GCN

Layerwise Relevance Visualization in Convolutional Text Graph Classifiers (D19-53)

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.

Copied to clipboard

Robert Schwarzenberg, Marc Hübner, David Harbecke, Christoph Alt, Leonhard Hennig

Jointly Learning Aspect-Focused and Inter-Aspect Relations with Graph Convolutional Networks for Aspect Sentiment Analysis (2020.coling-main)

Challenge:	Existing explainability methods do not focus on intermediate states in hidden layers of Deep Neural Networks (DNNs).
Approach:	They propose a method that visits visible and hidden layers of a deep neural network and projects them onto the interpretable domain.
Outcome:	The proposed method yields meaningful layerwise explanations for a GCN sentence classifier.

Copied to clipboard

Bin Liang, Rongdi Yin, Lin Gui, Jiachen Du, Ruifeng Xu

Jointly Learning Entity and Relation Representations for Entity Alignment (D19-1)

Challenge:	Existing methods for aspect sentiment analysis do not include explicit sentiment expressions.
Approach:	They propose to construct a heterogeneous graph by leveraging aspect-focused and inter-aspect contextual dependencies for the specific aspect.
Outcome:	The proposed model outperforms state-of-the-art methods on four benchmark datasets and significantly boosts performance in comparison with BERT.

Copied to clipboard

Yuting Wu, Xiao Liu, Yansong Feng, Zheng Wang, Dongyan Zhao

Self-Attention Graph Residual Convolutional Networks for Event Detection with dependency relations (2021.findings-emnlp)

Challenge:	Entity alignment is a viable method for integrating heterogeneous knowledge among different knowledge graphs (KGs).
Approach:	They propose a Graph Convolutional Network-based framework for learning relation representations by embedding relation seeds into entities and incorporating relation approximation into entities to iteratively improve alignment.
Outcome:	The proposed approach outperforms state-of-the-art methods on three real-world cross-lingual datasets.

Copied to clipboard

Anan Liu, Ning Xu, Haozhe Liu

Relation Classification with Entity Type Restriction (2021.findings-acl)

Challenge:	Existing methods to classify events using syntactic dependency relations have not been developed.
Approach:	They propose a model which combines syntactic dependency relations with attention-based dynamic tensors to mine node-to-node latent dependency relations via self-attention mechanism.
Outcome:	The proposed model improves on the ACE2005 dataset and compares with baseline models.

Copied to clipboard

Shengfei Lyu, Huanhuan Chen

Graph Based Network with Contextualized Representations of Turns in Dialogue (2021.emnlp-main)

Challenge:	Existing methods regard all relations as candidate relations for the two entities, which leads to inappropriate relations being candidate relations.
Approach:	They propose a paradigm which exploits entity types to restrict candidate relations by mutual restrictions.
Outcome:	The proposed paradigm improves GCN and SpanBERT on a standard dataset by 6.9 and 4.4 F1 points.

Copied to clipboard

Bongseok Lee, Yong Suk Choi

Better Combine Them Together! Integrating Syntactic Constituency and Dependency Representations for Semantic Role Labeling (2021.findings-acl)

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.

Copied to clipboard

Hao Fei, Shengqiong Wu, Yafeng Ren, Fei Li, Donghong Ji

A structure-enhanced graph convolutional network for sentiment analysis (2020.findings-emnlp)

Challenge:	Existing studies use only one singleton syntax for semantic role labeling (SRL).
Approach:	They propose a TreeLSTM-based integration that integrates phrasal boundaries and semantic relations from dependency into a labelaware GCN solution for simultaneously modeling syntactic edges and labels.
Outcome:	The proposed system achieves state-of-the-art performance on span-based and dependency-based SRL.

Copied to clipboard

Fanyu Meng, Junlan Feng, Danping Yin, Si Chen, Min Hu

Syntactic Text Processing

Challenge:	Recent work on sentiment analysis and aspect-based sentiment analysis does not exploit syntactic information from dependency parsing.
Approach:	They propose a weighted graph convolutional network which exploits syntactic information . they use BERT instead of Bi-LSTM to generate contextualized representations as inputs .
Outcome:	The proposed model can exploit rich syntactic information based on feature combination . it can improve on four ABSA tasks out of six and two SA tasks out .

