Papers with graph attention network
Transferability of Syntax-Aware Graph Neural Networks in Zero-Shot Cross-Lingual Semantic Role Labeling (2024.findings-emnlp)
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| Challenge: | Existing studies in cross-lingual semantic role labeling (SRL) lack a comprehensive analysis of their network selection. |
| Approach: | They compare the transferability of graph neural network-based models with universal dependency trees to English and 23 target languages. |
| Outcome: | The proposed models perform better in resource-poor languages than in resource rich ones. |
Graph Attention Network with Memory Fusion for Aspect-level Sentiment Analysis (2020.aacl-main)
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| Challenge: | Recent studies ignored the syntactic relationship between the aspect and its corresponding context words, leading the model to focus on syntaktically unrelated words mistakenly. |
| Approach: | They propose to extend the graph convolutional network by assigning different weights to edges of connected words. |
| Outcome: | The proposed method can improve on five datasets showing that it learns and exploits multiword relations and draws different weights of words to improve performance. |
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. |
Bidirectional Hierarchical Attention Networks based on Document-level Context for Emotion Cause Extraction (2021.findings-emnlp)
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| Challenge: | Emotion cause extraction (ECE) aims to extract the causes behind certain emotion in text. |
| Approach: | They propose a bidirectional hierarchical attention network corresponding to the specified candidate cause clause to capture document-level context in a structured and dynamic manner. |
| Outcome: | The proposed method achieves competitive performances on two public datasets in Chinese and English. |
Syntax-Aware Graph Attention Network for Aspect-Level Sentiment Classification (2020.coling-main)
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| Challenge: | Existing approaches to aspect-level sentiment classification focus on modeling the relationship between aspect words and their contexts with attention, and ignore the use of elaborate knowledge implicit in the context. |
| Approach: | They exploit syntactic awareness to the model by the graph attention network on the dependency tree structure and external pre-training knowledge by BERT language model, which helps to model the interaction between the context and aspect words better. |
| Outcome: | The proposed model can model the interaction between the context and aspect words better by using syntactic awareness and external pre-training knowledge. |
Event Pattern-Instance Graph: A Multi-Round Role Representation Learning Strategy for Document-Level Event Argument Extraction (2025.findings-acl)
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| Challenge: | Existing role-based span selection strategies ignore interrelations between events . authors propose a multi-round role representation learning strategy for document-level event argument extraction . |
| Approach: | They propose a pattern-instance graph to capture role semantics embedded in various associations . they also propose re-inventing the role representations learned from previous analyzed documents . |
| Outcome: | The proposed model captures role semantics embedded in various associations . iteratively updates representations of role nodes and edges to enrich their semantic information . the model improves prediction performance in subsequent rounds of span selection . |
DialogueGAT: A Graph Attention Network for Financial Risk Prediction by Modeling the Dialogues in Earnings Conference Calls (2022.findings-emnlp)
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| Challenge: | Existing models focus on extracting useful semantic information from conference call transcripts but ignore subtle yet important information of dialogue structures. |
| Approach: | They propose a graph attention network called DialogueGAT for financial risk prediction by simultaneously modeling the speakers and their utterances in conference calls. |
| Outcome: | The proposed model outperforms baseline models on a dataset of S&P1500 companies. |
Incorporating Syntax and Semantics in Coreference Resolution with Heterogeneous Graph Attention Network (2021.naacl-main)
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| Challenge: | Existing neural coreference resolution models lack syntactic and semantic information . however, such information has been shown to benefit other tasks. |
| Approach: | They propose a graph-based model that incorporates syntactic and semantic structures of sentences. |
| Outcome: | The proposed model incorporates syntactic and semantic structures of sentences. |
Exploring Large Language Models for Effective Rumor Detection on Social Media (2025.naacl-long)
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| Challenge: | Large-scale contexts hinder LLMs’ reasoning abilities while moderate contexts perform better for LLM. |
| Approach: | They propose a semantic-propagation collaboration-base framework that integrates small language models with LLMs for effective rumor detection. |
| Outcome: | The proposed framework bridges the gap between LLMs and LLM in facing long, structured data and offers a novel solution for rumor detection on social media. |
D2GCLF: Document-to-Graph Classifier for Legal Document Classification (2022.findings-naacl)
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| Challenge: | Existing methods learn latent representations for each document by considering the semantics and themes of the documents. |
| Approach: | They propose a document-to-graph classifier which extracts facts as relations between key participants in a law case and represents a legal document with four relation graphs. |
| Outcome: | The proposed method outperforms the state-of-the-art methods on a real-world legal document dataset. |
Bridging the Code Gap: A Joint Learning Framework across Medical Coding Systems (2024.lrec-main)
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| Challenge: | Existing methods for automating medical coding focus on a single coding system . however, there are still challenges to overcome in coding. |
| Approach: | They propose a joint learning framework for Across Medical coding systems which jointly learns different coding system through multi-task learning. |
| Outcome: | The proposed framework improves the performance of the MIMIC-IV ICD-9 and MIMICIV I CD-10 datasets. |
