downey, Jason Dunkelberger, Ahmed Elgohary, Sergey Feldman, Vu Ha, Rodney Kinney, Sebastian Kohlmeier, Kyle Lo, Tyler Murray, Hsu-Han Ooi, Matthew Peters, Joanna Power, Sam Skjonsberg, Lucy Lu Wang, Chris Wilhelm, Zheng Yuan, Madeleine van Zuylen, Oren Etzioni
| Challenge: | Fig. 1 summarizes a scalable system for organizing published scientific literature into a heterogeneous graph . authors describe methods used to enable semantic features in www.semanticscholar.org . |
| Approach: | They describe a scalable system for organizing published scientific literature into a heterogeneous graph to facilitate algorithmic manipulation and discovery. |
| Outcome: | The proposed system can be deployed on a scalable platform and report empirical results for each task. |
Multi-Hop Open-Domain Question Answering over Structured and Unstructured Knowledge (2022.findings-naacl)
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| Challenge: | Existing open-domain question answering systems only select one source to generate answer or conduct reasoning on structured information. |
| Approach: | They propose a Document-Entity Heterogeneous Graph Network to integrate different sources of information and conduct reasoning on heterogeneous information. |
| Outcome: | The proposed model outperforms the state-of-the-art methods on a HybirdQA dataset. |
COCO-EX: A Tool for Linking Concepts from Texts to ConceptNet (2021.eacl-demos)
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| Challenge: | ConceptNet is a semantic network which contains general commonsense facts about the world, e.g., Birds can fly or Computers are used for sending e-mails. |
| Approach: | They propose a tool for Extracting Concepts from texts and linking them to ConceptNet, using the maximum relational information stored in ConceptNet. |
| Outcome: | The proposed method extracts meaningful concepts from natural language texts and links them to conjunct concept nodes in ConceptNet, utilizing the maximum of relational information stored in the KnowledgeGraph. |
SPINDLE: Spinning Raw Text into Lambda Terms with Graph Attention (2023.eacl-demo)
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| Challenge: | SPINDLE is an open source Python module that parses text input into programs for meaning composition expressed as terms. |
| Approach: | They describe SPINDLE, an open source Python module that parses text input into programs for meaning composition expressed as terms. |
| Outcome: | The proposed parser captures two orthogonal axes of syntax and provides proof search and visualization tools. |
Newspaper Signaling for Crisis Prediction (2024.naacl-demo)
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| Challenge: | Existing systems for detecting crisis-related signals are limited due to unstructured data, media, and cultural bias, and multiple languages. |
| Approach: | They propose a model for multi-lingual and open-domain newspaper signaling for detecting crisis-related indicators in newspaper articles. |
| Outcome: | The proposed model can detect crisis-related indicators in multiple languages and can be used in open crisis domains in real-time. |
TaKG: A New Dataset for Paragraph-level Table-to-Text Generation Enhanced with Knowledge Graphs (2022.findings-aacl)
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| Challenge: | Existing table-to-text generation benchmarks have some limitations, such as E2E and ToTTo focusing on singlesentence generation tasks. |
| Approach: | They propose a new table-to-text generation dataset called TaKG that uses a set of knowledge graphs to enhance table input. |
| Outcome: | The proposed model outperforms existing models for short-text generation tasks and shows reliable performance on long-text generated across a variety of metrics. |
Faceted Hierarchy: A New Graph Type to Organize Scientific Concepts and a Construction Method (D19-53)
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| Challenge: | faceted concept hierarchy is a structure of parent-child relationships . concepts are expected to be organized in a hierarchical structure for student learning . |
| Approach: | They propose a faceted concept hierarchy that aims to build facets from scientific literature. |
| Outcome: | The proposed hierarchy is more complete than "type-of" relations, and resolves conflicts by maintaining the acyclic structure of a hierarchy. |
Deep Cognitive Reasoning Network for Multi-hop Question Answering over Knowledge Graphs (2021.findings-acl)
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| Challenge: | Knowledge Graphs (KGs) store structured human knowledge with nodes and edges being entities and relations between them. |
| Approach: | They propose a deep cognitive reasoning network that uses two phases to find answers in large candidate entity sets. |
| Outcome: | The proposed method significantly outperforms state-of-the-art methods on benchmark datasets. |
Temporal Fact Reasoning over Hyper-Relational Knowledge Graphs (2024.findings-emnlp)
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| Challenge: | Existing models of temporal fact reasoning do not explicitly specify temporal information for each fact. |
| Approach: | They propose a new type of data structure called hyper-relational TKG to study temporal fact reasoning over HKGs. |
| Outcome: | The proposed model is based on two new benchmark HTKG datasets . it provides additional key-value pairs (i.e., qualifiers) for each KG fact . |
Mixture of Length and Pruning Experts for Knowledge Graphs Reasoning (2025.emnlp-main)
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| 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 . |
Nakdan: Professional Hebrew Diacritizer (2020.acl-demos)
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| Challenge: | a system for automatic diacritization of Hebrew Text is available for both casual and expert users. |
| Approach: | They propose a system for automatic diacritization of Hebrew Text . the system combines declarative linguistic knowledge with machine learning models . |
| Outcome: | The proposed system is available for both casual and expert users. |
Modeling Sense Structure in Word Usage Graphs with the Weighted Stochastic Block Model (2021.starsem-1)
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| Challenge: | Word Usage Graphs capture fine-grained semantic proximity distinctions between word uses. |
| Approach: | They propose to model word use Graphs using a Bayesian weighted stochastic block model and a probabilistic weightes-based model to capture fine-grained semantic proximity distinctions between word uses. |
| Outcome: | The proposed model is compared with existing models and is empirically most adequate. |
An Experimental Analysis on Evaluating Patent Citations (2024.emnlp-main)
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| Challenge: | Graph Neural Networks (GNNs)-based methods can predict patent citations using only patent text. |
| Approach: | They propose to use Graph Neural Networks to predict citations for patents based on their semantic similarities to generate a semantic graph of patents. |
| Outcome: | The proposed methods produce 94% recall for patents with high citations and outperform baselines. |
GrapAL: Connecting the Dots in Scientific Literature (P19-3)
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| Challenge: | Several software tools are available to help researchers perform tasks such as searching for papers, assessing applicants for a research position and keeping track of papers published on topics of interest. |
| Approach: | They introduce a graph database of academic literature with an intuitive schema and query language . they open source the demo code to help other researchers develop applications that build on it . |
| Outcome: | The proposed tool can be used to find experts on a given topic for peer review, find indirect connections between biomedical entities, and compute citation-based metrics. |
Leveraging LLM-GNN Integration for Open-World Question Answering over Knowledge Graphs (2026.eacl-long)
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| Challenge: | Traditional KGQA assumes a closed world where answers must exist in the KG, limiting real-world applicability. |
| Approach: | They propose a system that combines a pre-trained GNN and an LLM for open-world QA. |
| Outcome: | The proposed system outperforms existing LLM–GNN systems on standard benchmarks and GLOW-BENCH, achieving up to 53.3% and an average 38% improvement. |
Graph-to-Sequence Learning using Gated Graph Neural Networks (P18-1)
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| Challenge: | Existing approaches to graph-to-sequence learning ignore the full graph structure, discarding key information. |
| Approach: | They propose a graph-to-sequence learning model that encodes the full graph structure and an input transformation that allows nodes and edges to have their own hidden representations. |
| Outcome: | The proposed model outperforms baselines in generation from AMR graphs and syntax-based neural machine translation while retaining the full graph structure. |
Understanding tables with intermediate pre-training (2020.findings-emnlp)
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| Challenge: | Textual entailment is well studied, but is less well studied for table enlargement . a new dataset of millions of examples is used to train the model . |
| Approach: | They adapt a table-based BERT model to recognize entailment from a dataset . they evaluate table pruning techniques as a pre-processing step to improve model efficiency . |
| Outcome: | The proposed model improves training and prediction efficiency at a moderate drop in accuracy. |
Learning beyond Datasets: Knowledge Graph Augmented Neural Networks for Natural Language Processing (N18-1)
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| Challenge: | Currently, machine learning is limited in scalability and is limited to specific training data. |
| Approach: | They propose to enhance learning models with world knowledge in the form of Knowledge Graph fact triples for natural language processing tasks. |
| Outcome: | The proposed method is highly scalable to the amount of prior information that has to be processed and can be applied to any generic NLP task. |
PipeNet: Question Answering with Semantic Pruning over Knowledge Graphs (2024.starsem-1)
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| Challenge: | Existing approaches to utilizing explicit knowledge graphs (KGs) are limited by the number of nodes in the subgraph. |
| Approach: | They propose a grounding-pruning-reasoning pipeline to prune noisy nodes in subgraphs to improve the efficiency of graph reasoning with KG. |
| Outcome: | The proposed method reduces computation cost and memory usage while obtaining decent representation of pruned subgraphs. |
Heterogeneous Graph Neural Networks to Predict What Happen Next (2020.coling-main)
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| Challenge: | Existing work on event representation cannot capture discontinuous event segments . Existing models cannot represent heterogeneous relations and discontinuous events . |
| Approach: | They propose a heterogeneous-event graph network to model missing events . they employ each unique word and individual event as nodes in the graph . |
| Outcome: | The proposed model outperforms baseline models on one-step and multi-step inference tasks. |
Complex Evolutional Pattern Learning for Temporal Knowledge Graph Reasoning (2022.acl-short)
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Zixuan Li, Saiping Guan, Xiaolong Jin, Weihua Peng, Yajuan Lyu, Yong Zhu, Long Bai, Wei Li, Jiafeng Guo, Xueqi Cheng
| Challenge: | Existing models for TKG reasoning focus on modeling fact sequences of a fixed length, which cannot discover complex evolutional patterns that vary in length. |
| Approach: | They propose to use a length-aware Convolutional Neural Network to handle evolutional patterns of different lengths via an easy-to-difficult curriculum learning strategy. |
| Outcome: | The proposed model improves performance under both offline and online learning strategies. |
Guiding Neural Entity Alignment with Compatibility (2022.emnlp-main)
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| Challenge: | Entity Alignment (EA) aims to find equivalent entities between two Knowledge Graphs (KGs) labelled data is used to learn neural EA models, but this aspect is neglected . |
| Approach: | They propose a framework to integrate compatibility into neural EA models . they aim to find equivalent entities between two Knowledge Graphs (KGs) |
| Outcome: | The proposed framework can achieve comparable effectiveness with supervised training using 20% of labelled data. |
BAG: Bi-directional Attention Entity Graph Convolutional Network for Multi-hop Reasoning Question Answering (N19-1)
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| Challenge: | Existing datasets for question answering and machine comprehension (MC) are limited to a single paragraph, or even part of it. |
| Approach: | They propose a bi-directional Attention Entity Graph Convolutional Network (BAG) that leverages relationships between nodes in an entity graph and attention information between a query and the entity graph to generate a prediction. |
| Outcome: | Experimental results show that the proposed network achieves state-of-the-art accuracy on the QAngaroo WIKIHOP dataset. |
AIT-QA: Question Answering Dataset over Complex Tables in the Airline Industry (2022.naacl-industry)
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Yannis Katsis, Saneem Chemmengath, Vishwajeet Kumar, Samarth Bharadwaj, Mustafa Canim, Michael Glass, Alfio Gliozzo, Feifei Pan, Jaydeep Sen, Karthik Sankaranarayanan, Soumen Chakrabarti
| Challenge: | Table Question Answering (Table QA) systems have been shown to be highly accurate when trained and tested on open-domain datasets built on top of Wikipedia tables. |
| Approach: | They propose a domain-specific Table QA test dataset to test Table Question Answering systems on open-domain datasets built on top of Wikipedia tables. |
| Outcome: | The proposed methods are highly accurate when tested on open-domain datasets built on top of Wikipedia tables. |
MASFactory: A Graph-centric Framework for Orchestrating LLM-Based Multi-Agent Systems with Vibe Graphing (2026.acl-demo)
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| Challenge: | Large language model-based multi-agent systems (MAS) are increasingly used to extend agentic problem solving via role specialization and collaboration. |
| Approach: | They propose a graph-centric framework for orchestrating large language model-based multi-agent systems . they compile a user's natural-language intent into an editable workflow specification and then into an executable graph . |
| Outcome: | The proposed framework compiles natural-language intent into an executable graph and then compile and executes it at runtime. |
Graph Based Network with Contextualized Representations of Turns in Dialogue (2021.emnlp-main)
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| Challenge: | Dialogue-based relation extraction (RE) aims to extract relation(s) between two arguments that appear in a dialogue. |
| Approach: | They propose a dialogue-based relation extraction model which is based on emotion recognition in conversations. |
| Outcome: | The proposed model outperforms the state-of-the-art models on most of the benchmark datasets. |
DART: Open-Domain Structured Data Record to Text Generation (2021.naacl-main)
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Linyong Nan, Dragomir Radev, Rui Zhang, Amrit Rau, Abhinand Sivaprasad, Chiachun Hsieh, Xiangru Tang, Aadit Vyas, Neha Verma, Pranav Krishna, Yangxiaokang Liu, Nadia Irwanto, Jessica Pan, Faiaz Rahman, Ahmad Zaidi, Mutethia Mutuma, Yasin Tarabar, Ankit Gupta, Tao Yu, Yi Chern Tan, Xi Victoria Lin, Caiming Xiong, Richard Socher, Nazneen Fatema Rajani
| Challenge: | Data-to-text annotations can be costly when dealing with tables with nontrivial structures. |
| Approach: | They propose a procedure for extracting semantic triples from tables that encodes their structures by exploiting table headers and table title. |
| Outcome: | The proposed method exploits the semantic dependencies between table headers and title to extract semantic triples from tables. |
Modeling Global and Local Node Contexts for Text Generation from Knowledge Graphs (2020.tacl-1)
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| Challenge: | Recent graph-to-text models generate text from graph data using global or local aggregation . global node encoding allows explicit communication between two distant nodes, but fails to capture long-range relationships. |
| Approach: | They propose to combine global and local aggregation to learn node representations . they propose to use global and locally encoding to learn contextualized node embeddings based on graph data . |
| Outcome: | The proposed models outperform state-of-the-art models on two graph-to-text datasets by 18.01 and 63.69 points. |
Towards Faithful Industrial RAG: A Reinforced Co-adaptation Framework for Advertising QA (2026.acl-industry)
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Wenwei Li, Ming Xu, Tianle Xia, Lingxiang Hu, Yiding Sun, Linfang Shang, Liqun Liu, Peng Shu, Huan Yu, Jie Jiang
| Challenge: | Existing methods for QA in industrial environments are inherently relational and often updated. |
| Approach: | They propose a framework that optimizes retrieval and generation through two components: Graph-aware Retrieval and evidence-constrained reinforcement learning. |
| Outcome: | Experiments on an internal advertising QA dataset show consistent gains across expert-judged dimensions including accuracy, completeness, safety, and URL validity. |
DNN-driven Gradual Machine Learning for Aspect-term Sentiment Analysis (2021.findings-acl)
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| Challenge: | Existing methods for Aspect-Term Sentiment Analysis (ATSA) use pre-specified lexicons to extract sentiment features. |
| Approach: | They propose a Deep Neural Network-driven approach for Aspect-Term Sentiment Analysis (ATSA) that leverages shared features between labeled and unlabeled instances for knowledge conveyance. |
| Outcome: | The proposed approach consistently achieves state-of-the-art performance on real benchmark data. |
A Table-to-Text Framework with Heterogeneous Multidominance Attention and Self-Evaluated Multi-Pass Deliberation (2023.findings-emnlp)
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| Challenge: | Table-to-text works have been widely applied in different domains, such as weather forecast and financial report generation. |
| Approach: | They propose a table-to-text approach on top of Self-evaluated multi-pass Generation and Heterogenous Multidominance Attention to explore the hierarchical structure. |
| Outcome: | The proposed method outperforms several SOTA methods quantitatively and qualitatively on three public datasets. |
Annotating Research Infrastructure in Scientific Papers: An NLP-driven Approach (2023.acl-industry)
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Seyed Amin Tabatabaei, Georgios Cheirmpos, Marius Doornenbal, Alberto Zigoni, Veronique Moore, Georgios Tsatsaronis
| Challenge: | a pipeline is used to identify, extract and link research infrastructure used in scientific publications. |
| Approach: | They propose a natural language processing pipeline for the identification, extraction and linking of Research Infrastructure (RI) used in scientific publications. |
| Outcome: | The proposed pipeline can be used to identify, extract and link research infrastructure used in scientific publications. |
Keyphrase Generation with Fine-Grained Evaluation-Guided Reinforcement Learning (2021.findings-emnlp)
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| Challenge: | Existing KG evaluation metrics are only aware of the exact correctness of predictions on phrase-level and ignore semantic similarities between similar predictions and targets, which inhibits the model from learning deep linguistic patterns. |
| Approach: | They propose a fine-grained evaluation metric to improve the previous KG framework . the evaluation metrics are only aware of the exact correctness of predictions on phrase-level . |
| Outcome: | The proposed method outperforms the existing frameworks among all evaluation scores. |
A Decade of Knowledge Graphs in Natural Language Processing: A Survey (2022.aacl-main)
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| Challenge: | Knowledge graphs (KGs) are a representation of semantic relations between entities . despite their popularity, there is still no general understanding of what exactly a KG is or for what tasks it is applicable. |
| Approach: | They analyze 507 papers on knowledge graphs in natural language processing (NLP) they provide a taxonomy of tasks and review the maturity of individual research streams . |
| Outcome: | The findings summarize the literature and highlight directions for future work. |
Fact Checking Machine Generated Text with Dependency Trees (2022.emnlp-industry)
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| Challenge: | Recent work has noted the benefits of natural language text generated by NLG systems over fixed templates. |
| Approach: | They propose a method that checks factuality of input text based on structured knowledge patterns and dependency relations with respect to the input text. |
| Outcome: | The proposed technique outperforms state-of-the-art techniques in this special, but important case. |
Effect Generation Based on Causal Reasoning (2021.findings-emnlp)
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| Challenge: | Existing methods for reasoning causalities on word level are limited . a word-level causal reasoning method may only predict the unintelligible effect of "quarrel" |
| Approach: | They propose a novel event-level causal reasoning method that structuralizes event-effect event pairs into an event causality network and shows its use in the task of effect generation. |