Aspect-Category based Sentiment Analysis with Hierarchical Graph Convolutional Network (2020.coling-main)

Copied to clipboard

Hongjie Cai, Yaofeng Tu, Xiangsheng Zhou, Jianfei Yu, Rui Xia

LogiGraph: Logical Reasoning with Contrastive Learning and Lightweight Graph Networks (2025.coling-main)

Challenge:	Aspect-based sentiment analysis studies focus on identifying sentiment polarities toward explicit aspects but ignore implicit aspects in text.
Approach:	They propose a hierarchy-sentiment hierarchy prediction problem to capture explicit and implicit aspects of aspect-based sentiment analysis.
Outcome:	The proposed model can consistently achieve the best results on four benchmarks.

Copied to clipboard

Xiang Li, Chen Shi, Yong Xu, Jun Huang

Cross-lingual Text Classification with Heterogeneous Graph Neural Network (2021.acl-short)

Challenge:	Existing methods emphasize contextual semantics while others pay more attention to explicit logical features. Existing models utilize graph convolutional networks (GCN) for node updates, still exhibiting some shortcomings.
Approach:	They propose a logical reasoning method with contrastive learning and lightweight graph networks (LogiGraph) they employ conjunction and punctuation marks as two types of edges to construct a dual graph.
Outcome:	The proposed method improves the GCN and employs conjunction and punctuation marks as two types of edges to construct a dual graph.

Copied to clipboard

Ziyun Wang, Xuan Liu, Peiji Yang, Shixing Liu, Zhisheng Wang

Multilinguality

Challenge:	Existing methods for cross-lingual text classification only consider factors beyond semantic similarity, causing performance degradation between some language pairs.
Approach:	They propose a method to incorporate heterogeneous information within and across languages for cross-lingual text classification using graph convolutional networks.
Outcome:	The proposed method significantly outperforms state-of-the-art models on all tasks and achieves consistent performance gain over baselines in low-resource settings.

Trading Syntax Trees for Wordpieces: Target-oriented Opinion Words Extraction with Wordpieces and Aspect Enhancement (2023.acl-short)

Copied to clipboard

Samuel Mensah, Kai Sun, Nikolaos Aletras

Syntactic Text Processing

Challenge:	State-of-the-art target-oriented opinion word extraction methods use pre-trained BERT wordpieces and graph convolutional networks (GCNs) to encode inputs.
Approach:	They trade syntax trees for BERT wordpieces to eliminate the GCN component from the methods’ architectures to improve TOWE performance.
Outcome:	The proposed approach achieves state-of-the-art results on benchmark datasets and should serve as a strong baseline for further research.

Dynamic Semantic Graph Construction and Reasoning for Explainable Multi-hop Science Question Answering (2021.findings-acl)

Copied to clipboard

Weiwen Xu, Huihui Zhang, Deng Cai, Wai Lam

AMUSED: A Multi-Stream Vector Representation Method for Use in Natural Dialogue (2020.lrec-1)

Challenge:	Existing approaches suffer from low confidence when retrieving evidence facts to fill the knowledge gap and lack transparent reasoning process.
Approach:	They propose a framework to exploit more valid facts while obtaining explainability for multi-hop question answering at web scale by dynamically constructing a semantic graph and reasoning over it.
Outcome:	The proposed framework surpasses existing approaches while maintaining high explainability on OpenBookQA and ARC-Challenge.

Copied to clipboard

Gaurav Kumar, Rishabh Joshi, Jaspreet Singh, Promod Yenigalla

BertGCN: Transductive Text Classification by Combining GNN and BERT (2021.findings-acl)

Challenge:	Current architectures only take care of semantic and contextual information for a given query and fail to fully account for syntactic and external knowledge which are crucial for generating responses in a chit-chat system.
Approach:	They propose a multi-stream deep learning architecture that learns unified embeddings for query-response pairs by incorporating Graph Convolution Networks over their dependency parse.
Outcome:	The proposed architecture improves on the next sentence prediction task and significantly improves existing techniques.

Copied to clipboard

Yuxiao Lin, Yuxian Meng, Xiaofei Sun, Qinghong Han, Kun Kuang, Jiwei Li, Fei Wu

Information Retrieval

Challenge:	Text classification is a core task in natural language processing (NLP) Graph neural networks (GNNs) serve as an effective approach for transductive learning.
Approach:	They propose a model that combines large scale pretraining and transductive learning for text classification.
Outcome:	The proposed model achieves SOTA performance on a wide range of datasets.