Relational Graph Attention Network for Aspect-based Sentiment Analysis (2020.acl-main)
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| Challenge: | Aspect-based sentiment analysis aims to determine the sentiment polarity towards a specific aspect in online reviews. |
| Approach: | They propose a relational graph attention network to encode a tree structure for sentiment prediction. |
| Outcome: | The proposed approach improves the performance of the graph attention network (GAT) on the SemEval 2014 and Twitter datasets. |
Multi-modal Concept Alignment Pre-training for Generative Medical Visual Question Answering (2024.findings-acl)
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| Challenge: | Medical Visual Question Answering (Med-VQA) aims to provide accurate answers to questions regarding medical images, a task particularly challenging for open-ended questions. |
| Approach: | They propose a multi-modal concept alignment pre-training approach for generative Med-VQA that leverages a knowledge graph sourced from medical image-caption datasets and the Unified Medical Language System. |
| Outcome: | The proposed approach significantly outperforms existing methods on a set of benchmark datasets and shows high efficiency and knowledge-image alignment capability. |
Contrastive Document Representation Learning with Graph Attention Networks (2021.findings-emnlp)
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| Challenge: | Existing methods for document representation learning are significantly affected by the scarcity of document-level data. |
| Approach: | They propose to use a graph attention network on top of the available pretrained Transformers model to learn document embeddings. |
| Outcome: | Empirically, the proposed approach is effective in document classification and document retrieval tasks. |
Synonym Knowledge Enhanced Reader for Chinese Idiom Reading Comprehension (2020.coling-main)
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| Challenge: | Experimental results show that our model achieves state-of-the-art performance for Chinese idiom comprehension. |
| Approach: | They propose a model that can mitigate the inconsistency between literal and literal meanings by incorporating the synonym knowledge enhanced reader into the model. |
| Outcome: | The proposed model achieves state-of-the-art on a Chinese idiom reading comprehension dataset. |
Towards Context-Aware Code Comment Generation (2020.findings-emnlp)
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| Challenge: | Existing methods for code comments generate comments manually, but they suffer from poor scalability and high maintenance cost due to the expensive overhead of writing comment templates. |
| Approach: | They propose a method to automatically generate code comments at a function level by targeting object-oriented programming languages. |
| Outcome: | The proposed approach outperforms the state-of-the-art methods and is comparable with existing methods. |
Reasoning Over Semantic-Level Graph for Fact Checking (2020.acl-main)
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| Challenge: | Existing methods for fact checking use string concatenation or fusing features of isolated evidence sentences. |
| Approach: | They propose a method suitable for reasoning about the semantic-level structure of evidence . they use graph convolutional network and graph attention network to exploit the structure . |
| Outcome: | The proposed method improves claim verification accuracy and FEVER score on a benchmark dataset. |
A Knowledge-Aware Sequence-to-Tree Network for Math Word Problem Solving (2020.emnlp-main)
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| Challenge: | Existing methods for solving math word problems ignore background common-sense knowledge . a novel knowledge-aware sequence-to-tree (KA-S2T) network incorporates external knowledge and global expression information. |
| Approach: | They propose a knowledge-aware sequence-to-tree network that incorporates external knowledge and global expression information into the problem. |
| Outcome: | The proposed model can achieve better performance than previous models on a Math23K dataset. |
DRTS Parsing with Structure-Aware Encoding and Decoding (2020.acl-main)
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| Challenge: | Discourse representation tree structure (DRTS) parsing is a new semantic parser which ignores structural information. |
| Approach: | They propose a structural-aware model to integrate structural information into the model . they use graph attention network (GAT) to exploit structural information for effective modeling . |
| Outcome: | The proposed model can achieve the best performance on a benchmark dataset. |
FaGANet: An Evidence-Based Fact-Checking Model with Integrated Encoder Leveraging Contextual Information (2024.lrec-main)
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| Challenge: | Existing evidence-based fact-checking efforts are time-consuming and challenging . however, relying on surface patterns of claims makes it difficult to identify subtle connections between claims and evidence. |
| Approach: | They propose a model that leverages sentence-level attention and graph attention network to enhance accuracy and fusing claims and evidence information for accurate identification of even well-disguised data. |
| Outcome: | The proposed model improves accuracy and state-of-the-art in the evidence-based fact-checking task. |
Leveraging AMR Graph Structure for Better Sequence-to-Sequence AMR Parsing (2024.lrec-main)
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| Challenge: | Recent studies on AMR parsing often regard this task as a seq2seq translation problem. |
| Approach: | They propose to translate AMR graphs into AMR token sequences in pre-processing and recover AMR from sequences after decoding. |
| Outcome: | The proposed approach outperforms baseline and achieves 85.5 0.1 and 84.2 0.2 Smatch scores on AMR 2.0 and AMR 3.0. |
Multi-document Summarization through Multi-document Event Relation Graph Reasoning in LLMs: a case study in Framing Bias Mitigation (2025.acl-long)
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| Challenge: | a recent study has focused on detecting media bias in news articles . a multi-document event relation graph is used to generate a neutralized summary . |
| Approach: | They propose to generate a neutralized summary given multiple articles presenting different ideological views. |
| Outcome: | The proposed method mitigates media bias and improves content preservation. |