| Outcome: | The proposed method generates more reasonable effect sentences than well-designed competitors. |
Mitigating Hallucination by Integrating Knowledge Graphs into LLM Inference – a Systematic Literature Review (2025.acl-srw)
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| Challenge: | Large Language Models (LLMs) have made significant progress on different language tasks, but they tend to "hallucinate" plausible but factually incorrect answers. |
| Approach: | They propose to integrate knowledge graphs (KGs) into LLM inference to reduce hallucinations by searching online and applying a selection process. |
| Outcome: | The proposed integration improves performance on benchmark datasets and also to mitigate hallucinations. |
SocAoG: Incremental Graph Parsing for Social Relation Inference in Dialogues (2021.acl-long)
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| Challenge: | Existing studies focus on identifying entities' relations from the semantics of dialogues-they utilize either the attention mechanism or a refined token graph to locate informative words. |
| Approach: | They propose a sequential structure prediction task to incrementally parse SocAoG for dynamic inference upon any incoming utterance. |
| Outcome: | Empirical results show that the proposed model infers social relations more accurately than the state-of-the-art methods. |
Align-then-Enhance: Multilingual Entailment Graph Enhancement with Soft Predicate Alignment (2023.findings-acl)
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| Challenge: | Existing approaches to learn typed entailment graphs with predicates as nodes and enttailment relations as edges are incomplete. |
| Approach: | They propose a task to utilize entailment information from one EG to enhance another in a different language. |
| Outcome: | The proposed framework outperforms existing graphs in multilingual entailment graph enhancement tasks. |
CareerPathKG: Knowledge Graph Integrated Framework for Career Intelligence (2026.eacl-industry)
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| Challenge: | a new framework for career orientation is needed to address the challenges of the labor market . a recent study found that traditional ML and large language models are brittle when faced with heterogeneous job descriptions . |
| Approach: | They propose a career-path knowledge graph-based recruitment framework to capture occupations, skill requirements and career transitions using standardized taxonomies enriched with job-posting data. |
| Outcome: | The proposed framework captures occupations, skill requirements, and career transitions using standardized taxonomies enriched with job-posting data. |
Coordinate Constructions in English Enhanced Universal Dependencies: Analysis and Computational Modeling (2021.eacl-main)
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| Challenge: | Enhanced Universal Dependencies (UD) parses are used to represent syntactic dependencies between words, prioritizing links between content words. |
| Approach: | They propose to use a heuristic rule-based converter to propagate conjuncts from conjunction heads to other conjunctions to determine which set of links to propagation from a semantic perspective. |
| Outcome: | The proposed method outperforms existing methods using basic-layer tree parsers and converters. |
Question-Answer Sentence Graph for Joint Modeling Answer Selection (2023.eacl-main)
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| Challenge: | Existing approaches to automate Question Answering (QA) are graph-based and can target large text databases. |
| Approach: | They propose graph-based approaches for Answer Sentence Selection (AS2) . they train and integrate state-of-the-art (SOTA) models for computing scores . |
| Outcome: | The proposed approach outperforms baseline models on academic benchmarks and a real-world dataset on unseen queries. |
RecStream: Graph-aware Stream Management for Concurrent Recommendation Model Online Serving (2025.coling-industry)
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| Challenge: | Existing systems that use recommendation models perform poorly under highly concurrent scenarios. |
| Approach: | They propose a system that optimizes stream configurations based on model characteristics and concurrency levels. |
| Outcome: | The proposed system outperforms existing methods under high concurrency scenarios. |
Acquisition of Phrase Correspondences Using Natural Deduction Proofs (N18-1)
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| Challenge: | Existing methods for Recognizing Textual Entailment (RTE) lack phrasal knowledge. |
| Approach: | They propose a method for detecting paraphrases via natural deduction proofs of semantic relations between sentence pairs. |
| Outcome: | The proposed method detects paraphrases that are absent from existing paraphrase databases and improves accuracy of RTE tasks. |
DocTime: A Document-level Temporal Dependency Graph Parser (2022.naacl-main)
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Puneet Mathur, Vlad Morariu, Verena Kaynig-Fittkau, Jiuxiang Gu, Franck Dernoncourt, Quan Tran, Ani Nenkova, Dinesh Manocha, Rajiv Jain
| Challenge: | Document dependency graphs (TDGs) are used to understand the temporal relations between events mentioned in a document and to improve downstream tasks such as timeline creation and time-aware summarization. |
| Approach: | They propose a temporal dependency graph parser that takes input from a text document and produces a graph that incorporates longer range dependencies. |
| Outcome: | The proposed framework outperforms existing models on three datasets and improves tasks such as timeline creation, time-aware summarization, and temporal information extraction. |
Deja vu: Contrastive Historical Modeling with Prefix-tuning for Temporal Knowledge Graph Reasoning (2024.findings-naacl)
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| Challenge: | Existing text-based methods for Temporal Knowledge Graph Reasoning struggle to balance textual knowledge and temporal information with expensive purpose-built training strategies. |
| Approach: | They propose a Contrastive historical modeling framework with prefix-tuning for TEmporal Reasoning that feeds history-contextualized text into the pseudo-Siamese encoders to strike a textual-temporal balance. |
| Outcome: | The proposed framework achieves superior performance on four transductive and three few-shot inductive TKGR benchmarks. |
Generating Fine-Grained Open Vocabulary Entity Type Descriptions (P18-1)
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| Challenge: | Fig. 1 shows an example of a concise entity description presented to a user. |
| Approach: | They propose a dynamic memory-based network that generates a short open vocabulary description of an entity by leveraging induced fact embeddings and dynamic context. |
| Outcome: | The proposed network generates a short open vocabulary description of an entity . it can discern relevant information for more accurate generation of type description . |
Beyond Verbal Cues: Emotional Contagion Graph Network for Causal Emotion Entailment (2025.findings-acl)
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| Challenge: | Recent studies have focused on identifying the causes of emotions by understanding verbal contextual utterances, but this study often lacks recognizing the underlying emotional stimuli present in these utterrances. |
| Approach: | They propose an Emotional Contagion Graph Network that simulates the impact of non-verbal emotional cues on the counterpart’s emotions. |
| Outcome: | The proposed model is compared with state-of-the-art models on a benchmark dataset and the results are encouraging. |
Dangling-Aware Entity Alignment with Mixed High-Order Proximities (2022.findings-naacl)
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Juncheng Liu, Zequn Sun, Bryan Hooi, Yiwei Wang, Dayiheng Liu, Baosong Yang, Xiaokui Xiao, Muhao Chen
| Challenge: | Existing methods for dangling-aware entity alignment are underexplored but important problem. |
| Approach: | They propose a framework that uses high-order proximities to detect dangling entities and align matchable entities. |
| Outcome: | The proposed framework detects dangling entities and aligns matchable entities better than existing methods. |
ToTTo: A Controlled Table-To-Text Generation Dataset (2020.emnlp-main)
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Ankur Parikh, Xuezhi Wang, Sebastian Gehrmann, Manaal Faruqui, Bhuwan Dhingra, Diyi Yang, Dipanjan Das
| Challenge: | Existing methods for data-to-text generation often hallucinate phrases not supported by the Wikipedia table. |
| Approach: | They propose a controlled task where annotators directly revise existing Wikipedia sentences to generate a one-sentence description. |
| Outcome: | The proposed task produces a one-sentence description from a Wikipedia table and highlighted cells. |
GNEG: Graph-Based Negative Sampling for word2vec (P18-2)
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| Challenge: | Generally speaking, negative sampling is the best choice for distributed word representation learning. |
| Approach: | They hypothesize that taking into account global, corpus-level information and generating a different noise distribution for each target word better satisfies the requirements of negative examples for each training word. |
| Outcome: | The proposed approach boosts the word analogy task by about 5% and improves the performance on word similarity tasks by about 11% compared to the baseline. |
HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data (2020.findings-emnlp)
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| Challenge: | Existing question answering datasets focus on dealing with homogeneous information, but using homogenous information alone might lead to coverage problems. |
| Approach: | They propose a large-scale question-answering dataset that requires reasoning on heterogeneous information. |
| Outcome: | The proposed model can achieve an EM score of 40% while the existing model is far behind human performance. |
Extend, don’t rebuild: Phrasing conditional graph modification as autoregressive sequence labelling (2021.emnlp-main)
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| Challenge: | Generating or modifying graphs from natural language text has applications in many subfields, such as dependency parsing or knowledge graph construction. |
| Approach: | They propose a method that first embeds the graph and the instructions with a joint encoder and then rebuilds it using a separate generative model for graphs conditioned on h. |
| Outcome: | The proposed method improves accuracy on three scene graph modification data sets while the state-of-the-art fails to generalize. |
QABISAR: Query-Article Bipartite Interactions for Statutory Article Retrieval (2025.coling-main)
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| Challenge: | Existing methods for Statutory Article Retrieval (SAR) are vague and underspecified . however, a new approach is needed to bridge the gap between legal expertise and public understanding . |
| Approach: | They propose a framework for statutory article retrieval that leverages bipartite interactions between queries and articles to capture diverse aspects inherent in them. |
| Outcome: | The proposed framework overcomes the semantic mismatch problem when modeling each query-article pair in isolation. |
Exclusive Hierarchical Decoding for Deep Keyphrase Generation (2020.acl-main)
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| Challenge: | Existing approaches to generate keyphrases ignore hierarchical compositionality of keyphrase set and generate duplicated keyphrase sets. |
| Approach: | They propose a hierarchical decoding framework that explicitly models hierarchic compositionality of a keyphrase set and either a soft or a hard exclusion mechanism to enhance the diversity of the generated keyphrases. |
| Outcome: | The proposed framework generates less duplicated and more accurate keyphrases on a set of keyphrase sets. |
Towards Dynamic Computation Graphs via Sparse Latent Structure (D18-1)
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| Challenge: | Existing approaches to learn latent structure are limited by factorization assumptions or end-to-end differentiability. |
| Approach: | They propose a method that allows for end-to-end learning of latent structure predictors jointly with a downstream predictor. |
| Outcome: | The proposed method allows for unrestricted dynamic graph construction from the global latent structure while maintaining differentiability. |
Pathway2Text: Dataset and Method for Biomedical Pathway Description Generation (2022.findings-naacl)
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| Challenge: | Neural text generation is a novel technique to describe biomedical pathways without manually curation. |
| Approach: | They propose a new dataset Pathway2Text which contains 2,367 pairs of biomedical pathways and textual descriptions. |
| Outcome: | The proposed method improves on both Graph2Text and Text2Graph tasks and can be used as a benchmark for biomedical named entity recognition. |
REANO: Optimising Retrieval-Augmented Reader Models through Knowledge Graph Generation (2024.acl-long)
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| Challenge: | Existing knowledge graphs suffer from incompleteness and lack information critical for answering given questions. |
| Approach: | They propose to enhance the open domain question answering model with a knowledge graph generation module that generates KGs from the passages and an answer predictor. |
| Outcome: | The proposed model improves the exact match score by 2.7% on the EntityQuestion dataset, with an average improvement of 1.8% across all the datasets. |
Generalization in Text-based Games via Hierarchical Reinforcement Learning (2021.findings-emnlp)
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| Challenge: | Reinforcement Learning (RL) based agents are promising for text-based games, but their generalization remains a challenge. |
| Approach: | They propose a hierarchical framework for reinforcement learning based on knowledge graphs . they propose to decompose the game into subtasks and execute a sub-policy in the low level to conduct goal-conditioned reinforcement learning. |
| Outcome: | The proposed framework enjoys favorable generalizability on a set of difficulty levels and is able to handle complex training tasks. |
Dynamic Relevance Graph Network for Knowledge-Aware Question Answering (2022.coling-1)
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| Challenge: | Existing approaches to solve commonsense question answering problems often miss some edges between entities, which breaks the reasoning chain. |
| Approach: | They propose a graph neural network architecture that uses relevance as graph edges to establish new edges dynamically for learning node representations in the graph network. |
| Outcome: | The proposed approach shows competitive performance on two QA benchmarks, CommonsenseQA and OpenbookQA, compared to the state-of-the-art published results. |
TaTA: A Multilingual Table-to-Text Dataset for African Languages (2023.findings-emnlp)
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Sebastian Gehrmann, Sebastian Ruder, Vitaly Nikolaev, Jan Botha, Michael Chavinda, Ankur Parikh, Clara Rivera
| Challenge: | Existing data-to-text generation datasets are limited to English and a small number of other languages. |
| Approach: | They create the first large multilingual table-to-text dataset with a focus on African languages. |
| Outcome: | The proposed dataset includes 8,700 examples in nine languages including four African languages and a zero-shot test language. |
DALK: Dynamic Co-Augmentation of LLMs and KG to answer Alzheimer’s Disease Questions with Scientific Literature (2024.findings-emnlp)
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Dawei Li, Shu Yang, Zhen Tan, Jae Baik, Sukwon Yun, Joseph Lee, Aaron Chacko, Bojian Hou, Duy Duong-Tran, Ying Ding, Huan Liu, Li Shen, Tianlong Chen
| Challenge: | Recent advances in large language models have achieved promising performances across various applications, but the challenge of integrating long-tail knowledge continues to impede the seamless adoption of LLMs in specialized domains. |
| Approach: | They propose a dynamic co-augmentation framework for the refinement of large language models and knowledge graphs in the context of Alzheimer's Disease. |
| Outcome: | The proposed framework can be used to study Alzheimer's Disease (AD) using LLMs and KGs. |
Structural Deep Encoding for Table Question Answering (2025.findings-acl)
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| Challenge: | Tabular data is a common data format, but many models flatten the structure of a table into a sequence of tokens, resulting in computational costs and over-fitting issues. |
| Approach: | They propose to use special tokens to mark rows and columns, structured embeddings, and sparse attention patterns to preserve structural information of tabular data. |
| Outcome: | The proposed models enhance computational efficiency and preserve structural integrity, leading to better overall performance. |
Can LLM Graph Reasoning Generalize beyond Pattern Memorization? (2024.findings-emnlp)
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| Challenge: | Existing studies seek to enhance the graph reasoning capabilities of Large Language Models (LLMs) by specialized instruction tuning. |
| Approach: | They propose to evaluate LLM graph reasoning generalization using in-distribution settings . they propose to use three strategies to improve LLM generalization . |
| Outcome: | The proposed benchmark evaluates LLM graph reasoning generalization with in-distribution settings only . it shows that LLMs struggle to generalize across reasoning and real-world patterns . |
OpenGraph: Towards Open Graph Foundation Models (2024.findings-emnlp)
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| Challenge: | Graph Neural Networks (GNNs) have emerged as promising techniques for encoding structural information and improving performance in tasks like link prediction and node classification. |
| Approach: | They propose a graph foundation model that generalizes to unseen graph data with different properties. |
| Outcome: | The proposed model achieves remarkable zero-shot graph learning performance across various settings. |
Mind the Query: A Benchmark Dataset towards Text2Cypher Task (2025.emnlp-industry)
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| Challenge: | Graph databases store data in nodes and relationships, enabling more natural modeling of complex, interconnected data. |
| Approach: | They present a high-quality dataset for the Text2Cypher task . it is enabling the translation of natural language (NL) questions into executable Cypher queries over graph databases. |
| Outcome: | The proposed dataset includes 27,529 NL queries and corresponding Cyphers spanning across 11 real-world graph datasets. |
A Multi-source Graph Representation of the Movie Domain for Recommendation Dialogues Analysis (2022.lrec-1)
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| Challenge: | Graph databases are well-suited for crossreferencing information from multiple sources to support machine learning tasks. |
| Approach: | They propose a graph-based structure of multiple resources enriched with graph analytics approaches to provide an encompassing view of the movie recommendation domain and of the way people talk about it during the recommendation task. |
| Outcome: | The proposed graph-based structure provides an encompassing view of the domain and of the way people talk about it during the recommendation task. |
From Graph to Text and Back: Semantic Fidelity in Automated Industrial Knowledge Graphs (2026.acl-industry)
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| Challenge: | Large Language Models (LLMs) often hallucinate entities or omit relations, posing unacceptable liability. |
| Approach: | They propose a self-supervised round-trip pipeline to enforce strict semantic fidelity in KG-to-text generation. |
| Outcome: | The proposed approach improves triple-extraction accuracy and verbalization faithfulness without manual annotation or massive teacher models. |
Multi-Channel Graph Neural Network for Entity Alignment (P19-1)
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| Challenge: | Existing methods to learn alignment-oriented knowledge graph embeddings suffer from structural heterogeneity and limited seed alignments. |
| Approach: | They propose a multi-channel Graph Neural Network model to learn alignment-oriented knowledge graph embeddings by encoding two KGs via multiple channels. |
| Outcome: | The proposed model is expected to reconcile the structural differences of two KGs, and thus make better use of seed alignments. |
Learning from Sibling Mentions with Scalable Graph Inference in Fine-Grained Entity Typing (2022.acl-long)
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| Challenge: | Existing models struggle to handle hard mentions due to insufficient contexts, limiting their overall typing performance. |
| Approach: | They propose to exploit sibling mentions to enhance the mention representations by adding unseen test mentions as new nodes for inference. |
| Outcome: | The proposed model outperforms ten strong baseline models and outperformed strong baselines. |
Event Schema Induction with Double Graph Autoencoders (2022.naacl-main)
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| Challenge: | Experimental results show that a new method for learning event schemas from historical events is effective. |
| Approach: | They propose a new event schema induction framework which captures global dependencies among nodes in event graphs. |
| Outcome: | Experimental results show that the proposed model can learn event schemas with global consistency. |