Joint Type Inference on Entities and Relations via Graph Convolutional Networks (P19-1)

Copied to clipboard

Changzhi Sun, Yeyun Gong, Yuanbin Wu, Ming Gong, Daxin Jiang, Man Lan, Shiliang Sun, Nan Duan

GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation Extraction (P19-1)

Challenge:	a novel graph convolutional network (GCN) is proposed for the task of joint entity relation extraction.
Approach:	They propose a graph convolutional network running on an entity-relation bipartite graph . they propose combining two different methods to perform joint entity relation extraction .
Outcome:	The proposed model outperforms existing joint models in entity performance and is competitive with the state-of-the-art in relation performance.

Copied to clipboard

Tsu-Jui Fu, Peng-Hsuan Li, Wei-Yun Ma

A Joint Model for Dropped Pronoun Recovery and Conversational Discourse Parsing in Chinese Conversational Speech (2021.acl-long)

Challenge:	GraphRel is an end-to-end relation extraction model that uses graph convolutional networks to learn named entities and relations.
Approach:	They propose a graph-based relation extraction model which uses graph convolutional networks to jointly learn named entities and relations.
Outcome:	The proposed model outperforms previous models on two public datasets: NYT and WebNLG.

Copied to clipboard

Jingxuan Yang, Kerui Xu, Jun Xu, Si Li, Sheng Gao, Jun Guo, Nianwen Xue, Ji-Rong Wen

Event Schema Induction with Double Graph Autoencoders (2022.naacl-main)

Challenge:	Existing work regards dropped pronoun recovery and conversational discourse parsing as two separate tasks and tackles them separately.
Approach:	They propose a neural model for dropped pronoun recovery and conversational discourse parsing in Chinese conversational speech.
Outcome:	The proposed model outperforms the state-of-the-art models on a new dataset . the proposed model is based on linguistic and semantic information from Chinese conversational speech .

Copied to clipboard

Xiaomeng Jin, Manling Li, Heng Ji

Improving Relation Extraction through Syntax-induced Pre-training with Dependency Masking (2022.findings-acl)

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.

Copied to clipboard

Yuanhe Tian, Yan Song, Fei Xia

Language Models

Challenge:	Existing studies require modifications to existing baseline architectures to leverage syntactic information.
Approach:	They propose to leverage syntactic information to improve relation extraction by training a syntax-induced encoder on auto-parsed data through dependency masking.
Outcome:	The proposed approach outperforms baseline models and achieves state-of-the-art results on two English datasets.

Dating Documents using Graph Convolution Networks (P18-1)

Copied to clipboard

Shikhar Vashishth, Shib Sankar Dasgupta, Swayambhu Nath Ray, Partha Talukdar

RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information (D18-1)

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.

Copied to clipboard

Shikhar Vashishth, Rishabh Joshi, Sai Suman Prayaga, Chiranjib Bhattacharyya, Partha Talukdar

Lightweight, Dynamic Graph Convolutional Networks for AMR-to-Text Generation (2020.emnlp-main)

Challenge:	Distantly-supervised Relation Extraction (RE) methods ignore readily available side information.
Approach:	They propose a distantly-supervised neural relation extraction method which uses additional side information from KBs to train an extractor.
Outcome:	The proposed method improves performance even when limited side information is available.

Copied to clipboard

Yan Zhang, Zhijiang Guo, Zhiyang Teng, Wei Lu, Shay B. Cohen, Zuozhu Liu, Lidong Bing

Semi-supervised User Geolocation via Graph Convolutional Networks (P18-1)

Challenge:	AMR-to-text generation is used to transduce Abstract Meaning Representation structures (AMRs) Graph Convolution Networks (GCNs) are not able to capture non-local information and follow a local (first-order) information aggregation scheme.
Approach:	They propose a dynamic fusion mechanism that captures richer non-local interactions . they propose weight tied convolutions and group graph convolution to reduce memory usage .
Outcome:	The proposed model outperforms state-of-the-art models on two benchmark datasets with significantly fewer parameters while maintaining the model capacity.

Copied to clipboard

Afshin Rahimi, Trevor Cohn, Timothy Baldwin

Responsible & Trustworthy NLP

Challenge:	Social media user geolocation is vital to many applications such as event detection.
Approach:	They propose a multiview geolocation model that uses both text and network context.
Outcome:	The proposed model outperforms baseline models and the state-of-the-art models under minimal supervision.