Is Table Retrieval a Solved Problem? Exploring Join-Aware Multi-Table Retrieval (2024.acl-long)
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| Challenge: | Existing methods for retrieving relevant tables are not sufficient as many questions require retrieving multiple tables and joining them through a join plan that cannot be discerned from the user query itself. |
| Approach: | They propose a method that uncovers useful join relations during table retrieval. |
| Outcome: | The proposed method outperforms the state-of-the-art methods for table retrieval by up to 9.3% in F1 score and for end-to-end QA by up 5.4% in accuracy. |
CRAFT: Training-Free Cascaded Retrieval for Tabular QA (2026.acl-long)
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| Challenge: | Existing methods for open-domain table question answering require retraining or fine-tuning on new datasets. |
| Approach: | They propose a zero-shot, cascaded retrieval approach that uses a sparse retrieval model to filter a subset of candidates before applying more expensive dense models as re-rankers. |
| Outcome: | The proposed method outperforms state-of-the-art retrieval models on the NQ-Tables dataset. |
Dating Documents using Graph Convolution Networks (P18-1)
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| Challenge: | Existing approaches for document dating assume accurate knowledge of document date, but this is not always available for arbitrary documents from the Web. |
| Approach: | They propose a Graph Convolutional Network (GCN) based document dating approach which exploits syntactic and temporal graph structures of document in a principled way. |
| Outcome: | The proposed approach outperforms state-of-the-art models on real-world datasets by 19% absolute accuracy points. |
KG-MuLQA: A Framework for KG-based Multi-Level QA Extraction and Long-Context LLM Evaluation (2026.acl-long)
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Nikita Tatarinov, Vidhyakshaya Kannan, Haricharana Srinivasa, Arnav Raj, Harpreet Singh Anand, Varun Singh, Aditya Luthra, Ravij Lade, Agam Shah, Sudheer Chava
| Challenge: | KG-MulQA extracts QA pairs at multiple complexity levels along three key dimensions: multi-hop retrieval, set operations, and answer plurality. |
| Approach: | They propose a framework that extracts QA pairs at multiple complexity levels along three key dimensions: multi-hop retrieval, set operations, and answer plurality. |
| Outcome: | The framework extracts QA pairs at multiple complexity levels along key dimensions . it enables fine-grained assessment of model performance across controlled difficulty levels. |
Everything Has a Cause: Leveraging Causal Inference in Legal Text Analysis (2021.naacl-main)
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| Challenge: | Existing studies focus on analyzing structured data, while mining causal relationship among factors from unstructured data is of great importance. |
| Approach: | They propose a graph-based causal inference framework which builds causal graphs from fact descriptions without much human involvement. |
| Outcome: | The proposed framework can capture nuance from fact descriptions among confusing charges and provide explainable discrimination in few-shot settings. |
Exploiting Hybrid Semantics of Relation Paths for Multi-hop Question Answering over Knowledge Graphs (2022.coling-1)
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| Challenge: | Existing approaches to answer natural language questions on knowledge graphs (KGQA) use large-scale entity-related text corpus or knowledge graph embeddings as auxiliary information to facilitate answer selection. |
| Approach: | They propose to integrate explicit textual information and implicit KG structural features of relation paths into a novel rotate-and-scale entity link prediction framework. |
| Outcome: | The proposed method is superior to existing methods on three KGQA datasets and shows that it can be used to identify answer entities. |
Heterogeneous Graph Neural Networks for Concept Prerequisite Relation Learning in Educational Data (2021.naacl-main)
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| Challenge: | Existing methods to learn prerequisite relations among concepts are lacking . concepts are crucial for learning, organizing, applying and generating knowledge . |
| Approach: | They propose a concept prerequisite relation learning approach which combines concept representation and concept pairwise features to make it more practical. |
| Outcome: | The proposed method achieves state-of-the-art results on four datasets. |
Bilingual Lexicon Induction for Low-Resource Languages using Graph Matching via Optimal Transport (2022.emnlp-main)
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| Challenge: | Existing literature on bilingual lexicon induction fails in low-resource scenarios . a language dataset is considered low- resource based on its own embedding space . |
| Approach: | They propose a graph-matching method that improves bilingual lexicon induction performance across 40 language pairs using optimal transport. |
| Outcome: | The proposed method is especially strong with low amounts of supervision. |
Direct Evaluation of Chain-of-Thought in Multi-hop Reasoning with Knowledge Graphs (2024.findings-acl)
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| Challenge: | Prior research on evaluating large language models focused on answer accuracy, neglecting the correctness of the generated CoT. |
| Approach: | They propose a discriminative and generative CoT evaluation paradigm to assess LLMs’ knowledge of reasoning and the accuracy of the generated CoT. |
| Outcome: | The proposed evaluation paradigm assesses LLMs’ knowledge of reasoning and the accuracy of the generated CoT. |
Neural Path Hunter: Reducing Hallucination in Dialogue Systems via Path Grounding (2021.emnlp-main)
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| Challenge: | Dialogue systems that generate factually incorrect responses are often unfitful and hallucinate factuality invalid. |
| Approach: | They propose a method to improve faithfulness and reduce hallucination of neural dialogue systems to known facts supplied by a Knowledge Graph. |
| Outcome: | The proposed approach improves faithfulness and reduces hallucination of dialogue systems to known facts . it leverages a token-level fact critic to identify plausible sources of hallucinism . |
ReaRev: Adaptive Reasoning for Question Answering over Knowledge Graphs (2022.findings-emnlp)
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| 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. |
Process-Level Representation of Scientific Protocols with Interactive Annotation (2021.eacl-main)
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| Challenge: | Existing efforts to automate wet lab workflows are focusing on graph-prediction models that capture both concrete, exact quantities ("30 minutes") and vague instructions ("swirl") |
| Approach: | They manually annotate PEGs in a corpus of complex lab protocols with a novel interactive textual simulator that keeps track of entity traits and semantic constraints during annotation. |
| Outcome: | The proposed graph-prediction models are good at entity identification and local relation extraction while addressing challenges such as cross-sentence relations and long-range coreference. |
BanglaAutoKG: Automatic Bangla Knowledge Graph Construction with Semantic Neural Graph Filtering (2024.lrec-main)
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| Challenge: | Bangla is underrepresented in KGs due to lack of comprehensive datasets, encoders, NER models, part-of-speech taggers, and lemmatizers. |
| Approach: | Bangla is underrepresented in KGs due to lack of comprehensive datasets, encoders, NER models, part-of-speech taggers, and lemmatizers. authors propose a framework that can automatically construct Bengali KG from any Bangla text. |
| Outcome: | The proposed framework can automatically construct Bengali KGs from any Bangla text. |
GRNFormer: A Biologically-Guided Framework for Integrating Gene Regulatory Networks into RNA Foundation Models (2025.findings-acl)
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Mufan Qiu, Xinyu Hu, Fengwei Zhan, Sukwon Yun, Jie Peng, Ruichen Zhang, Bhavya Kailkhura, Jiekun Yang, Tianlong Chen
| Challenge: | Foundation models for single-cell RNA sequencing ignore biological prior knowledge encoded in gene regulatory relationships and fail to leverage multi-omics signals. |
| Approach: | They propose a framework that integrates multi-scale gene regulatory networks into RNA foundation model training. |
| Outcome: | The proposed framework improves on state-of-the-art models on three downstream tasks . it integrates multi-scale gene regulatory networks (GRNs) from multi-omics data into training . |
From the One, Judge of the Whole: Typed Entailment Graph Construction with Predicate Generation (2023.acl-long)
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| Challenge: | Existing methods to construct entailment graphs suffer from severe sparsity issues due to limited corpora and the long-tail phenomenon of predicate distributions. |
| Approach: | They propose a multi-stage method to generate entailment graphs by generating new predicates and detecting enanglement relations among seed predicats. |
| Outcome: | The proposed method can generate high-quality graphs with high precision over state-of-the-art methods and boost the performance of down-stream inference tasks. |
Taxonomy Construction of Unseen Domains via Graph-based Cross-Domain Knowledge Transfer (2020.acl-main)
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| Challenge: | Existing taxonomies are either entirely absent or missing. |
| Approach: | They propose a GNN-based cross-domain transfer framework for the taxonomy construction task. |
| Outcome: | The proposed framework improves on benchmark datasets from science and environment domains. |
Recurrent One-Hop Predictions for Reasoning over Knowledge Graphs (C18-1)
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| Challenge: | Large scale knowledge graphs (KGs) such as Freebase are generally incomplete. |
| Approach: | They propose a model that predicts entities at each step of mh-KB paths . the model is based on recurrent neural networks and vector representations of entities and relations . |
| Outcome: | The proposed models show state-of-the-art for two important multi-hop KG reasoning tasks. |
Predicting Semantic Relations using Global Graph Properties (D18-1)
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| Challenge: | Semantic graphs encode the structural qualities of language as a representation of human knowledge. |
| Approach: | They propose a global-theoretic model that integrates global and local properties of semantic graphs to improve local prediction of relational relations between synsets. |
| Outcome: | The proposed model improves on the local task of predicting semantic relations between synsets, yielding state-of-the-art results on the WN18RR dataset. |
Structural Encoding and Pre-training Matter: Adapting BERT for Table-Based Fact Verification (2021.eacl-main)
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| Challenge: | Existing research on fact verification focuses on unstructured textual evidence, but it is still underexplored. |
| Approach: | They propose to use a table-based language model to verify textual statements . they use cell embeddings and numerical information to improve accuracy . |
| Outcome: | The proposed method outperforms the state-of-the-art model on question answering tasks even without modeling numerical information. |
N-ary Constituent Tree Parsing with Recursive Semi-Markov Model (2021.acl-long)
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| Challenge: | Existing graph-based constituent parsing methods generate hidden nodes with the dummy label inside the n-ary nodes to transform the tree into a binary tree for prediction. |
| Approach: | They propose a graph-based constituent parsing framework that uses a 1-order semi-Markov model to predict the immediate children sequence of a constituent candidate. |
| Outcome: | The proposed framework obtains the F1 of 95.92% and 92.50% on the datasets of PTB and CTB 5.1 respectively. |
KGHaluBench: A Knowledge Graph-Based Hallucination Benchmark for Evaluating the Breadth and Depth of LLM Knowledge (2026.findings-eacl)
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| Challenge: | Existing benchmarks for large language models are limited by static and narrow questions, leading to limited coverage and misleading evaluations. |
| Approach: | They propose a Knowledge Graph-based hallucination benchmark that assesses Large Language Models across the breadth and depth of their knowledge and provides a fairer and more comprehensive insight into LLM truthfulness. |
| Outcome: | The proposed framework assesses LLMs across breadth and depth of their knowledge, and provides a fairer and more comprehensive insight into LLM truthfulness. |
Beyond Model Performance: Can Link Prediction Enrich French Lexical Graphs? (2024.lrec-main)
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| Challenge: | lexical resources are essential for the development of NLP systems, but with advances in language models and deep learning, they are increasingly being replaced by web-derived text. |
| Approach: | They propose a resource-centric study of link prediction approaches over French lexical-semantic graphs. |
| Outcome: | The proposed method is more accurate and reliable than previous methods. |
Structure-Augmented Keyphrase Generation (2021.emnlp-main)
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| Challenge: | Creating keyphrases that are likely to be words absent from the given document is challenging . |
| Approach: | They propose novel keyphrase generation tasks that augment missing context by adding keyphrases to documents. |
| Outcome: | The proposed keyphrase generation task outperforms the state-of-the-art in two keyphrase tasks. |
MedWriter: Knowledge-Aware Medical Text Generation (2020.coling-main)
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Youcheng Pan, Qingcai Chen, Weihua Peng, Xiaolong Wang, Baotian Hu, Xin Liu, Junying Chen, Wenxiu Zhou
| Challenge: | Recent studies focus on the information of unstructured text rather than structured information of the knowledge graph. |
| Approach: | They propose a knowledge-aware text generation model for medical domains that incorporates knowledge graphs into the model to improve the quality of generated text. |
| Outcome: | The proposed model improves the quality of generated text and has robust superiority over other methods. |
Learning to Decompose and Organize Complex Tasks (2021.naacl-main)
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| Challenge: | Using a novel end-to-end pipeline, we propose a solution that consumes a complex task and induces 'dependency graphs' from unstructured text to represent sub-tasks and their relationships. |
| Approach: | They propose a pipeline that consumes a complex task and induces 'dependency graphs' from unstructured text to represent sub-tasks and their relationships. |
| Outcome: | The proposed pipeline outperforms state-of-the-art graph induction pipelines in a dataset of complex tasks with their sub-task graphs. |
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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| 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 . |
Can Knowledge Graphs Reduce Hallucinations in LLMs? : A Survey (2024.naacl-long)
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| Challenge: | Increasing the use of knowledge graphs to augment LLMs has led to hallucinations . large language models (LLMs) are prone to producing hallucinosis due to knowledge gaps . |
| Approach: | They review knowledge graph-based augmentation techniques in large language models to assess their effectiveness and examine their performance. |
| Outcome: | The proposed methods have been evaluated against three groups of LLMs and offer methodological comparisons and performance evaluations. |
GRAG: Graph Retrieval-Augmented Generation (2025.findings-naacl)
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| Challenge: | Naive Retrieval-Augmented Generation (RAG) focuses on individual documents during retrieval and is not suitable for networked documents. |
| Approach: | They propose a novel divide-and-conquer strategy that retrieves optimal subgraph structure in linear time. |
| Outcome: | The proposed approach outperforms current state-of-the-art methods on graph reasoning benchmarks. |
A Top-down Graph-based Tool for Modeling Classical Semantic Maps: A Case Study of Supplementary Adverbs (2025.naacl-long)
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| Challenge: | Semantic map models (SMMs) construct a network-like conceptual space from cross-linguistic instances or forms based on the connectivity hypothesis. |
| Approach: | They propose a graph-based algorithm that automatically generates conceptual spaces and SMMs in a top-down manner. |
| Outcome: | The proposed model is compared with human annotations and other automated methods on cross-linguistic supplementary adverbs. |
CAGK: Collaborative Aspect Graph Enhanced Knowledge-based Recommendation (2024.lrec-main)
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| Challenge: | Existing KG-based recommendations have low link rates, redundant knowledge in KG, and low ratings and negative aspect sentiment. |
| Approach: | They propose a model that integrates auxiliary information such as social networks, user or item attributes, images, contextual data, etc. |
| Outcome: | The proposed model improves on two widely used benchmark datasets, Amazon-book and Yelp2018. |
Open-Domain Contextual Link Prediction and its Complementarity with Entailment Graphs (2021.findings-emnlp)
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| Challenge: | Existing methods for linking knowledge graphs only use textual contexts . contextual link prediction is useful for finding context-dependent entailments . |
| Approach: | They propose a task of open-domain contextual link prediction which uses textual context and KG structure to perform link prediction. |
| Outcome: | The proposed model can ground the triples in the context of the original dataset and infer missing relations in context. |
Identifying Predictive Causal Factors from News Streams (D19-1)
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| Challenge: | Existing word embedding techniques are not suited to learn relationships between words in different documents and contexts. |
| Approach: | They propose a new framework to uncover the relationship between news events and real world phenomena by measuring how word occurrence influences future occurrence. |
| Outcome: | The proposed framework outperforms existing methods in stock price prediction errors for 12 months and 4 years. |
Knowledge Graph Unlearning with Schema (2025.coling-main)
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| Challenge: | Unlearning on knowledge graphs has not been extensively studied. |
| Approach: | They propose a new unlearning method based on schema for knowledge graph (KG) they update the representation of the deleted element’s neighborhood with an unlearning object that regulates the affinity between the affected neighborhood and the instances within the same schema. |
| Outcome: | The proposed method is evaluated on various KG embedding models with benchmark datasets. |
Old is Gold: Linguistic Driven Approach for Entity and Relation Linking of Short Text (N19-1)
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Ahmad Sakor, Isaiah Onando Mulang’, Kuldeep Singh, Saeedeh Shekarpour, Maria Esther Vidal, Jens Lehmann, Sören Auer
| Challenge: | Short texts challenge NLP tasks because they lack context or are partially malformed. |
| Approach: | They propose a method which maps entities and relations within a short text to Wikipedia mentions. |
| Outcome: | The proposed approach outperforms state-of-the-art methods for short text query inventories. |
SALT: Step-level Advantage Assignment for Long-horizon Agents via Trajectory Graph (2026.findings-eacl)
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| Challenge: | Large Language Models (LLMs) have demonstrated remarkable capabilities, but their application to complex, multi-step, and long-horizon tasks remains challenging. |
| Approach: | They propose a framework that provides a finer-grained advantage assignment derived solely from outcome rewards. |
| Outcome: | The proposed framework provides a finer-grained advantage assignment, derived solely from outcome rewards. |
Encoding Social Information with Graph Convolutional Networks forPolitical Perspective Detection in News Media (P19-1)
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| Challenge: | Identifying the political perspective shaping the way news events are discussed in the media is an important and challenging task. |
| Approach: | They propose a neural architecture for representing relational information to capture social context of news documents. |
| Outcome: | The proposed model performs better than supervised models in the supervised setting and shows that it provides a distant supervision signal. |
UniToolBench: A Benchmark for Tool-Augmented LLMs in Cross-Domain, Universal Task Automation (2026.findings-eacl)
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| Challenge: | Existing benchmarks that focus on manually curated tool graphs lack scalability and diversity across domains. |
| Approach: | They propose a large-scale, cross-domain benchmark to evaluate LLMs' ability to reason over and utilize interconnected tools for automation. |
| Outcome: | The proposed benchmark incorporates automated tool graph construction by formulating link prediction as a probabilistic task, instead of relying on categorical LLM outputs. |
SiMFy: A Simple Yet Effective Approach for Temporal Knowledge Graph Reasoning (2023.findings-emnlp)