Leveraging Argumentation Knowledge Graph for Interactive Argument Pair Identification (2021.findings-acl)

Copied to clipboard

Jian Yuan, Zhongyu Wei, Donghua Zhao, Qi Zhang, Changjian Jiang

C3LPGCN:Integrating Contrastive Learning and Cooperative Learning with Prompt into Graph Convolutional Network for Aspect-based Sentiment Analysis (2024.findings-naacl)

Challenge:	Existing researches focus on sentence matching but the interaction of opinions requires reasoning of knowledge, which is beyond textual information.
Approach:	They propose to leverage external knowledge to enhance the identification of interactive argument pairs by analyzing the discussion thread of the target topic in an online forum.
Outcome:	The proposed model achieves state-of-the-art in the benchmark dataset.

Copied to clipboard

Ye He, Shihao Zou, YuzheChen YuzheChen, Xianying Huang

MultiDM-GCN: Aspect-guided Response Generation in Multi-domain Multi-modal Dialogue System using Graph Convolutional Network (2020.findings-emnlp)

Challenge:	Recent studies have shown that graph convolutional networks (GCNs) can model syntactic information but incorrect syntaktic structure may introduce additional noise.
Approach:	They propose a graph convolutional network which integrates Contrastive Learning and Cooperative Learning with Prompt into GCN to alleviate the noise when modeling syntactic information.
Outcome:	The proposed model outperforms state-of-the-art models on three datasets and significantly outperformed existing models.

Copied to clipboard

Mauajama Firdaus, Nidhi Thakur, Asif Ekbal

Relation Extraction with Word Graphs from N-grams (2021.emnlp-main)

Challenge:	Existing research suggests that engaging conversations include visual cues (e.g., a video or images) or audio cue.
Approach:	They propose a multi-modal conversational framework that generates the responses following the different aspects of a product or service to cater to the user's needs.
Outcome:	The proposed framework outperforms baselines for the task-oriented dialogue setup.

Copied to clipboard

Han Qin, Yuanhe Tian, Yan Song

Bridging the Gap: Attending to Discontinuity in Identification of Multiword Expressions (N19-1)

Challenge:	Recent studies for relation extraction (RE) leverage the dependency tree of the input sentence to improve performance.
Approach:	They propose to use a graph convolutional network to build a context graph without dependency parsers.
Outcome:	The proposed approach improves neural RE methods without dependency parsers on English benchmark datasets.

Copied to clipboard

Omid Rohanian, Shiva Taslimipoor, Samaneh Kouchaki, Le An Ha, Ruslan Mitkov

Language Models

Challenge:	Existing approaches to identify discontinuous multiword expressions are limited in dealing with discontinuous occurrences.
Approach:	They propose a method to tag Multiword Expressions using a language-independent deep learning architecture to target discontinuity.
Outcome:	The proposed model outperforms baseline models on a multilingual dataset and scores higher than baseline models.

Label-Specific Dual Graph Neural Network for Multi-Label Text Classification (2021.acl-long)

Copied to clipboard

Qianwen Ma, Chunyuan Yuan, Wei Zhou, Songlin Hu

Dependency-driven Relation Extraction with Attentive Graph Convolutional Networks (2021.acl-long)

Challenge:	Existing studies for multi-label text classification do not explore label-specific semantic components from documents.
Approach:	They propose a label-specific dual graph neural network that incorporates category information to learn label-related components from documents.
Outcome:	The proposed model outperforms state-of-the-art models on three benchmark datasets and achieves better performance with respect to tail labels.

Copied to clipboard

Yuanhe Tian, Guimin Chen, Yan Song, Xiang Wan

Textbook Question Answering with Multi-modal Context Graph Understanding and Self-supervised Open-set Comprehension (P19-1)

Challenge:	Existing studies suffer from noise in dependency trees, which can cause confusions in relation extraction.
Approach:	They propose a dependency-driven approach for relation extraction with attentive graph convolutional networks . they apply an attention mechanism upon graph convolutional networks to different word dependencies .
Outcome:	The proposed approach outperforms previous studies on two English datasets and achieves state-of-the-art performance.

Copied to clipboard

Daesik Kim, Seonhoon Kim, Nojun Kwak

Neural Reranking for Dependency Parsing: An Evaluation (2020.acl-main)

Challenge:	In a decade, question answering (QA) has been one of the most promising achievements in the field of natural language processing (NLP).
Approach:	They propose a new module f-GCN based on graph convolutional networks (GCN) to extract knowledge features from multi-modal contexts in complex input data.
Outcome:	The proposed model outperforms state-of-the-art methods on the textbook question answering task and on the visual features of the dataset.

Copied to clipboard

Bich-Ngoc Do, Ines Rehbein

Syntactic Text Processing

Challenge:	Recent work shows that neural rerankers can improve dependency parsing results over the top k trees produced by a base parser.
Approach:	They propose to use a discriminative reranker to improve dependency parsing results . they propose to incorporate global information into the model to improve parse accuracies .
Outcome:	The proposed model outperforms existing models on English and German and Czech, and is the only one to improve on German and Chinese data.

Modeling Structural Similarities between Documents for Coherence Assessment with Graph Convolutional Networks (2023.acl-long)

Copied to clipboard

Wei Liu, Xiyan Fu, Michael Strube

Event Detection: Gate Diversity and Syntactic Importance Scores for Graph Convolution Neural Networks (2020.emnlp-main)

Challenge:	Existing methods focus on a single document’s coherence patterns, ignoring the underlying correlation between documents.
Approach:	They propose a GCN-based coherence model that captures structural similarities between documents by mining subgraph patterns and a heterogeneous graph for the training corpus.
Outcome:	The proposed model outperforms baseline models on discourse coherence and automated essay scoring tasks.

Copied to clipboard

Viet Dac Lai, Tuan Ngo Nguyen, Thien Huu Nguyen

DynaEval: Unifying Turn and Dialogue Level Evaluation (2021.acl-long)

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.

Copied to clipboard

Chen Zhang, Yiming Chen, Luis Fernando D’Haro, Yan Zhang, Thomas Friedrichs, Grandee Lee, Haizhou Li

Natural Language Interfaces

Challenge:	Existing evaluation metrics focus on the turn-level quality of a dialogue . a unified framework that holistically considers the quality of the entire dialogue is needed .
Approach:	They propose a unified automatic evaluation framework which holistically considers the quality of the entire dialogue.
Outcome:	The proposed framework outperforms the state-of-the-art dialogue coherence model and correlates strongly with human judgements across multiple evaluation aspects at both turn and dialogue level.

Adaptive Graph Convolutional Network for Knowledge Graph Entity Alignment (2022.findings-emnlp)

Copied to clipboard

Renbo Zhu, Xukun Luo, Meng Ma, Ping Wang

Global Context-enhanced Graph Convolutional Networks for Document-level Relation Extraction (2020.coling-main)

Challenge:	Entity alignment (EA) aims to identify equivalent entities from different Knowledge Graphs (KGs) noisy neighbors of entities transfer invalid information, drown out equivalent information, and ultimately reduce the performance of EA.
Approach:	They propose a method to deal with neighbor noises to reduce the performance of EA by capturing the differences and complementarities of multiple KGs.
Outcome:	The proposed framework outperforms the state-of-the-art methods in supervised and unsupervised settings.

Copied to clipboard

Huiwei Zhou, Yibin Xu, Weihong Yao, Zhe Liu, Chengkun Lang, Haibin Jiang

Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks (D19-1)

Challenge:	Existing approaches to document-level relation extraction are difficult to establish direct connections between distant entity pairs.
Approach:	They propose a global context-enhanced Graph Convolutional Network model which captures rich global context information of entities in a document.
Outcome:	The proposed model captures rich global context information of entities in a document.

Copied to clipboard

Chen Zhang, Qiuchi Li, Dawei Song

Multi-hop Graph Convolutional Network with High-order Chebyshev Approximation for Text Reasoning (2021.acl-long)

Challenge:	Existing aspects-based sentiment classification models lack a mechanism to account for relevant syntactical constraints and word dependencies.
Approach:	They propose to build a Graph Convolutional Network over the dependency tree of a sentence to exploit syntactical information and word dependencies.
Outcome:	The proposed model is comparable to state-of-the-art models on three benchmarking collections.

Copied to clipboard

Shuoran Jiang, Qingcai Chen, Xin Liu, Baotian Hu, Lisai Zhang

Aspect-Level Sentiment Analysis Via Convolution over Dependency Tree (D19-1)

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.

Copied to clipboard

Kai Sun, Richong Zhang, Samuel Mensah, Yongyi Mao, Xudong Liu

Bipartite Flat-Graph Network for Nested Named Entity Recognition (2020.acl-main)

Challenge:	Existing methods to identify sentiment polarity of opinion words are cumbersome due to the amount of opinionated material on the internet.
Approach:	They propose a method to identify sentiment polarity of opinion words on a specific aspect of a sentence using neural networks.
Outcome:	The proposed method is the state-of-the-art in aspect-based sentiment classification.