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| 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. |
Representations for Question Answering from Documents with Tables and Text (2021.eacl-main)
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| Challenge: | a study aims to improve question answering on tables by refining table representations based on textual context. |
| Approach: | They aim to improve question answering from tables by refining table representations based on textual context. |
| Outcome: | The proposed method improves on the Natural Questions dataset using text and table representations. |
TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance (2021.acl-long)
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Fengbin Zhu, Wenqiang Lei, Youcheng Huang, Chao Wang, Shuo Zhang, Jiancheng Lv, Fuli Feng, Tat-Seng Chua
| Challenge: | Existing QA systems focus on unstructured text, structured knowledge base, or semi-structured tables. |
| Approach: | They propose a large-scale question answering model based on financial reports . numerical reasoning is usually required to infer the answer . |
| Outcome: | The proposed model achieves 58.0% inF1, an 11.1% increase over the baseline model, but still lags behind the best human model. |
Learning to Update Knowledge Graphs by Reading News (D19-1)
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| 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. |
Normalizing Compositional Structures Across Graphbanks (2020.coling-main)
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| Challenge: | Graph-based meaning representations (MRs) exhibit structural differences that reflect different theoretical and design considerations, presenting challenges to uniform linguistic analysis and cross-framework semantic parsing. |
| Approach: | They propose a method to normalize MRs at the compositional level by linguistically-grounded rules. |
| Outcome: | The proposed method increases the match in compositional structure between MRs and improves multi-task learning in a low-resource setting. |
Integrating Lexical Information into Entity Neighbourhood Representations for Relation Prediction (2021.naacl-main)
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| Challenge: | Existing methods to predict knowledge base relations are limited by maintenance costs and text-based formats. |
| Approach: | They propose a system that can extend relational database tables with information extracted from a document corpus. |
| Outcome: | The proposed system outperforms existing methods by incorporating embeddings of text-based representations of the entities and relations. |
GMH: A General Multi-hop Reasoning Model for KG Completion (2021.emnlp-main)
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| Challenge: | Knowledge graphs are incomplete with many facts missing, causing performance bottlenecks in many applications. |
| Approach: | They propose a general multi-hop reasoning task that can be formulated as a search process and can be extended to long-distance reasoning scenarios. |
| Outcome: | The proposed model improves on baselines in short and long distance reasoning scenarios. |
Knowing the No-match: Entity Alignment with Dangling Cases (2021.acl-long)
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| Challenge: | Existing approaches to find entities that cannot find alignment across knowledge graphs (KGs) despite their importance, knowledge graph is expensive and suffers from incompleteness. |
| Approach: | They propose a framework for entity alignment and dangling entity detection that can be used to abstain from predicting alignment for detected dangle entities. |
| Outcome: | The proposed framework can abstain from predicting alignment for detected dangling entities. |
IM-TQA: A Chinese Table Question Answering Dataset with Implicit and Multi-type Table Structures (2023.acl-long)
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| Challenge: | Existing benchmarks only evaluate model performance on tables with explicit table structures, which means headers are explicitly annotated and treated as model input during inference. |
| Approach: | They propose a new Table Question Answering (TQA) dataset with implicit and multi-type table structures that requires the model to understand tables without directly available header annotations. |
| Outcome: | The proposed framework outperforms baselines on a dataset with implicit and multi-type table structures and can handle multi-table tables including previously neglected complex tables. |
Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering (C18-1)
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| Challenge: | Existing approaches to Knowledge Base Question Answering focus on semantic parsing . previous work focused on selecting the correct semantic relations and not on the structure of the semantic parses . |
| Approach: | They propose to use Gated Graph Neural Networks to encode the graph structure of the semantic parse. |
| Outcome: | The proposed approach outperforms baseline models that do not explicitly model the structure. |
Multiple Knowledge GraphDB (MKGDB) (2020.lrec-1)
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| Challenge: | ConceptNet, DBpedia, WebIsAGraph, WordNet and Wikipedia category hierarchy are used to create a large-scale graph database. |
| Approach: | They propose to use multiple taxonomy backbones extracted from 5 existing knowledge graphs to create a large-scale graph database. |
| Outcome: | The proposed database is intended to favour and support the development of open-domain natural language processing applications relying on knowledge bases. |
Dual Attention Network for Cross-lingual Entity Alignment (2020.coling-main)
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| Challenge: | Experimental results on three real-world cross-lingual entity alignment datasets have shown the effectiveness of DAEA. |
| Approach: | They propose a dual attention network for cross-lingual entity alignment . they use relation-aware graph attention and hierarchical attention to solve this problem . |
| Outcome: | The proposed approach can be applied to three real-world cross-lingual entity alignment datasets. |
Inductive Relation Prediction with Logical Reasoning Using Contrastive Representations (2022.emnlp-main)
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| Challenge: | Existing methods for relation prediction in knowledge graphs (KGs) are limited by the inductive setting because entities in training process are finite. |
| Approach: | They propose a graph convolutional network-based model LogCo with logical reasoning by contrastive representations that extracts subgraphs and relational paths between two entities to supply the entity-independence. |
| Outcome: | The proposed model outperforms existing methods on twelve inductive datasets. |
Effective Token Graph Modeling using a Novel Labeling Strategy for Structured Sentiment Analysis (2022.acl-long)
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| Challenge: | Structured sentiment analysis is a dependency parsing problem, with imbalanced label distributions and large text spans. |
| Approach: | They propose a novel labeling strategy which contains two sets of token pair labels . they propose tuple extraction problem with a more balanced label distribution . |
| Outcome: | The proposed model outperforms existing models on 5 benchmark datasets in four languages. |
GRAFF: GRaph-Augmented Fine-grained Fusion for Large Language Models (2026.findings-eacl)
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| Challenge: | Existing methods to integrate graphs into LLMs compress the graph's structural information into a single token, restricting their ability to capture deep semantic and structural information. |
| Approach: | They propose a method that integrates fine-grained node-level structural information with corresponding text entities to LLMs via a lightweight, structure adapter module. |
| Outcome: | The proposed method outperforms baseline models in graph-based question answering by 10.24%. |
FGDGNN: Fine-Grained Dynamic Graph Neural Network for Rumor Detection on Social Media (2025.findings-acl)
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| Challenge: | Existing methods for detecting rumors on social media focus on coarse-grained temporal information and ignore fine-grain temporal dynamics. |
| Approach: | They propose a fine-grained dynamic graph neural network model which incorporates fine-grain temporal information into a unified framework for rumor detection. |
| Outcome: | The proposed model improves on three public real-world datasets. |
Towards Propagation Uncertainty: Edge-enhanced Bayesian Graph Convolutional Networks for Rumor Detection (2021.acl-long)
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| Challenge: | Existing studies on rumor detection focus on text content and propagation structure . however, the uncertainty caused by unreliable relations in propagation structures is common . |
| Approach: | They propose a Bayesian-based model that captures propagation uncertainty for rumor detection. |
| Outcome: | The proposed model achieves better performance than baseline methods on rumor detection and early rumour detection tasks. |
Graph Embeddings for Argumentation Quality Assessment (2022.findings-emnlp)
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| Challenge: | Argumentation is the process by which arguments are constructed, compared, evaluated in several respects and judged in order to establish whether any of them is warranted. |
| Approach: | They propose to annotate 1908 arguments tagged with quality facets from a resource of 402 persuasive essays and to use them to create a neural architecture that takes into account the support and attack relations holding among the arguments. |
| Outcome: | The proposed neural architecture outperforms state-of-the-art and standard arguments on the persuasive essays dataset. |
Faithful Inference Chains Extraction for Fact Verification over Multi-view Heterogeneous Graph with Causal Intervention (2025.coling-main)
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| Challenge: | Existing methods for fact verification do not extract faithful inference chains due to the diversity of relation paths. |
| Approach: | They propose a multi-view heterogeneous Graph with causal intervention to extract evidence graphs from the knowledge graph. |
| Outcome: | The proposed model provides precise evidence graphs and achieves state-of-the-art performance on the public KG-based fact verification dataset FactKG. |
A Comprehensive Evaluation and Correction of the TimeBank Corpus (2022.lrec-1)
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| Challenge: | TimeML is an annotation scheme for capturing temporal information in text. |
| Approach: | They propose to use TimeML to validate TimeML and provide a rich dataset of events, temporal expressions, and temporal relationships for training and testing temporal analysis systems. |
| Outcome: | The proposed methods detect and correct errors in the TimeML corpus and provide a reference corpus for training and testing temporal analysis systems. |
Exploring Key Point Analysis with Pairwise Generation and Graph Partitioning (2024.naacl-long)
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| Challenge: | Existing methods for key point analysis rely on semantic similarity instead of measuring the existence of shared key points . |
| Approach: | They propose a key point analysis approach with pairwise generation and graph partitioning to summarize arguments into a concise set of key points. |
| Outcome: | The proposed model surpasses existing models on ArgKP and QAM datasets. |
Argument Pair Extraction with Mutual Guidance and Inter-sentence Relation Graph (2021.emnlp-main)
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| Challenge: | Existing studies on argumentation mining focus on monological argumentation and dialogical argumentation. |
| Approach: | They propose a mutual guidance framework that could guide arguments in one passage . they propose an inter-sentence relation graph to effectively model the inter-relations between two sentences . |
| Outcome: | The proposed method outperforms the current state-of-the-art model. |
KERAG: Knowledge-Enhanced Retrieval-Augmented Generation for Advanced Question Answering (2025.findings-emnlp)
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| Challenge: | Traditional Knowledge Graph Question Answering (KGQA) methods rely on semantic parsing to retrieve knowledge strictly necessary for answer generation. |
| Approach: | They propose a retrieval-filtering-summarization pipeline that enhances QA coverage by retrieving a broader subgraph likely to contain relevant information. |
| Outcome: | The proposed pipeline surpasses state-of-the-art solutions by about 7% in quality and exceeds GPT-4o (Tool) by 10-21%. |
Construction of Paired Knowledge Graph - Text Datasets Informed by Cyclic Evaluation (2024.lrec-main)
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Ali Mousavi, Xin Zhan, He Bai, Peng Shi, Theodoros Rekatsinas, Benjamin Han, Yunyao Li, Jeffrey Pound, Joshua M. Susskind, Natalie Schluter, Ihab F. Ilyas, Navdeep Jaitly
| Challenge: | Prior studies have shown that sequence-to-sequence models learn to hallucinate when the conditioning data has poor correlation with the sequence being produced. |
| Approach: | They construct a dataset that pairs Knowledge Graphs (KG) and text together and compare their results to a cyclic evaluation model. |
| Outcome: | The proposed model performs better on cyclic generation of KGs than on KG-T, but less well on synchronization of KTs. |
Generating Accurate Electronic Health Assessment from Medical Graph (2020.findings-emnlp)
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| Challenge: | Existing models based on medical domain-specific knowledge or patients’ prior diagnoses and clinical encounters were mainly based upon clinical diagnoses. |
| Approach: | They propose a graph neural network model that incorporates clinical knowledge into an end-to-end corpus-learning system and builds on it. |
| Outcome: | The proposed model significantly improves the BLEU and rouge score compared with baseline models and physicians’ evaluation showed that it generates high-quality assessments. |
Capturing Global Structural Information in Long Document Question Answering with Compressive Graph Selector Network (2022.emnlp-main)
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| Challenge: | Existing methods to answer long document questions ignore the global structure of the long document, which is essential for long-range understanding. |
| Approach: | They propose a Compressive Graph Selector Network to capture the global structure of the long document in a compressive and iterative manner. |
| Outcome: | The proposed model outperforms existing methods on two datasets. |
RLKGF: Reinforcement Learning from Knowledge Graph Feedback Without Human Annotations (2025.findings-acl)
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| Challenge: | Lack of human preference labels remains a significant bottleneck when applying RLHF to a downstream domain. |
| Approach: | They propose a method that leverages human priors encoded in Knowledge Graphs (KGs) to derive RL rewards in the absence of manual annotations. |
| Outcome: | Experiments on three public and one private medical dialogue datasets show that the proposed method outperforms the competitive RLAIF in improving LLM diagnostic accuracy. |
NG-Router: Graph-Supervised Multi-Agent Collaboration for Nutrition Question Answering (2026.eacl-long)
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Kaiwen Shi, Zheyuan Zhang, Zhengqing Yuan, Keerthiram Murugesan, Vincent Galassi, Chuxu Zhang, Yanfang Ye
| Challenge: | Existing methods for nutrition question answering face limited reasoning capacity and contextual overload . poor dietary patterns are associated with more than 11 million deaths in 2017 . |
| Approach: | They propose a framework that enables supervised multi-agent collaboration for nutritional QA. |
| Outcome: | The proposed framework outperforms single-agent and ensemble baselines in multi-agency reasoning tasks. |
Converting Legacy Data to CLDF: A FAIR Exit Strategy for Linguistic Web Apps (2024.lrec-main)
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| Challenge: | a number of web applications that enabled comparative linguistics research became obsolete . cross-linguistic data formats (CLDF) are available for use in linguistic research . |
| Approach: | a new standard allows researchers to convert legacy linguistic web apps into FAIR data . the standard uses W3C recommendations Model for Tabular Data and Metadata on the Web and MetaData Vocabulary for Tabulary . |
| Outcome: | a new standard can be used to convert legacy linguistic web apps into FAIR datasets . the standard is built on the W3C recommendations Model for Tabular Data and Metadata on the Web and MetaData Vocabulary for Tabulary on the web . |
Playing Text-Adventure Games with Graph-Based Deep Reinforcement Learning (N19-1)
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| Challenge: | Text adventure games provide a platform for exploring reinforcement learning in combinatorial action space, such as natural language. |
| Approach: | They propose a deep reinforcement learning architecture that represents the game state as a knowledge graph which is learned during exploration. |
| Outcome: | The proposed architecture can learn a control policy faster than baseline alternatives. |
PixT3: Pixel-based Table-To-Text Generation (2024.acl-long)
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| Challenge: | Table-to-text generation is a visual recognition task that uses textual descriptions from structured inputs. |
| Approach: | They propose to rethink data-to-text generation as a visual recognition task by removing the need for rendering the input in a string format. |
| Outcome: | The proposed model overcomes the challenges of linearization and input size limitations and is applicable to open-ended and controlled generation settings. |
Employing Argumentation Knowledge Graphs for Neural Argument Generation (2021.acl-long)
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| Challenge: | Existing methods for generating arguments use end-to-end knowledge graphs or are controlled with respect to the argument's topic, aspects, or stance. |
| Approach: | They construct and populate three knowledge graphs and encode them into debate portals and relevant paragraphs from Wikipedia. |
| Outcome: | The proposed model produces arguments with superior quality than those generated without knowledge. |
Summarize before Aggregate: A Global-to-local Heterogeneous Graph Inference Network for Conversational Emotion Recognition (2020.coling-main)
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| Challenge: | Existing studies focus on modeling emotion influences with utterance-level features, with little attention paid on phrase-level semantic connection between utterrances. |
| Approach: | They propose a two-stage Summarization and Aggregation Graph Inference Network which integrates inference for topic-related emotional phrases and local dependency reasoning over neighbouring utterances in a global-to-local fashion. |
| Outcome: | The proposed model outperforms the state-of-the-art models on three CER benchmark datasets. |
COMPACT: Building Compliance Paralegals via Clause Graph Reasoning over Contracts (2026.eacl-long)
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| Challenge: | Existing legal NLP benchmarks focus on single-clause tasks, such as ContractNLI and CUAD. |
| Approach: | They propose a framework that models cross-clause dependencies through structured clause graphs by extracting deontic-temporal entities from clauses and constructs typed relationship graphs capturing definitional dependencies, exception hierarchies, and temporal sequences. |
| Outcome: | The proposed framework extracts deontic-temporal entities from clauses and constructs typed relationship graphs capturing definitional dependencies, exception hierarchies, and temporal sequences. |
DynamicTOC: Persona-based Table of Contents for Consumption of Long Documents (2022.naacl-main)
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Himanshu Maheshwari, Nethraa Sivakumar, Shelly Jain, Tanvi Karandikar, Vinay Aggarwal, Navita Goyal, Sumit Shekhar
| Challenge: | Long documents are tedious to read through and can be authored by multiple entities . traditional document navigation is through a Table of Contents (ToC) but there is no way to highlight information relevant to different personas. |
| Approach: | They propose a dynamic table of content-based navigator that highlights sections of interest . DYNAMICTOC is augmented with short questions to assist users in understanding underlying content . |
| Outcome: | The proposed navigator highlights sections of interest in documents as per the aspects relevant to different personas. human and automatic evaluations suggest the efficacy of both end-to-end pipeline and different components. |
Compounding Geometric Operations for Knowledge Graph Completion (2023.acl-long)
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| Challenge: | Knowledge graph embedding (KGE) is one of the most fundamental problems in AI research. |
| Approach: | They propose a new knowledge graph embedding model by leveraging translation, rotation, and scaling operations to form a composite one. |
| Outcome: | The proposed model outperforms existing models on three KG prediction tasks. |
LiDo RDF: From a Relational Database to a Linked Data Graph of Linguistic Terms and Bibliographic Data (L18-1)
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| Challenge: | linguists and researchers benefit from the data by looking it up on the Web . a new approach allows the direct use and reuse of the data for scientific research and machine processing . |