Copied to clipboard

Ying Luo, Hai Zhao

Dependency Graph Enhanced Dual-transformer Structure for Aspect-based Sentiment Classification (2020.acl-main)

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.

Copied to clipboard

Hao Tang, Donghong Ji, Chenliang Li, Qiji Zhou

RELATE: Generating a linguistically inspired Knowledge Graph for fine-grained emotion classification (2022.lrec-1)

Challenge:	Aspect-based sentiment classification is a popular task aimed at identifying the corresponding emotion of a given aspect.
Approach:	They propose a dependency graph enhanced dual-transformer network to support mutual reinforcement between the flat representation learning and graph-based representation learning.
Outcome:	The proposed model outperforms state-of-the-art methods on five datasets with a large margin.

Copied to clipboard

Annika Marie Schoene, Nina Dethlefs, Sophia Ananiadou

An Empirical Study on Leveraging Position Embeddings for Target-oriented Opinion Words Extraction (2021.emnlp-main)

Challenge:	Existing knowledge resources for sentiment analysis (SA) tasks are either large, common-sense knowledge graphs (KGs) that cover a limited amount of polarities/emotions or they are smaller in size (e.g. lexicons) . however, these resources are limited by the low coverage of e.t. and scalability.
Approach:	They propose a new directed KG called ‘RELATE’ which incorporates the benefit of semantics without relying on costly human annotation.
Outcome:	The proposed KG overcomes low coverage of emotions and scalability issues . it is the first KG of its size to cover Ekman’s six basic emotions that are directed towards entities.

Copied to clipboard

Samuel Mensah, Kai Sun, Nikolaos Aletras

Representation Learning

Challenge:	Current methods for extracting opinion words for an aspect in text leverage position embeddings to capture relative position of word to the target.
Approach:	They propose to use pretrained word embeddings to extract opinion words for a given aspect in text.
Outcome:	The proposed methods outperform current methods on a task based on pre-trained word embeddings and position embedders.

BERT4GCN: Using BERT Intermediate Layers to Augment GCN for Aspect-based Sentiment Classification (2021.emnlp-main)

Copied to clipboard

Zeguan Xiao, Jiarun Wu, Qingliang Chen, Congjian Deng

Bidirectional Skeleton-Based Isolated Sign Recognition using Graph Convolutional Networks (2022.lrec-1)

Challenge:	Existing approaches to Aspect-based sentiment classification ignore sequential features of context and lack syntactic knowledge of sentences.
Approach:	They propose a model which integrates sequential grammatical features from context and syntactic knowledge from dependency graphs to augment GCN to better encode dependency graph outputs.
Outcome:	The proposed model outperforms state-of-the-art models when equipped with contextual word embedding from pre-training language models.

Copied to clipboard

Konstantinos M. Dafnis, Evgenia Chroni, Carol Neidle, Dimitri Metaxas

Lˆ2GC:Lorentzian Linear Graph Convolutional Networks for Node Classification (2024.lrec-main)

Challenge:	a new method for computer-based sign recognition from video is proposed . it involves explicit detection of the start and end frames of isolated signs .
Approach:	They propose a skeleton-based method that involves explicit detection of start and end frames of signs . they apply a modified WLASL dataset with corrections to the gloss labeling .
Outcome:	The proposed method outperforms state-of-the-art methods on the modified WLASL dataset . it has a success rate of 77.43% and 94.54% for top-5 .

Copied to clipboard

Qiuyu Liang, Weihua Wang, Feilong Bao, Guanglai Gao

Information Retrieval

Challenge:	Existing linear GCNs perform neural network operations in Euclidean space, which do not capture tree-like hierarchical structure of graphs.
Approach:	They propose a Lorentzian linear GCN framework that maps features into hyperbolic space and performs a feature transformation to capture the underlying tree-like structure of data.
Outcome:	The proposed framework achieves state-of-the-art accuracy on standard citation networks datasets and 81.3% on PubMed datasets.

DrKGC: Dynamic Subgraph Retrieval-Augmented LLMs for Knowledge Graph Completion across General and Biomedical Domains (2025.findings-emnlp)

Copied to clipboard

Yongkang Xiao, Sinian Zhang, Yi Dai, Huixue Zhou, Jue Hou, Jie Ding, Rui Zhang