| Approach: | They propose to convert LiDo TBD database into Linked Data graph using Semantic Web . goal is to enable direct use and reuse of data for scientific research community . |
| Outcome: | The proposed dataset is based on the framework developed by linguist Dr. Christian Lehmann 40 years ago and is available on the LiDo website since 2006. |
ReTAG: Reasoning Aware Table to Analytic Text Generation (2023.emnlp-main)
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| Challenge: | Table to text models generate descriptive summaries that repeat information contained within a table in sentences. |
| Approach: | They propose a table-aware table-to-text model that uses vector-quantization to infuse different types of analytical reasoning into the output. |
| Outcome: | The proposed model achieves 2.2%, 2.9% improvement on PARENT metric over state-of-the-art models. |
Structure-Discourse Hierarchical Graph for Conditional Question Answering on Long Documents (2023.findings-acl)
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| Challenge: | Existing approaches to conditional question answering on long documents ignore document structure and discourse relations between sentences in document sections. |
| Approach: | They construct a Structure-Discourse Hierarchical Graph and conduct bottom-up information propagation to address this issue. |
| Outcome: | The proposed approach outperforms the existing methods on the conditional question answering on long documents by 3.0 EM score and 2.4 F1 score on answer measuring, and 2.2 EM and 1.9 F1 scores on jointly answer and condition measuring. |
Reasoning over Entity-Action-Location Graph for Procedural Text Understanding (2021.acl-long)
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| Challenge: | Procedural text understanding aims at tracking the states and locations of entities mentioned in a paragraph. |
| Approach: | They propose a framework to model entities-entity, action, and location relations using a graph neural network. |
| Outcome: | The proposed approach outperforms strong baselines on two datasets, ProPara and Recipes. |
Eureka: Neural Insight Learning for Knowledge Graph Reasoning (2022.coling-1)
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| Challenge: | Existing knowledge embedding methods have limited performance on knowledge graph reasoning tasks . eureka is empowered to learn seen relations with sufficient training triples . |
| Approach: | They propose a neural insight learning framework called Eureka to bridge the “seen” to “unsea” gap . Eureca is empowered to learn seen relations with sufficient training triples while providing flexibility to learn unseen relations given only one trigger . |
| Outcome: | The proposed framework outperforms state-of-the-art models on seen and unseen relations . it can learn seen and unseen relationships with sufficient training triples . |
The ACoLi Dictionary Graph (2020.lrec-1)
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| Challenge: | ACoLi Dictionary Graph is a collection of multilingual open source dictionaries available in two machine-readable formats. |
| Approach: | They propose to map and harmonize ACoLi Dictionary Graph into a unified representation and a tabular data format to facilitate their use in NLP tasks. |
| Outcome: | The ACoLi Dictionary Graph is a collection of multilingual open source dictionaries available in two machine-readable formats. |
“Who said it, and Why?” Provenance for Natural Language Claims (2020.acl-main)
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| Challenge: | generating and publishing content is so easy, we are bombarded with information and are exposed to all kinds of claims. |
| Approach: | They propose a formal definition of provenance graph for a given natural language claim . they evaluate the approach using two benchmark datasets to capture provenance . |
| Outcome: | The proposed method shows initial success in capturing provenance and its effectiveness on claim verification. |
Multilingual SubEvent Relation Extraction: A Novel Dataset and Structure Induction Method (2022.findings-emnlp)
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| Challenge: | Existing methods for subevent relation extraction (SRE) focus on sequential order of words in texts to enhance representation learning. |
| Approach: | They propose a method that learns to induce effective graph structures for input texts . they use word alignment frameworks with dependency paths and optimal transport . |
| Outcome: | The proposed method is able to induce effective graph structures for input texts to boost representation learning. |
Iterative Knowledge Graph Refinement and Integration for Medical Question Answering (2026.findings-acl)
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| Challenge: | Existing graph-based RAG methods heuristically retrieve and refine question-relevant subgraphs, potentially introducing redundant and noisy factual information that is difficult for LLMs to process. |
| Approach: | They propose to integrate knowledge graphs (KGs) through retrieval-augmented generation methods to improve LLM reasoning by incorporating external trustworthy knowledge resources. |
| Outcome: | The proposed framework achieves state-of-the-art against baseline competitors on three medical QA benchmark datasets. |
Improving Factual Accuracy of Neural Table-to-Text Output by Addressing Input Problems in ToTTo (2024.naacl-long)
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| Challenge: | Neural Table-to-Text models produce hallucinated outputs that are factually incorrect or unrelated to the input data. |
| Approach: | They manually annotated 1,837 texts generated by multiple Neural Table-to-Text models in the politics domain of the ToTTo dataset. |
| Outcome: | The proposed model reduces factual errors by 52% to 76% . the proposed model also struggles with tabular inputs that are structured in a non-standard way, especially when the input lacks distinct row and column values or the column headers are not correctly mapped to corresponding values. |
TACR: A Table Alignment-based Cell Selection Method for HybridQA (2023.findings-acl)
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| Challenge: | Hybrid Question-Answering datasets lack a robust reasoning model for text-based QA. |
| Approach: | They propose a table-question-alignment-based cell-selection and reasoning model for hybrid text and table QA. |
| Outcome: | The proposed model outperforms baselines on HybridQA and WikiTableQuestions datasets on cell selection and argumentation. |
Mining the Past with Dual Criteria: Integrating Three types of Historical Information for Context-aware Event Forecasting (2025.emnlp-main)
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Rong Ma, Lei Wang, Yating Yang, Bo Ma, Rui Dong, Fengyi Yang, Ahtamjan Ahmat, Kaiwen Lu, Xinyue Wang
| Challenge: | Existing methods focus on entities and structural dependencies but overlook implicitly relevant information. |
| Approach: | They propose a method that leverages event semantics for relevance modeling and incorporates a self-supervised semantic filter based on factual event associations to capture implicitly relevant historical information. |
| Outcome: | The proposed method outperforms existing methods on three public benchmark datasets and is highly effective on two structured temporal knowledge graph forecasting datasets. |
Retrieval-Augmented Parsing for Complex Graphs by Exploiting Structure and Uncertainty (2023.findings-emnlp)
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| Challenge: | Retrieval augmentation is effective for large graph parsing tasks, but can fail to identify the most informative exemplars . structure-aware and uncertainty-guided adaptive retrieval (SUGAR) exploits two unique sources of information: structural similarity and model uncertainty. |
| Approach: | They propose a structure-aware and uncertainty-guided adaptive retrieval approach that exploits structural similarity and model uncertainty to improve retrieval-augmented parsing for complex graph problems. |
| Outcome: | The proposed method improves retrieval-augmented parsing for graph parsers with large output graphs and non-trivial structure. |
Related Works in the Linguistic Data Consortium Catalog (2020.lrec-1)
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| Challenge: | Existing metadata standards for Related Works are used to define relations between language resources. |
| Approach: | They describe the development and implementation of a Related Works schema and the steps to implementation. |
| Outcome: | The proposed schema has been implemented in the Linguistic Data Consortium's (LDC) Catalog. |
The Future is not One-dimensional: Complex Event Schema Induction by Graph Modeling for Event Prediction (2021.emnlp-main)
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| Challenge: | Event schemas encode knowledge of stereotypical structures of events and their connections . previous work on event schema induction focuses on atomic events or linear temporal sequences . |
| Approach: | They propose a Temporal Complex Event Schema: a graph-based schema representation that encompasses events, arguments, temporal connections and argument relations. |
| Outcome: | The proposed model outperforms existing models on HITS@1 by 17.8%. |
Organizing and Improving a Database of French Word Formation Using Formal Concept Analysis (2022.lrec-1)
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| Challenge: | Fig. 1 shows examples of five derivational families. |
| Approach: | They propose to use Formal Concept Analysis to organize and improve Démonette2 . they use a poset to represent derivational families in a partially ordered set . |
| Outcome: | The proposed approach can be used to improve the quality of a derivational database in French. |
STRUCTSUM Generation for Faster Text Comprehension (2024.acl-long)
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| Challenge: | Current large language models (LLMs) fail to adequately structure and organize information in a way that facilitates comprehension. |
| Approach: | They propose a taxonomy of problems around factuality, global and local structure common to both modalities and propose 'auto-QA' to improve the accuracy of generated structured representations. |
| Outcome: | The proposed models improve accuracy and speed without loss of accuracy. |
Sketch and Refine: Towards Faithful and Informative Table-to-Text Generation (2021.findings-acl)
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| Challenge: | Existing methods for table-to-text generation suffer from poor faithfulness and low coverage. |
| Approach: | They propose a method that combines Autoregressive and Non-Autoregressive generation to generate a table-to-text from a key-value table using a skeleton and an edit-based non-autoregressively generation model. |
| Outcome: | The proposed method outperforms the existing methods on WikiPerson and WikiBio datasets on coverage and faithfulness. |
DepWiGNN: A Depth-wise Graph Neural Network for Multi-hop Spatial Reasoning in Text (2023.findings-emnlp)
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| Challenge: | Existing approaches for spatial reasoning in text overlook the gap between natural language and symbolic structures. |
| Approach: | They propose a novel depth-wise Graph Neural Network to aggregate spatial information over the depth dimension instead of the breadth dimension of the graph. |
| Outcome: | The proposed model outperforms existing methods on two multi-hop spatial reasoning datasets. |
Annotating Temporal Dependency Graphs via Crowdsourcing (2020.emnlp-main)
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| Challenge: | Existing temporal annotation schemes have been limited due to the complexity of temporal relations between events. |
| Approach: | They propose to build a corpus of Wikinews articles annotated with temporal dependency graphs . they also propose a crowdsourcing strategy to annotate TDGs based on the corpus . |
| Outcome: | The proposed method achieves a good trade-off between completeness and practicality in temporal annotation. |
Graph based Neural Networks for Event Factuality Prediction using Syntactic and Semantic Structures (P19-1)
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| Challenge: | Existing work on event factuality prediction (EFP) relies on syntactic and semantic information to identify important context words. |
| Approach: | They propose a graph-based neural network that integrates syntactic and semantic information more effectively. |
| Outcome: | The proposed model integrates syntactic and semantic information more effectively . it provides more meaningful information for downstream tasks than classification formulations . |
Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs (2026.acl-long)
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Yu Li, Xiaoran Shang, Qizhi Pei, Yun Zhu, Xin Gao, Honglin Lin, Zhanping Zhong, Zhuoshi Pan, Zheng Liu, Xiaoyang Wang, Conghui He, Dahua Lin, Feng Zhao, Lijun Wu
| Challenge: | High-quality post-training data is the primary engine driving LLM capabilities . datasets are often treated as isolated artifacts, overlooking their true developmental context . |
| Approach: | They propose a framework to reconstruct the evolutionary graph of dataset development using data lineage. |
| Outcome: | The proposed framework characterizes domain-specific structural patterns in Math-oriented datasets and general-domain corpora. |
Event Detection: Gate Diversity and Syntactic Importance Scores for Graph Convolution Neural Networks (2020.emnlp-main)
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| Challenge: | Recent studies on event detection (ED) have shown that the syntactic dependency graph can be employed in graph convolutional neural networks (GCNs) but the computation of the hidden vectors in such graph-based models is agnostic to the trigger candidate words, leaving irrelevant information for the trigger candidates. |
| Approach: | They propose a mechanism to filter noisy information in the hidden vectors of graph-based models based on the information from the trigger candidate. |
| Outcome: | The proposed model achieves state-of-the-art on two ED datasets. |
HiddenTables and PyQTax: A Cooperative Game and Dataset For TableQA to Ensure Scale and Data Privacy Across a Myriad of Taxonomies (2023.emnlp-main)
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| Challenge: | A myriad of different Large Language Models (LLMs) face a common challenge in contextually analyzing table question-answering tasks. |
| Approach: | They propose a cooperative game that is played between the code-generating LLM "Solver" and the "Oracle" it is based on natural language schemas and ensures the security of the underlying data. |
| Outcome: | The proposed game shows that LLMs are ineffective at generalizing and performing on complex queries, handle compositional dependencies, and align natural language to programmatic commands when concrete table schemas are provided. |
S2ORC: The Semantic Scholar Open Research Corpus (2020.acl-main)
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| Challenge: | Academic papers are an increasingly important textual domain for natural language processing (NLP) research. |
| Approach: | They propose to aggregate 81.1M English-language academic papers into a unified source . they hope this resource will facilitate research and development of tools for text mining over academic text. |
| Outcome: | The proposed corpus includes metadata, abstracts, bibliographic references, and structured full text for 8.1M open access papers. |
Knowledge Graph-Guided Retrieval Augmented Generation (2025.naacl-long)
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| Challenge: | Existing studies on RAG focus on semantic retrieval of isolated relevant chunks, which ignore their intrinsic relationships. |
| Approach: | They propose a framework that utilizes knowledge graphs to provide fact-level relationships between chunks, improving the diversity and coherence of the retrieved results. |
| Outcome: | Extensive experiments on the HotpotQA dataset and its variants demonstrate the advantages of KG2RAG compared to existing RAG-based approaches in terms of response quality and retrieval quality. |
Multi-Row, Multi-Span Distant Supervision For Table+Text Question Answering (2023.acl-long)
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Vishwajeet Kumar, Yash Gupta, Saneem Chemmengath, Jaydeep Sen, Soumen Chakrabarti, Samarth Bharadwaj, Feifei Pan
| Challenge: | Existing question answering systems for tables and linked text are relatively unexplored. |
| Approach: | They propose a transformer-based question answering system that copes with distant supervision along both axes of the question and answer. |
| Outcome: | The proposed system beats baselines for HybridQA and OTT-QA with best EM and F1 scores on a held out test set. |
GLoCIM: Global-view Long Chain Interest Modeling for news recommendation (2025.coling-main)
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| Challenge: | Recent efforts to extract local subgraph information from click graphs have hindered collaboratively utilizing global click graph information. |
| Approach: | They propose a global-view long chain interests model that models a click graph with neighbor interest to enhance news recommendation. |
| Outcome: | The proposed method surpasses baseline methods on two real-world datasets. |
Dynamic Anticipation and Completion for Multi-Hop Reasoning over Sparse Knowledge Graph (2020.emnlp-main)
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| Challenge: | Existing reasoning methods for sparse KGs are incomplete and lack of evidential paths to target entities makes multi-hop reasoning difficult. |
| Approach: | They propose a multi-hop reasoning model over sparse KGs to solve this problem . they use latent prediction of embedding-based models to make the model perform more potential path search over sparses . |
| Outcome: | The proposed method outperforms state-of-the-art models on five datasets from Freebase, NELL and Wikidata. |
IRIS: An Iterative and Integrated Framework for Verifiable Causal Discovery in the Absence of Tabular Data (2025.acl-long)
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| Challenge: | Existing statistical methods for causal discovery are expensive, require high-quality structured tabular data, and are often not available for a wide range of NLP applications. |
| Approach: | They propose a framework that combines statistical and large language model methods to discover causal relations from a set of initial variables. |
| Outcome: | The proposed method combines statistical and LLM-based methods to discover known and novel causal relations. |
MAGIC: A Multi-Hop and Graph-Based Benchmark for Inter-Context Conflicts in Retrieval-Augmented Generation (2025.findings-emnlp)
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| Challenge: | Existing benchmarks for investigating knowledge conflict have notable limitations, including a narrow focus on the question answering setup, heavy reliance on entity substitution techniques, and a restricted range of conflict types. |
| Approach: | They propose a knowledge graph-based framework that generates varied and subtle conflicts between two similar yet distinct contexts while ensuring interpretability through the explicit relational structure of KGs. |
| Outcome: | The proposed framework generates varied and subtle conflicts between two similar yet distinct contexts while ensuring interpretability through the explicit relational structure of KGs. |
DAGN: Discourse-Aware Graph Network for Logical Reasoning (2021.naacl-main)
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| Challenge: | Recent QA with logical reasoning questions requires passage-level relations among the sentences. |
| Approach: | They propose a discourse-aware graph network that aggregates passage-level clues for QA by using discourse-based information. |
| Outcome: | The proposed model achieves competitive results on two logical reasoning QA datasets. |
Duality of Link Prediction and Entailment Graph Induction (P19-1)
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| Challenge: | In this paper, we show that link prediction and entailment graph induction are complementary. |
| Approach: | They propose an entailment score that exploits the new facts discovered by the link prediction model and then form engorgement graphs between relations. |
| Outcome: | The proposed entailment score outperforms prior state-of-the-art results on a standard entialment dataset and the new link prediction scores show improvements over the raw link prediction score. |
Capturing Relations between Scientific Papers: An Abstractive Model for Related Work Section Generation (2021.acl-long)
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| Challenge: | Existing related work generation models are inflexible and extract sentences from multiple papers to form a related work discussion. |
| Approach: | They propose a Relation-aware Related work generator which generates an abstractive related work from the given multiple scientific papers in the same research area. |
| Outcome: | The proposed model improves over existing models and can be used to familiarize researchers with the state of the art in the field. |
DRAMA: Dynamic Multi-Granularity Graph Estimate Retrieval over Tabular and Textual Question Answering (2024.lrec-main)
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| Challenge: | TableTextQA tasks require tabular and textual data, gaining increasing attention . however, row-based approaches suffer from limitations such as lack of interaction between rows . |
| Approach: | They propose a method that incorporates an interaction mechanism among multiple rows . Empirical results demonstrate that the proposed method is effective . |
| Outcome: | Empirical results show that the proposed model is effective on tabFact and HybridQA datasets. |
Coherent Comments Generation for Chinese Articles with a Graph-to-Sequence Model (P19-1)
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| Challenge: | Existing models for article comment generation are too long and often result in general and irrelevant comments. |
| Approach: | They propose to generate comments with a graph-to-sequence model that models the input news as a topic interaction graph. |
| Outcome: | The proposed model can generate coherent and informative comments compared with several strong baseline models. |
EffiQA: Efficient Question-Answering with Strategic Multi-Model Collaboration on Knowledge Graphs (2025.coling-main)
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Zixuan Dong, Baoyun Peng, Yufei Wang, Jia Fu, Xiaodong Wang, Xin Zhou, Yongxue Shan, Kangchen Zhu, Weiguo Chen
| Challenge: | Existing approaches that integrate LLMs and KGs either underutilize the reasoning abilities of LLM or suffer from prohibitive computational costs due to tight coupling. |
| Approach: | They propose a framework that can strike a balance between performance and efficiency via an iterative paradigm. |
| Outcome: | The proposed framework can strike a balance between performance and efficiency via an iterative paradigm. |
Handling Divergent Reference Texts when Evaluating Table-to-Text Generation (P19-1)
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| Challenge: | Existing text generation metrics rely on reference texts, such as BLEU and ROUGE, but they are too expensive to apply repeatedly. |
| Approach: | They propose a metric which aligns n-grams from the generated texts to the semi-structured data before computing their precision and recall. |
| Outcome: | The proposed metric correlates with human judgments better than existing text generation metrics while being easier to use. |
Simple and Effective Graph-to-Graph Annotation Conversion (2022.coling-1)
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| Challenge: | Existing work on graph-structured annotation conversions has focused on feature-based models which are not easily applicable to new conversions. |
| Approach: | They propose two graph-to-graph conversion approaches which use pseudo data and inherit parameters to guide conversions respectively. |
| Outcome: | The proposed approaches outperform strong baselines with higher conversion score on a graph-structured dataset and other datasets. |
A Re-evaluation of Knowledge Graph Completion Methods (2020.acl-main)
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| Challenge: | Knowledge Graph Completion (KGC) aims at automatically predicting missing links for large-scale knowledge graphs. |
| Approach: | They propose a protocol to evaluate KGC methods that is robust to handle bias in the model, which can substantially affect the final results. |
| Outcome: | The proposed evaluation protocol is robust to handle bias in the model, which can substantially affect the final results. |
DIGAT: Modeling News Recommendation with Dual-Graph Interaction (2022.findings-emnlp)
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| Challenge: | Existing news recommendation methods lack effective news-user feature interaction. |
| Approach: | They propose to use news-graph and user-graph channels to enhance news encodings . they also propose to perform effective feature interaction between news and user graphs based on semantic-augmented graphs. |
| Outcome: | The proposed graph attention networks outperform existing NR methods on the benchmark dataset MIND. |
CortexDebate: Debating Sparsely and Equally for Multi-Agent Debate (2025.findings-acl)
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| Challenge: | Existing methods to improve the reasoning performance of LLMs suffer from two major shortcomings: too lengthy input contexts and overconfidence dilemma. |
| Approach: | They propose a method to debating among LLM agents using a sparse debator graph . they use a module called McKinsey-based Debate Matter to optimize the debators . |
| Outcome: | The proposed method has been well demonstrated across eight datasets from four task types. |
MuGER2: Multi-Granularity Evidence Retrieval and Reasoning for Hybrid Question Answering (2022.findings-emnlp)
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| Challenge: | Conventional HQA models retrieve coarse- or fine-grained evidence to reason the answer . however, they neglect a more general scenario requiring reasoning over heterogeneous data to answer a question. |
| Approach: | They propose a multi-granularity evidence retrieval and reasoning approach to answer questions over heterogeneous data using tables and passages linked to table cells. |
| Outcome: | The proposed approach significantly boosts the performance on the HybridQA dataset. |
Data Collection vs. Knowledge Graph Completion: What is Needed to Improve Coverage? (2021.emnlp-main)
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| Challenge: | Knowledge Graph Completion (KGC) attempts to learn missing links from subsets. |
| Approach: | This survey/position paper discusses ways to improve coverage of resources such as WordNet. |
| Outcome: | The proposed method improves WordNet coverage by reducing the number of words in the sample and reducing unbalanced corpora. |
MusKGC: A Flexible Multi-source Knowledge Enhancement Framework for Open-World Knowledge Graph Completion (2025.emnlp-main)
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| Challenge: | Open-world knowledge graph completion (KGC) aims to infer novel facts by enriching existing graphs with external knowledge sources while maintaining semantic consistency under the open-world assumption (OWA). |
| Approach: | They propose a multi-source knowledge enhancement framework based on an open-world assumption (OWA) that integrates external knowledge sources and a new evaluation strategy to validate new facts. |
| Outcome: | The proposed model achieves SOTA performance across benchmarks and the evaluation strategy effectively assesses new facts under OWA. |
Multi-hop Graph Convolutional Network with High-order Chebyshev Approximation for Text Reasoning (2021.acl-long)
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| Challenge: | Existing single-hop graph reasoning in Graph convolutional networks may miss some important non-consecutive dependencies. |
| Approach: | They propose a graph convolutional network with the high-order dynamic Chebyshev approximation which augments multi-hop graph reasoning by fusing messages aggregated from direct and long-term dependencies into one convolutionalist layer. |
| Outcome: | The proposed model improves on four transductive and inductive NLP tasks and the ablation of the existing model. |
Exploring and Evaluating Attributes, Values, and Structures for Entity Alignment (2020.emnlp-main)
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| Challenge: | Entity alignment (EA) aims at building a Knowledge Graph (KG) of rich content by linking the equivalent entities from various KGs. |
| Approach: | They propose to use an attributed value encoder to partition a Knowledge Graph into subgraphs to model the various types of attribute triples efficiently. |
| Outcome: | The proposed method achieves significant improvements over 12 baselines in cross-lingual and monolingual datasets. |
Toward An Epic Epigraph Graph (L18-1)
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| Challenge: | a database of epigraphs is being developed to reveal literary influence as a set of connections between authors over time. |
| Approach: | a database of epigraphs is created to map literary influence as a set of connections between authors . the database is being developed under an open license . |
| Outcome: | a database of epigraphs is being developed to reveal literary influence over time . the database includes epigraph quotations from over 12,000 literary works . authors use epigraph to set theme and link work to existing body of literature . |
Question-guided Knowledge Graph Re-scoring and Injection for Knowledge Graph Question Answering (2024.findings-emnlp)
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| Challenge: | Knowledge graph question answering (KGQA) aims to provide factual answers to natural language questions by leveraging structured information stored in a knowledge graph. |
| Approach: | They propose a Question-guided Knowledge Graph Re-scoring method to eliminate noisy pathways for the input question, thereby focusing specifically on pertinent factual knowledge. |
| Outcome: | The proposed method eliminates noisy pathways for the input question, thereby focusing specifically on pertinent factual knowledge. |
SHAPE: Unifying Safety, Helpfulness and Pedagogy for Educational LLMs (2026.acl-long)
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| Challenge: | Existing educational LLMs are vulnerable to pedagogical jailbreaks where students use answer-inducing prompts to elicit solutions rather than scaffolded instructions. |
| Approach: | They propose a graph-augmented tutoring pipeline that infers prerequisite concepts from queries and identifies mastery gaps. |
| Outcome: | The proposed method improves safety under two pedagogical jailbreak scenarios while maintaining near-ceiling helpfulness under the same evaluation protocol. |
PTB Graph Parsing with Tree Approximation (P19-1)
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| Challenge: | a new method for nonlocal dependency identification is proposed for the Penn Treebank (PTB) syntactic structures are represented as graphs due to nonlocal dependencies . |
| Approach: | They propose a method that approximates PTB graph-structured representations by trees . their method reduces nonlocal dependency identification and constituency parsing into single parser . |
| Outcome: | The proposed method outperforms the previous methods in nonlocal dependency identification. |
GraCoRe: Benchmarking Graph Comprehension and Complex Reasoning in Large Language Models (2025.coling-main)
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| Challenge: | Existing benchmarks focus primarily on pure graph understanding, lacking a comprehensive evaluation across all graph types and detailed capability definitions. |
| Approach: | They propose a benchmark to evaluate LLMs' graph comprehension and reasoning abilities using a three-tier hierarchical taxonomy and a granular taxonomies. |
| Outcome: | The proposed model includes 11 datasets with 5,140 graphs of varying complexity. |
Enhancing Knowledge Selection via Multi-level Document Semantic Graph (2024.lrec-main)
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| Challenge: | Existing methods view knowledge selection as a sentence matching or classification. Existing techniques can’t capture the semantic relationships within complex documents. |
| Approach: | They propose a method that can construct multi-level document semantic graph from the grounding document and store semantic relationships within the documents effectively. |
| Outcome: | The proposed method can store semantic relationships within documents effectively and efficiently and achieve state-of-the-art results on public datasets. |
Structure-aware Knowledge Graph-to-text Generation with Planning Selection and Similarity Distinction (2023.emnlp-main)
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| Challenge: | Existing methods to generate knowledge graph-to-text (KG-to) text rely on pre-trained language models to bridge the gap between the different structures of the input KG and the target text. |
| Approach: | They propose a method that integrates graph structure-aware modules with pre-trained language models to capture the intricate topology information present in the KG. |
| Outcome: | The proposed model captures the topology information present in the knowledge graph and distinguishes similar input KGs through contrastive learning techniques. |
A Generative Adaptive Replay Continual Learning Model for Temporal Knowledge Graph Reasoning (2025.acl-long)
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| Challenge: | Existing Continual Learning (CL)-based Temporal Knowledge Graph Reasoning methods are incomplete and reorganize historical facts without preserving historical knowledge. |
| Approach: | They propose a method which generates and adaptively replays historical entity distributions from the whole historical context. |
| Outcome: | The proposed method outperforms baselines in reasoning and mitigating forgetting. |
Building a Knowledge Graph from Natural Language Definitions for Interpretable Text Entailment Recognition (L18-1)
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| Challenge: | a conceptual model for dictionary definitions is used to construct a knowledge graph from natural language definitions. |
| Approach: | They propose a method for automatically building a graph world knowledge base from natural language definitions. |
| Outcome: | The proposed method was used in an interpretable text entailment recognition approach. |
RHGN: Relation-gated Heterogeneous Graph Network for Entity Alignment in Knowledge Graphs (2023.findings-acl)
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| Challenge: | Existing methods for entity alignment fail to account for heterogeneity among KGs and distinction between KG entities and relations. |
| Approach: | They propose a Relation-gated Heterogeneous Graph Network (RHGN) that uses a relation-gate based convolutional layer to distinguish relations and entities in the KG. |
| Outcome: | Extensive experiments on four datasets show that the proposed method is superior to state-of-the-art methods. |
Can LLMs be Good Graph Judge for Knowledge Graph Construction? (2025.emnlp-main)
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| Challenge: | Existing methods for converting unstructured text into structured Knowledge Graphs (KGs) have limitations such as large amount of noise, inaccurate knowledge, and hallucination . |
| Approach: | They propose a GraphJudge framework to reduce noise in real-world documents . they propose Graphjudge to fine-tune a LLM as a graph judge to enhance quality . |
| Outcome: | The proposed framework eliminates noise in real-world documents and improves the quality of generated KGs. |
Aligning Actions Across Recipe Graphs (2021.emnlp-main)
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| Challenge: | a recipe explains step by step how to cook a dish, but recipes differ in which cooking actions they describe explicitly, how they describe them, and in which order. |
| Approach: | They propose a recipe corpus which annotates cooking steps in recipes at sentence level . they train a neural model to predict recipes on ARA and model it for automatic understanding . |
| Outcome: | The proposed model can predict recipes with fine-grained structural information . it shows that recipes can be explained in different ways, or not at all . |
DWUG: A large Resource of Diachronic Word Usage Graphs in Four Languages (2021.emnlp-main)
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| Challenge: | Existing methods for graded contextual word meaning annotation have not been implemented yet. |
| Approach: | They propose a multi-round incremental annotation process and a clustering algorithm to group usages into senses to create a large-scale dataset. |
| Outcome: | The proposed method is the largest resource of graded contextualized, diachronic word meaning annotation in four different languages, based on 100,000 human semantic proximity judgments. |
Bipartite Flat-Graph Network for Nested Named Entity Recognition (2020.acl-main)
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| Challenge: | Existing models only consider the unidirectional delivery of information from innermost layers to outer ones, but instead focus on nested entities. |
| Approach: | They propose a bipartite flat-graph network (BiFlaG) for nested named entity recognition (NER) the bipartites are bidirectional LSTM and graph convolutional network (GCN) they first use the entities recognized by the flat NER module to construct an entity graph . |
| Outcome: | The proposed model outperforms existing models on three standard nested NER datasets. |
Neighborhood Matching Network for Entity Alignment (2020.acl-main)
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| Challenge: | Structural heterogeneity between knowledge graphs is an outstanding challenge for entity alignment. |
| Approach: | They propose a framework for entity alignment that uses a neighborhood matching module to combine neighborhood differences. |
| Outcome: | The proposed framework outperforms existing methods on three datasets. |
Social Bot-Aware Graph Neural Network for Early Rumor Detection (2022.coling-1)
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| 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. |
Is Graph Structure Necessary for Multi-hop Question Answering? (2020.emnlp-main)
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| 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 . |
Sentence Ordering with a Coherence Verifier (2023.findings-acl)
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| Challenge: | Recent sentence ordering studies can be classified into 2 categories: pair-wise ranking-based and sequence generation-based methods. |
| Approach: | They propose a sentence ordering method by plugging a coherence verifier into ranking-based and sequence generation-based methods. |
| Outcome: | The proposed method improves on topological sorting-based and pointer network-based methods with topological and point-based models. |
Bridging Distance and Spectral Positional Encodings via Anchor-Based Diffusion Geometry Approximation (2026.acl-long)
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| Challenge: | Molecular graph learning benefits from positional signals that capture local neighborhoods and global topology. |
| Approach: | They propose to use anchor-based distance encodings to approximate diffusion geometry. |
| Outcome: | The proposed model outperforms models without positional encodings on DrugBank with a shared GNP-based DDI backbone. |
Message Passing for Hyper-Relational Knowledge Graphs (2020.emnlp-main)
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| Challenge: | Existing approaches to link prediction over knowledge graphs (KGs) are designed to work over triple-based models, where facts are represented as binary relations between entities. |
| Approach: | They propose a message passing based graph encoder - StarE capable of modeling hyper-relational knowledge graphs (KGs) they propose to encode an arbitrary number of additional information along with the main triple while keeping the semantic roles of qualifiers and triples intact. |
| Outcome: | The proposed model outperforms existing models across multiple benchmarks and shows that leveraging qualifiers is vital for link prediction. |
Are Message Passing Neural Networks Really Helpful for Knowledge Graph Completion? (2023.acl-long)
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| Challenge: | Existing knowledge graphs are far from complete with large portions of triplets missing. |
| Approach: | They propose to use Graph Neural Networks to learn powerful embeddings to improve model performance. |
| Outcome: | The proposed models achieve comparable performance to MLP models, suggesting that MP may not be as crucial as previously thought. |
Parsing into Variable-in-situ Logico-Semantic Graphs (2020.acl-main)
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| Challenge: | a new type of graph-based meaning representation allows analysis for scope-related phenomena. |
| Approach: | They propose variable-in-situ logico-semantic graphs to bridge gap between semantic graph and logical form parsing. |
| Outcome: | The proposed graph-based meaning representation achieves 92.39% accuracy in terms of elementary dependency match . the output of the proposed parser is highly coherent . |
Enhancing Safe and Controllable Protein Generation via Knowledge Preference Optimization (2025.acl-long)
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| Challenge: | Protein language models pose significant risks of generating harmful sequences, e.g., viral transmissibility, drug resistance, environmental imbalances, public health crises, etc. |
| Approach: | They propose a protein-based model that integrates prior knowledge via a Protein Safety Knowledge Graph to minimize the risk of generating harmful sequences. |
| Outcome: | The proposed framework reduces the likelihood of producing hazardous sequences while maintaining high functionality. |
Efficient Mind-Map Generation via Sequence-to-Graph and Reinforced Graph Refinement (2021.emnlp-main)
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| Challenge: | Existing methods to generate mind-maps from text are difficult to capture the overall semantics of a document. |
| Approach: | They propose an efficient mind-map generation network that converts a document into a graph via sequence-to-graph. |
| Outcome: | The proposed network reduces inference time by thousands of times compared with existing methods and reveals key semantic structures better than plain text. |
Single Sequence Prediction over Reasoning Graphs for Multi-hop QA (2023.acl-long)
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| Challenge: | Recent generative approaches for multi-hop question answering (QA) use fusion-in-decoder to generate a single sequence output . but, they often have difficulty accurately identifying passages corresponding to key entities in the context . |
| Approach: | They propose a single-sequence prediction method that integrates a graph structure linking key entities in each context passage to relevant subsequent passages for each question. |
| Outcome: | The proposed method improves answer exact-match/F1 scores and faithfulness of grounding on the hotpotQA dataset and achieves state-of-the-art numbers on the Musique dataset. |
Disentangled Multi-span Evolutionary Network against Temporal Knowledge Graph Reasoning (2025.findings-acl)
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| Challenge: | Existing methods for temporal knowledge Graphs neglect internal structural interactions between subgraphs and ignore potential smooth features that do not lead to semantic changes. |
| Approach: | They propose to use a disentangled multi-span evolutionary network to capture local neighbor features while perceiving historical neighbor semantic information. |
| Outcome: | Extensive experiments show that the proposed model outperforms the state-of-the-art in TKG reasoning by 22.7%. |
From Graph to Word Bag: Introducing Domain Knowledge to Confusing Charge Prediction (2024.lrec-main)
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| Challenge: | Existing charge prediction methods have shown impressive performance, but they face significant challenges when dealing with confusing charges, such as Snatch and Robbery. |
| Approach: | They propose a novel approach which introduces domain knowledge regarding constituent elements to guide the model in making judgments on confusing charges, much like a judge’s reasoning process. |
| Outcome: | The proposed approach maintains exceptional performance in imbalanced label distributions. |
Propagate-Selector: Detecting Supporting Sentences for Question Answering via Graph Neural Networks (2020.lrec-1)
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| Challenge: | Existing question-answering models do not require reasoning across sentences in the given context (passage). |
| Approach: | They propose a graph neural network that propagates information over sentences to understand information that cannot be inferred when considering sentences in isolation. |
| Outcome: | The proposed approach obtains the best performance compared to the widely used answer-selection models that do not consider the intersentential relationship. |
Beyond Topology: Generative Node Importance Estimation via Structure-Guided Semantic Reasoning (2026.findings-acl)
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| Challenge: | Existing methods for estimating node importance are limited and rely on topological aggregation. |
| Approach: | They propose a generative reasoning framework that leverages Large Language Models to generate precise importance scores for entities in Knowledge Graphs. |
| Outcome: | Extensive experiments show that the proposed framework outperforms existing methods and is generalized across domains. |
FactSpotter: Evaluating the Factual Faithfulness of Graph-to-Text Generation (2023.findings-emnlp)
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| Challenge: | Graph-to-text (G2T) generation is an important task in natural language generation as it renders graphs accessible to non-technical users in downstream applications such as question answering. |
| Approach: | They propose a metric that correctly identifies factual faithfulness and uses it to determine if a triple is present in a generated text. |
| Outcome: | The proposed metric achieves highest correlation with human annotations on data correctness, data coverage, and relevance. |
Towards Enhancing Relational Rules for Knowledge Graph Link Prediction (2023.findings-emnlp)
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| Challenge: | Existing knowledge graph reasoning methods are inadequate for missing knowledge . Various methods are explored to facilitate reasoning for missing information . |
| Approach: | They propose a novel knowledge graph reasoning approach that uses a query-related fusion gate unit to model the sequentiality of relation composition and a buffering update mechanism to alleviate lagged entity information propagation. |
| Outcome: | Experimental results show that the proposed approach is superior on both transductive and inductive link prediction tasks. |
KG-FPQ: Evaluating Factuality Hallucination in LLMs with Knowledge Graph-based False Premise Questions (2025.coling-main)
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| Challenge: | Existing benchmarks that assess this vulnerability rely on manual construction, resulting in limited size and lack of expandability. |
| Approach: | They propose a method to generate false premise questions based on knowledge graphs . they modify true triplets extracted from KGs to create false premises . |
| Outcome: | The proposed method generates semantically rich FPQs using state-of-the-art GPTs. |
Learning Latent Relations for Temporal Knowledge Graph Reasoning (2023.acl-long)
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| Challenge: | Existing methods for Temporal Knowledge Graph reasoning capture intra- and inter-time latent relations between entities that appear at different times. |
| Approach: | They propose a Latent relations Learning method for TKG reasoning that captures latent relations between entities at different times. |
| Outcome: | The proposed method exploits the intra- and inter-time latent relations of entities at different times. |
CoG: Controllable Graph Reasoning via Relational Blueprints and Failure-Aware Refinement over Knowledge Graphs (2026.acl-long)
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| Challenge: | Existing approaches to large language models often exhibit cognitive rigidity, causing reasoning stagnation. |
| Approach: | They propose a training-free framework that mimics the interplay between intuition and deliberation. |
| Outcome: | The proposed framework outperforms state-of-the-art approaches on three benchmarks. |
Hierarchical Graph Convolutional Network Approach for Detecting Low-Quality Documents (2024.lrec-main)
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| Challenge: | Consistency within a document is a crucial feature indicative of its quality . low-quality documents often lack internal consistency or contain content unrelated to headlines . |
| Approach: | They propose a hierarchical graph convolutional network that detects internal inconsistencies within a document and incongruences between the title and body. |
| Outcome: | The proposed model outperforms existing models on the inconsistency dataset and on the publicly available incongruent-related dataset. |
Hierarchical Graph Network for Multi-hop Question Answering (2020.emnlp-main)
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| Challenge: | Existing multi-hop question answering models focus on multi-level reasoning across multiple documents or paragraphs. |
| Approach: | They propose a hierarchical graph network that aggregates clues from scattered texts . they use a set of contextual encoders to initialize nodes on different levels of granularity . |
| Outcome: | The proposed model outperforms existing multi-hop QA approaches on the HotpotQA benchmark. |
Structural Information Preserving for Graph-to-Text Generation (2020.acl-main)
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| Challenge: | Existing models that mess up or drop the core structural information of input graphs are lacking in graph-to-text generation. |
| Approach: | They propose to leverage richer training signals to guide a graph-to-text generation model by focusing on autoencoding losses and back-propagating the losses to better calibrate the model. |
| Outcome: | Experiments on two benchmarks show the proposed model over a state-of-the-art model . two types of autoencoding losses are used to back-propagate the model based on multitask training . |
SRLGRN: Semantic Role Labeling Graph Reasoning Network (2020.emnlp-main)
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| Challenge: | Existing models that use context and type-matching heuristics do not provide realistic evaluation of reasoning capabilities. |
| Approach: | They propose a graph reasoning network based on the semantic structure of the sentences to learn cross paragraph reasoning paths and find supporting facts and the answer jointly. |
| Outcome: | The proposed network shows competitive performance on the HotpotQA distractor setting benchmark compared to the state-of-the-art models. |
SWAM: Adaptive Sliding Window and Memory-Augmented Attention Model for Rumor Detection (2025.emnlp-main)
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| Challenge: | Existing methods for rumor detection on social media focus on static graphs, ignoring dynamic and incremental propagation . rumour detection on the social media platform is crucial to mitigating harmful effects of rumors. |
| Approach: | They propose a sliding window and memory-augmented attention model for rumor detection . they use a dynamic propagation graph and memory to capture the long-term dependency . |
| Outcome: | The proposed model is compared with the state-of-the-art models on two public datasets. |
GraphCheck: Breaking Long-Term Text Barriers with Extracted Knowledge Graph-Powered Fact-Checking (2025.acl-long)
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Yingjian Chen, Haoran Liu, Yinhong Liu, Jinxiang Xie, Rui Yang, Han Yuan, Yanran Fu, Peng Yuan Zhou, Qingyu Chen, James Caverlee, Irene Li
| Challenge: | Existing fact-checking methods that use large language models often generate subtle factual errors. |
| Approach: | They propose a fact-checking framework that uses extracted knowledge graphs to enhance text representation. |
| Outcome: | GraphCheck outperforms existing specialized fact-checkers on seven benchmarks spanning general and medical domains . Graph Neural Networks process extracted knowledge graphs as a soft prompt, enabling efficient fact- checking in a single inference call. |
UniKER: A Unified Framework for Combining Embedding and Definite Horn Rule Reasoning for Knowledge Graph Inference (2021.emnlp-main)
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| Challenge: | Knowledge graph inference has been studied extensively due to its wide applications. |
| Approach: | They propose a framework that restricts logical rules to be definite Horn rules and can exploit the knowledge in logical rule-based reasoning and KGE in an extremely efficient way. |
| Outcome: | The proposed framework can exploit the knowledge in logical rules and improve KGE in an extremely efficient way. |
Hallucination Mitigation in Natural Language Generation from Large-Scale Open-Domain Knowledge Graphs (2023.emnlp-main)
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| Challenge: | Graph-to-text models trained on small-scale datasets or datasets with limited variety of graph shapes are not adequate for more realistic large-scale, open-domain settings. |
| Approach: | They propose a novel approach that, given a graph-sentence pair in GraphNarrative, trims the sentence to eliminate portions that are not present in the corresponding graph. |
| Outcome: | The proposed model can be trained on existing datasets and is available on github. |
Rumor Detection on Twitter with Claim-Guided Hierarchical Graph Attention Networks (2021.emnlp-main)
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| Challenge: | Existing methods for rumor detection are limited to the strict relation of user responses or oversimplify the conversation structure. |
| Approach: | They propose a method that reinforces interaction of user opinions while reducing negative impact imposed by irrelevant posts. |
| Outcome: | The proposed method improves performance on three Twitter datasets and can detect rumors at early stages. |
Improving Chemical Understanding of LLMs via SMILES Parsing (2025.emnlp-main)
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| Challenge: | Molecular string representations such as SMILES and SELFIES are becoming a standard format for applying large language models (LLMs) however, molecular strings follow complex syntactic rules for encoding molecules, which LLMs struggle to interpret. |
| Approach: | They propose a framework that parses SMILES into clean and deterministic tasks to promote graph-level molecular comprehension. |
| Outcome: | The proposed framework improves structural comprehension and competes with the baseline on the Mol-Instructions benchmark. |
Information Extraction with Differentiable Beam Search on Graph RNNs (2024.lrec-main)
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| Challenge: | Existing approaches to information extraction suffer from exposure bias due to discrepancy between training and decoding. |
| Approach: | They propose to cast graph generation as auto-regressive sequence labeling and make it aware of decoding procedure by using differentiable beam search. |
| Outcome: | The proposed model outperforms its non-decoding-aware version on ACE05 and ConLL04 datasets. |
A Diffusion Model for Event Skeleton Generation (2023.findings-acl)
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| Challenge: | Existing methods for event schema generation are noise-sensitive and error-accumulating, e.g., inability to correct errors while generating schema. |
| Approach: | They propose a novel diffusion event graph model that embeds and roundes event graphs into learnable latent representations and a denoising process to maintain the model's robustness. |
| Outcome: | The proposed model achieves better results than existing state-of-the-art models on three IED bombing datasets. |
USSA: A Unified Table Filling Scheme for Structured Sentiment Analysis (2023.acl-long)
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| Challenge: | Structured Sentiment Analysis (SSA) is a problem of bi-lexical dependency parsing . previous studies have cast it as a bottleneck because of overlap and discontinuity issues . |
| Approach: | They propose a bi-lexical dependency parsing graph and a table-filling scheme that addresses overlap and discontinuity issues. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on benchmark datasets. |
GRI-QA: a Comprehensive Benchmark for Table Question Answering over Environmental Data (2025.findings-acl)
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Michele Luca Contalbo, Sara Pederzoli, Francesco Del Buono, Venturelli Valeria, Francesco Guerra, Matteo Paganelli
| Challenge: | Existing Table Question Answering systems for environmental reporting are complex and require specialized terminology and complex questions. |
| Approach: | They propose a test benchmark to evaluate Table QA approaches in the environmental domain. |
| Outcome: | The proposed benchmark reveals a significant gap between human and model performance in multi-step reasoning. |
Multivalent Entailment Graphs for Question Answering (2021.emnlp-main)
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Nick McKenna, Liane Guillou, Mohammad Javad Hosseini, Sander Bijl de Vroe, Mark Johnson, Mark Steedman
| Challenge: | a recent study shows that drawing inferences between open domain predicates is a necessity for true language understanding. |
| Approach: | They propose to reinterpret the Distributional Inclusion Hypothesis to model entailment between predicates of different valencies. |
| Outcome: | The proposed graphs are more useful than using the same valency evidence, the authors show . they show that drawing on evidence across valencies answers more questions than using only the same evidence. |
AppBench: Planning of Multiple APIs from Various APPs for Complex User Instruction (2024.emnlp-main)
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| Challenge: | Existing state-of-the-art Large Language Models (LLMs) still cannot perform well in this situation even with the help of in-context learning and finetuning. |
| Approach: | They propose a benchmark to evaluate LLMs’ ability to plan and execute multiple APIs from various sources in order to complete the user’s task. |
| Outcome: | The proposed benchmarks show that the existing state-of-the-art LLMs still cannot perform well in this situation even with in-context learning and finetuning. |
The xtsv Framework and the Twelve Virtues of Pipelines (2020.lrec-1)
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| Challenge: | Numerous small independent tools do not fit in a larger framework, and they may not be acknowledged properly. |
| Approach: | They propose an abstract framework for building NLP pipelines which is based on xtsv and a tsvo format with header plus some additional features. |
| Outcome: | The proposed framework is based on a concrete application for a Hungarian NLP pipeline. |
A Tree Extension for CoNLL-RDF (2020.lrec-1)
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| Challenge: | CoNLL-RDF provides a bridge for popular oneword-per-line formats . main reasons for their popularity are the simplicity of tables and tab-separated values . |
| Approach: | They propose a technology that provides a bridge between knowledge graphs and natural language processing. |
| Outcome: | The proposed technology provides a bridge for popular one-word-per-line formats . it provides native support for word-level annotations, but not phrase structures or text structure . |
Dialogue Medical Information Extraction with Medical-Item Graph and Dialogue-Status Enriched Representation (2023.findings-emnlp)
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| Challenge: | Existing approaches ignore relationships between medical items and statuses in the multi-turn doctor-patient dialogue. |
| Approach: | They propose a task to extract structured medical information from free text dialogues . they propose 'Dialogue Medical Information Extraction' to model relationships between items . |
| Outcome: | The proposed model outperforms previous models and achieves state-of-the-art performance on the public benchmark data set. |
FactKG: Fact Verification via Reasoning on Knowledge Graphs (2023.acl-long)
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| Challenge: | knowledge graphs (KGs) have not been fully utilized as a knowledge source for fact verification. |
| Approach: | They propose a dataset to enable the community to better use knowledge graphs . they propose 108k natural language claims with five types of reasoning . |
| Outcome: | The proposed dataset consists of 108k natural language claims with five types of reasoning . authors believe the proposed method can advance reliability and practicality . |
Relation-Aware Question Answering for Heterogeneous Knowledge Graphs (2023.findings-emnlp)
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| Challenge: | Existing retrieval-based approaches to solve multihop Knowledge Base Question Answering (KBQA) fail to utilize information from head-tail entities and the semantic connection between relations to enhance the information capturing of relations in KGs. |
| Approach: | They propose to use a dual relation graph to find the answer entity in a knowledge graph . they use primal entity graph reasoning, dual relation grafitment and interaction . |
| Outcome: | The proposed approach achieves significant performance gain over the prior state-of-the-art on two public datasets, WebQSP and CWQ. |
Tree-KG: An Expandable Knowledge Graph Construction Framework for Knowledge-intensive Domains (2025.acl-long)
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| Challenge: | Knowledge graphs are a useful tool for organizing complex data in knowledge-intensive domains. |
| Approach: | They propose an expandable framework that combines structured domain texts with advanced semantic techniques to create a tree-like graph from textbooks. |
| Outcome: | The proposed framework surpasses competing methods in the text-Annotated dataset with high scores on the Text-Annalytated data. |
Linking Named Entities in Diderot’s Encyclopédie to Wikidata (2024.lrec-main)
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| Challenge: | Encyclopédie was published between 1751 and 1772 and aimed to collect the knowledge of its time. |
| Approach: | They describe the annotation of more than 9,100 Encyclopédie entries with Wikidata identifiers . they extract all geographic entries and annotate 8,300 entries having a geographic content only. |
| Outcome: | The annotation process and application examples are presented in this paper. |
Improving Multi-hop Logical Reasoning in Knowledge Graphs with Context-Aware Query Representation Learning (2024.findings-acl)
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| Challenge: | Existing methods rely on linear sequential operations to solve First-Order Logic queries. |
| Approach: | They propose a model-agnostic approach that fully integrates the context of the query graph. |
| Outcome: | The proposed method improves performance on two datasets by 19.5%. |
WebClipper: Efficient Evolution of Web Agents with Graph-based Trajectory Pruning (2026.acl-long)
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Junjie Wang, Zequn Xie, Dan Yang, Jie Feng, Yue Shen, Duolin Sun, Meixiu Long, Yihan Jiao, Zhehao Tan, Jian Wang, Peng Wei, Jinjie Gu
| Challenge: | Open-source web agents rely on long tool-call trajectories with cyclic reasoning loops and exploration of unproductive branches. |
| Approach: | They propose a framework that compresses web agent trajectories via graph-based pruning. |
| Outcome: | The proposed framework reduces tool-call rounds by 20% while improving accuracy and efficiency while maintaining the same level of performance as existing models. |
Generate-on-Graph: Treat LLM as both Agent and KG for Incomplete Knowledge Graph Question Answering (2024.emnlp-main)
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Yao Xu, Shizhu He, Jiabei Chen, Zihao Wang, Yangqiu Song, Hanghang Tong, Guang Liu, Jun Zhao, Kang Liu
| Challenge: | Existing methods to integrate LLMs with Knowledge Graphs (KGs) however, these methods are often incomplete to cover all the knowledge required to answer questions. |
| Approach: | They propose to integrate LLMs with Knowledge Graphs (KGs) to address insufficient knowledge and hallucination issues in Large Language Models. |
| Outcome: | The proposed method outperforms existing methods on two datasets. |
Table-R1: Inference-Time Scaling for Table Reasoning Tasks (2025.emnlp-main)
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| Challenge: | In this study, we explore inference-time scaling on table reasoning tasks. |
| Approach: | They propose a large-scale dataset of reasoning traces and a reinforcement learning with verifiable rewards approach to enable inference-time scaling on table reasoning tasks. |
| Outcome: | The proposed model matches or exceeds GPT-4.1 and DeepSeek-R1 models on diverse table reasoning tasks. |
Beyond Completion: A Foundation Model for General Knowledge Graph Reasoning (2025.findings-acl)
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Yin Hua, Zhiqiang Liu, Mingyang Chen, Zheng Fang, Chi Man Wong, Lingxiao Li, Chi Man Vong, Huajun Chen, Wen Zhang
| Challenge: | Existing foundation models for general knowledge graph reasoning have focused on their structural aspects, with most efforts restricted to in-KG tasks. |
| Approach: | They propose a conditional encoding architecture that bridges the gap between textual and structural modalities, enabling seamless integration. |
| Outcome: | The proposed model outperforms baseline models on 28 datasets and is generalized to out-of-KG tasks. |
AutoGraph-R1: End-to-End Reinforcement Learning for Knowledge Graph Construction (2026.acl-long)
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Hong Ting Tsang, Jiaxin Bai, Haoyu Huang, Qiao Xiao, Tianshi Zheng, Baixuan Xu, Shujie Liu, Yangqiu Song
| Challenge: | Currently, knowledge graphs are decoupled from their downstream application, resulting in suboptimal graph structures. |
| Approach: | They propose a framework to directly optimize KG construction for task performance using Reinforcement Learning (RL). |
| Outcome: | The proposed framework improves performance across multiple QA benchmarks and consistently achieves significant performance gains over task-agnostic baseline graphs. |
A Comprehensive Graph Framework for Question Answering with Mode-Seeking Preference Alignment (2025.findings-acl)
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| Challenge: | Existing studies struggle with achieving global understanding of large language models . GraphMPA is a graph-based framework with mode-seeking preference alignment . |
| Approach: | They propose a graph-based framework with mode-seeking preference alignment to improve model outputs. |
| Outcome: | The proposed framework constructs a hierarchical document graph mimicking human cognitive processes for information understanding and synthesis. |
GraphKV: Breaking the Static Selection Paradigm with Graph-Based KV Cache Eviction (2025.emnlp-main)
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| Challenge: | Efficient Key-Value (KV) cache management is essential for processing long text sequences in large language models (LLMs). |
| Approach: | They propose a graph-based framework that redefines token selection for KV cache compression. |
| Outcome: | The proposed framework can be used in existing KV cache eviction methods such as SnapKV and PyramidKV in a plug-and-play manner. |
M3TQA: Massively Multilingual Multitask Table Question Answering (2026.findings-acl)
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Daixin Shu, Jian Yang, Zhenhe Wu, Xianjie Wu, Xianfu Cheng, Guan Xiangyuan, Yanghai Wang, Pengfei Wu, Tingyang Yang, Hualei Zhu, Wei Zhang, Ge Zhang, Jiaheng Liu, Zhoujun Li
| Challenge: | Existing multilingual table benchmarks suffer from geolinguistic imbalance - overrepresenting certain languages and lacking sufficient scale for rigorous cross-lingual analysis. |
| Approach: | They propose a framework for massively multilingual table question answering that includes tables expanded to 97 languages from Chinese and English sources. |
| Outcome: | Experiments on state-of-the-art LLMs show that synthetically generated training data significantly boosts performance, especially for low-resource languages. |
AgentGL: Towards Agentic Graph Learning with LLMs via Reinforcement Learning (2026.acl-long)
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| Challenge: | Existing agentic frameworks treat external information as unstructured text and fail to leverage topological dependencies inherent in real-world data. |
| Approach: | They propose to reframe graph learning as an interleaved process of topology-aware navigation and LLM-based inference. |
| Outcome: | The proposed framework outperforms strong GraphLLMs and GraphRAG benchmarks in multiple LLM backbones. |
Learning to Think on Hypergraph: HyperCoT for Structure-Guided N-ary Knowledge Graph Completion (2026.acl-long)
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| Challenge: | Existing methods to solve knowledge hypergraph link prediction problem are limited by their ability to generate chain-of-thought (CoT) representations. |
| Approach: | They propose a structure-aware approach that models multi-hop structural reasoning as a depth-sensitive progressive evidence accumulation process. |
| Outcome: | Experiments on three real-world datasets show that HyperCoT outperforms strong n-ary KGC baselines while yielding interpretable multi-hop reasoning traces. |
CoverICL: Selective Annotation for In-Context Learning via Active Graph Coverage (2024.emnlp-main)
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Costas Mavromatis, Balasubramaniam Srinivasan, Zhengyuan Shen, Jiani Zhang, Huzefa Rangwala, Christos Faloutsos, George Karypis
| Challenge: | In-context learning (ICL) uses few-shot labeled examples to perform selective annotation. |
| Approach: | They propose an algorithm that incorporates uncertainty sampling into selective annotation for ICL . CoverICL builds a nearest-neighbor graph based on the semantic similarity between candidate ICL examples . |
| Outcome: | The proposed algorithm outperforms existing methods for low-budget active learning (AL) it is up to 2x more budget-efficient than SOTA methods for high-budge AL. |
Decoding on Graphs: Faithful and Sound Reasoning on Knowledge Graphs through Generation of Well-Formed Chains (2025.acl-long)
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| Challenge: | Existing research on the utilization of Knowledge Graphs (KGs) for large language models (LLMs) relies on subgraph retriever or iterative prompting, overlooking the potential synergy of LLMs’ step-wise reasoning capabilities and KGs’ structural nature. |
| Approach: | They propose a graph-aware constrained decoding framework that facilitates a deep synergy between LLMs and KGs by constraint derived from the topology of the KG. |
| Outcome: | The proposed framework can provide faithful and sound reasoning for KGQA. |
Are the Values of LLMs Structurally Aligned with Humans? A Causal Perspective (2025.findings-acl)
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Yipeng Kang, Junqi Wang, Yexin Li, Mengmeng Wang, Wenming Tu, Quansen Wang, Hengli Li, Tingjun Wu, Xue Feng, Fangwei Zhong, Zilong Zheng
| Challenge: | Current approaches to value alignment focus on a few core values, such as helpfulness, harmlessness, and honesty. |
| Approach: | They propose to use latent causal value graphs to guide two lightweight value-steering methods . role-based prompting and sparse autoencoder (SAE) steering are also used . |
| Outcome: | Experiments on Gemma-2B-IT and Llama3-8B- IT show that the proposed methods are effective and controllable. |
Context-Aware Reasoning On Parametric Knowledge for Inferring Causal Variables (2025.findings-emnlp)
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| Challenge: | randomized experiments provide strong inferences, but are often infeasible due to ethical or practical constraints. |
| Approach: | They propose a benchmark where the objective is to complete a partial causal graph. |
| Outcome: | The proposed benchmarks show that they can hypothesize backdoor variables between a cause and its effect. |
Regret-Now: A Physics-Inspired Regret Framework for Temporal Knowledge Graph Question Answering with LLMs (2026.findings-acl)
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| Challenge: | Large Language Models have impressive results in general reasoning tasks, but they still exhibit a lack of dynamic error-correction. |
| Approach: | They propose a temporal reasoning framework that uses the principle of minimum potential energy to model the reasoning process as a dynamic trajectory moving toward a more stable state. |
| Outcome: | The proposed framework shows consistent gains over strong baselines on two standard TKGQA benchmarks. |
Extending First-Order Logic for Factual Reasoning over Knowledge Graphs (2026.acl-long)
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| Challenge: | Existing methods for factual reasoning over knowledge graphs lack support for multiple quantifiers and connectives. |
| Approach: | They propose an extended FOL -structure over knowledge graphs that incorporates comparison predicates and counting quantifiers. |
| Outcome: | The proposed method achieves state-of-the-art on Fact-FOLX-KG, while previous methods experience performance drop on claims requiring comparison and counting. |
A Multi-View Media Profiling Suite: Resources, Evaluation, and Analysis (2026.findings-acl)
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Muhammad Arslan Manzoor, Dilshod Azizov, Daniil Orel, Umer Siddique, Zain Muhammad Mujahid, Yufang Hou, Preslav Nakov
| Challenge: | a large-scale label set for media outlets from Media Bias/Fact Check (MBFC) is lacking in the field. |
| Approach: | They propose to use a large-scale label set to analyze outlets' representations . they also propose to evaluate embedding views and fusion strategies . |
| Outcome: | The proposed method achieves state-of-the-art results on ACL-2020 and establishes strong benchmarks on MBFC-2025. |
ClimateCause: Complex and Implicit Causal Structures in Climate Reports (2026.findings-acl)
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| Challenge: | Existing datasets for causal discovery from text lack granularity and abstraction for domains characterized by such complex causality. |
| Approach: | They propose a manually expert-annotated dataset of higher-order causal structures from science-for-policy climate reports. |
| Outcome: | The proposed dataset is highly readable and can be used to quantify readability. |
Semantics-Aware Dual Graph Convolutional Networks for Argument Pair Extraction (2024.lrec-main)
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| Challenge: | Argument pair extraction (APE) aims to extract interactive argument pairs from two separate passages. |
| Approach: | They propose to tackle the lexical and semantic relevance of arguments with a pre-trained Rouge-guided Transformer (ROT) by using a word graph and a gating mechanism to fuse two graphs. |
| Outcome: | The proposed approach achieves state-of-the-art on F1 score and significantly improves on existing best alternative. |
MDBench: A Synthetic Multi-Document Reasoning Benchmark Generated with Knowledge Guidance (2025.findings-acl)
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| Challenge: | Multi-document reasoning is an area of increasing relevance given LLM capabilities in handling longer-context inputs, but few benchmarks exist to rigorously examine model behavior in this setting. |
| Approach: | They propose a new dataset for evaluating LLMs on the task of multi-document reasoning that uses condensed structured seed knowledge to modify it through LLM-assisted edits. |
| Outcome: | The proposed method generates document sets and QA examples on a multi-document reasoning task using a synthetic generation process. |
Sub-Table Rescorer for Table Question Answering (2024.lrec-main)
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| Challenge: | Tabular language models truncate the sequence of a long table due to their input token limits. |
| Approach: | They propose a sub-table rescorer to improve the performance of an inner table retriever-based inference. |
| Outcome: | The proposed sub-table rescorer improves the performance of an ITR-based inference. |
Enhancing Complex Reasoning in Knowledge Graph Question Answering through Query Graph Approximation (2025.findings-acl)
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| Challenge: | Existing knowledge-grounded question answering frameworks lack essential triplets related to the questions . Existing approaches to knowledge-based QA are incomplete in the context of KGs . |
| Approach: | They propose a framework to provide answers to structured queries by leveraging Knowledge Graphs. |
| Outcome: | The proposed framework outperforms existing methods on QA tasks where KGs are incomplete . the framework is based on a set of data from a dataset of QA questions . |
Tailoring Table Retrieval from a Field-aware Hybrid Matching Perspective (2025.emnlp-main)
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| Challenge: | Empirical results show that a hybrid retrieval approach to table retrieval outperforms state-of-the-art benchmarks. |
| Approach: | They propose a table-tailored HYbrid matching rEtriever which addresses table matching needs from a field-aware hybrid perspective. |
| Outcome: | Empirical results show that the proposed rEtriever outperforms state-of-the-art retrieval methods. |
BYOKG-RAG: Multi-Strategy Graph Retrieval for Knowledge Graph Question Answering (2025.emnlp-main)
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Costas Mavromatis, Soji Adeshina, Vassilis N. Ioannidis, Zhen Han, Qi Zhu, Ian Robinson, Bryan Thompson, Huzefa Rangwala, George Karypis
| Challenge: | Existing approaches to knowledge graph question answering (KGQA) rely on Large Language Model (LLM) agents for graph traversal and retrieval. |
| Approach: | They propose a framework that synergizes Large Language Models with specialized graph retrieval tools to enhance KGQA. |
| Outcome: | The proposed framework outperforms the second-best graph retrieval method by 4.5% points while showing better generalization to custom KGs. |
CITE: Benchmarking Heterogeneous Text-Attributed Graph Models (2026.acl-long)
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| Challenge: | Recent advances in large language models and text-aware graph learning have increased interest in reasoning over text-attributed graphs. |
| Approach: | They propose a large-scale heterogeneous text-attributed graph benchmark for catalytic materials that contains over 438K nodes and 1.2M edges . they establish standardized evaluation protocols for node classification and link prediction and conduct ablation studies to assess the impact of graph heterogenity and textual attributes. |
| Outcome: | The proposed benchmarks are compared to existing methods and provide a baseline for the evaluation of four classes of learning paradigms. |
Piece of Table: A Divide-and-Conquer Approach for Selecting Subtables in Table Question Answering (2026.acl-long)
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| Challenge: | Existing approaches to QA tables rely on independent row or column selection, fail to capture cross-row and cross-column dependencies, or attempt global reasoning. |
| Approach: | They propose a divide-and-conquer subtable selection framework that aggregates local evidence without requiring explicit global reasoning. |
| Outcome: | The proposed framework outperforms previous approaches to table QA in the noisy context. |
Using Bibliodata LODification to Create Metadata-Enriched Literary Corpora in Line with FAIR Principles (2024.lrec-main)
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| Challenge: | Literature corpus building is relatively nascent, and standardized procedures for curating literary corpora are not yet developed. |
| Approach: | They propose a workflow for the creation and reuse of literary corpora using a metadata-enriched Polish Novel Corpus from the 19th and 20th centuries. |
| Outcome: | The proposed workflow includes a multi-stage metadata enrichment and verification process and efficient data collection and data sharing according to the FAIR principles and 5- and 7-star data standards. |
Learning to Evolve: A Self-Improving Framework for Multi-Agent Systems via Textual Parameter Graph Optimization (2026.findings-acl)
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| Challenge: | Existing methods for designing and optimizing multi-agent systems are static and do not learn from experience. |
| Approach: | They propose a framework that enables a multi-agent system to learn to evolve . they use "textual gradients" to pinpoint failures and suggest granular modifications . |
| Outcome: | a new framework enables a multi-agent system to learn to evolve . it learns from historical optimization experiences to improve its performance . |
HELIOS: Harmonizing Early Fusion, Late Fusion, and LLM Reasoning for Multi-Granular Table-Text Retrieval (2025.acl-long)
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| Challenge: | Existing methods for table-text retrieval are limited due to the need to bridge structured tables and unstructured passages. |
| Approach: | They propose a table-text retrieval system that combines the strengths of both approaches . they propose bipartite subgraph retrieval and query-relevant node expansion . |
| Outcome: | The proposed method outperforms state-of-the-art models with a 42.6% and 39.9% improvement on the OTT-QA benchmark. |
GAM: Hierarchical Graph-based Agentic Memory for LLM Agents (2026.acl-long)
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Zhaofen Wu, Hanrong Zhang, Fulin Lin, Wujiang Xu, Xinran Xu, Yankai Chen, Henry Peng Zou, Shaowen Chen, Weizhi Zhang, Xue Liu, Philip S. Yu, Hongwei Wang
| Challenge: | Current unified stream-based memory systems facilitate context updates but remain vulnerable to interference from transient noise. |
| Approach: | They propose a hierarchical Graph-based Agentic Memory framework that explicitly decouples memory encoding from consolidation to resolve conflict between rapid context perception and stable knowledge retention. |
| Outcome: | The proposed framework outperforms state-of-the-art benchmarks on LoCoMo and LongDialQA. |
ZoomRAG: Hierarchical Random-walk Zooming across Multi-scale Information Graphs for Fast and Accurate RAG (2026.findings-acl)
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Xianming Hu, Jingyang Chen, Bin Tang, Yihe Liu, Yihong Huang, Hongbo Zhao, Nuoyi Chen, Jie Zhang, Ping Li, Kai Zhang
| Challenge: | retrieval-augmented generation (RAG) is a powerful tool for NLP applications . but it is challenging to encode large knowledge bases as compact offline structures . |
| Approach: | They propose a coarse-to-fine hierarchical graph inference method that uses random walks to retrieve information from a corpus of documents. |
| Outcome: | The proposed method reduces offline indexing costs and accelerates retrieval. |
Investigating Links between Illicit Massage Businesses through Natural Language Processing and Graph Machine Learning (2026.findings-acl)
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| Challenge: | Illicit massage businesses exploit vulnerable individuals through forced sex or labor . identifying key indicators from vast volume of data associated with these businesses poses significant challenge . |
| Approach: | They propose a multi-stream data integration approach focusing on Yelp reviews . they propose bespoke subgraph extraction strategies to detect links between massage businesses . |
| Outcome: | The proposed approach outperforms baseline methods in a multi-stream data integration framework based on consumer reviews on Yelp.com and contextual data from the U.S. Census and business license records. |
MedEinst: Benchmarking the Einstellung Effect in Medical LLMs through Counterfactual Differential Diagnosis (2026.acl-long)
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| Challenge: | Existing medical benchmarks fail to detect the Einstellung Effect in clinical diagnosis . Existing models exhibit the Einstellung effect, relying on statistical shortcuts rather than logical reasoning. |
| Approach: | They propose a counterfactual benchmark that uses statistical shortcuts to diagnose patients . they propose CGME-based system that iteratively refines reasoning paths . |
| Outcome: | The proposed model achieves high baseline accuracy but severe bias trap rates . iteratively refines reasoning paths in an exemplar base and consolidates disease-specific knowledge into illness graphs. |
SocraticKG: Knowledge Graph Construction via QA-Driven Fact Extraction (2026.findings-acl)
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| Challenge: | Existing approaches to construct knowledge graphs struggle with factual coverage and information loss. |
| Approach: | They propose an automated KG construction method that introduces question-answer pairs as a structured intermediate representation to unfold document-level semantics prior to triple extraction. |
| Outcome: | The proposed method achieves superior factual retention while maintaining high structural cohesion even as extracted knowledge volume substantially expands. |
GraphSynth: Resolving the Diversity-Reliability Trade-off with Probabilistic Factor Graphs (2026.acl-long)
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| Challenge: | Large language models are a scaleable solution for the generation of synthetic data . however, the utility of such data is capped by a critical tension between diversity and factual reliability. |
| Approach: | They propose a framework which leverages a probabilistic factor graph modeling the universe of attributes. |
| Outcome: | The proposed framework outperforms state-of-the-art models with a high structural integrity and a boost in performance on downstream tasks. |
Risk-Controlled Event-Driven Cascading Updates for Knowledge Graph Consistency Restoration (2026.findings-acl)
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| Challenge: | Knowledge Graphs (KGs) typically treat updates as independent facts . factual, localized updates can contradict and invalidate previously correct knowledge . |
| Approach: | They propose a model-agnostic framework for cascading KG update identification that leverages conformal prediction to provide reliable uncertainty guarantees over the cascade as a whole. |
| Outcome: | The proposed framework provides reliable uncertainty guarantees over the cascade as a whole . it integrates large language models to enrich event representations with world knowledge. |