Papers with GCNs
Graph-based Deep Learning in Natural Language Processing (D19-2)
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| Challenge: | This tutorial aims to introduce graph-based deep learning techniques such as Graph Convolutional Networks (GCNs) for Natural Language Processing (NLP) |
| Approach: | It provides a brief introduction to graph-based deep learning techniques such as Graph Convolutional Networks (GCNs) for Natural Language Processing (NLP). |
| Outcome: | This tutorial provides a brief introduction to graph-based deep learning techniques such as Graph Convolutional Networks (GCNs) for natural language processing (NLP). |
SHIELD: LLM-Driven Schema Induction for Predictive Analytics in EV Battery Supply Chain Disruptions (2024.emnlp-industry)
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Zhi-Qi Cheng, Yifei Dong, Aike Shi, Wei Liu, Yuzhi Hu, Jason O’Connor, Alexander Hauptmann, Kate Whitefoot
| Challenge: | EV battery supply chain is vulnerable to disruptions caused by natural disasters and geopolitical tensions. |
| Approach: | They propose a system integrating Large Language Models with domain expertise for EV supply chain risk assessment. |
| Outcome: | Evaluated on 12,070 paragraphs from 365 sources (2022-2023), SHIELD outperforms baseline GCNs and LLM+prompt methods in disruption prediction. |
Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks (D18-1)
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| Challenge: | Existing approaches to align multilingual knowledge graphs with counterparts in different languages are not effective. |
| Approach: | They propose a novel approach for cross-lingual KG alignment via graph convolutional networks . they train GCNs to embed entities of each language into a unified vector space . |
| Outcome: | The proposed approach gets the best performance on real multilingual KGs compared with other embedding-based approaches. |
Privacy-Preserving Graph Convolutional Networks for Text Classification (2022.lrec-1)
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| Challenge: | Graph convolutional networks (GCNs) are powerful for representation learning on documents that naturally occur as graphs, but sensitive personal information is prone to privacy leaks. |
| Approach: | They propose a method that adapts differentially-private gradient-based training to GCNs and conduct experiments using two optimizers on five NLP datasets in two languages. |
| Outcome: | The proposed method improves baseline privacy bounds by 2.7 while retaining competitive F1 scores while providing strong privacy guarantees. |
ROPE: Reading Order Equivariant Positional Encoding for Graph-based Document Information Extraction (2021.acl-short)
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Chen-Yu Lee, Chun-Liang Li, Chu Wang, Renshen Wang, Yasuhisa Fujii, Siyang Qin, Ashok Popat, Tomas Pfister
| Challenge: | Graph Convolutional Networks (GCNs) have limited ability to capture reading orders of given word-level node representations in a graph. |
| Approach: | They propose a new positional encoding technique to capture word-level nodes in a graph. |
| Outcome: | The proposed method improves existing GCNs with an 8.4% F1 score on two datasets and a large-scale payment dataset. |
Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks (N18-2)
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| Challenge: | Semantic representations have long been argued as potentially useful for enforcing meaning preservation and improving generalization performance of machine translation methods. |
| Approach: | They propose to integrate semantic representations into neural machine translation by injecting a semantic bias into sentence encoders and achieving improvements in BLEU scores. |
| Outcome: | The proposed representations achieve better BLEU scores over the linguistic-agnostic and syntax-aware versions on the English–German language pair. |
Trading Syntax Trees for Wordpieces: Target-oriented Opinion Words Extraction with Wordpieces and Aspect Enhancement (2023.acl-short)
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| 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. |
Enhancing Drug-Drug Interaction Extraction from Texts by Molecular Structure Information (P18-2)
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| Challenge: | Graph Convolutional Networks (GCNs) can extract drug-drug interactions (DDIs) from texts using external drug molecular structure information. |
| Approach: | They propose a novel neural method to extract drug-drug interactions (DDIs) from texts using external drug molecular structure information. |
| Outcome: | The proposed model can extract drug-drug interactions (DDIs) from texts with high accuracy and the molecular information can enhance text-based extraction by 2.39 percent points in the F-score on the DDIExtraction 2013 shared task data set. |
GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation Extraction (P19-1)
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| 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. |
Lightweight, Dynamic Graph Convolutional Networks for AMR-to-Text Generation (2020.emnlp-main)
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| 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. |
Glocal: Incorporating Global Information in Local Convolution for Keyphrase Extraction (N19-1)
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| Challenge: | Graph Convolutional Networks (GCNs) model nodes’ local pairwise importance but lack the capability to model global relative importance in tasks where global ranking is a key component for the task. |
| Approach: | They propose to incorporate global relative importance information into the GCN family of models by using scaled node weights. |
| Outcome: | The proposed method improves keyphrase extraction by 2% and improves the baseline by 5%. |
C3LPGCN:Integrating Contrastive Learning and Cooperative Learning with Prompt into Graph Convolutional Network for Aspect-based Sentiment Analysis (2024.findings-naacl)
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| 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. |
Abusive Language Detection with Graph Convolutional Networks (N19-1)
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| Challenge: | Existing approaches to abusive language detection only capture shallow properties of online communities . a new approach captures both the structure of online community and linguistic behavior of users . |
| Approach: | They propose a graph convolutional network approach that captures the linguistic behavior of users . they propose to model homophily by embeddings for authors that encode the structure of their communities . |
| Outcome: | The proposed approach captures both the structure and linguistic behavior of users in online communities . authors show that the proposed approach significantly advances the current state of the art . |
Question Answering by Reasoning Across Documents with Graph Convolutional Networks (N19-1)
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| Challenge: | Recent research in reading comprehension has focused on answering questions based on individual documents or even single paragraphs. |
| Approach: | They propose a neural model which integrates and reasons relying on information spread within documents and across multiple documents. |
| Outcome: | The proposed model achieves state-of-the-art on a multi-document question answering dataset, WikiHop. |
Better Feature Integration for Named Entity Recognition (2021.naacl-main)
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| Challenge: | Existing approaches to named entity recognition (NER) focus on stacking the LSTM and graph neural networks (GCNs) however, the exact interaction mechanism between the two types of features is not clear and the performance gain is not significant. |
| Approach: | They propose a model that incorporates both types of features with a Synergized-LSTM which captures how the two types of feature interact. |
| Outcome: | The proposed model achieves better performance than previous approaches while requiring fewer parameters. |
Graph Convolutions over Constituent Trees for Syntax-Aware Semantic Role Labeling (2020.emnlp-main)
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| Challenge: | Semantic role labeling (SRL) is the task of identifying predicates and labeling argument spans with semantic roles. |
| Approach: | They propose to use graph convolutional networks to encode constituents and inform an SRL system by combining word representations of the first and last words in a constituent tree. |
| Outcome: | The proposed model is compared with other models and shows that it is more efficient than dependency trees. |
Inductive Topic Variational Graph Auto-Encoder for Text Classification (2021.naacl-main)
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| Challenge: | Existing methods for text classification do not assume explicit latent semantic structure of documents, making them less effective and difficult to interpret. |
| Approach: | They propose a model that integrates a topic model into variational graph-auto-encoder to capture hidden semantic information between documents and words. |
| Outcome: | The proposed model outperforms existing models on supervised and semi-supervised text classification and unsupervised representation learning. |
Neural Reranking for Dependency Parsing: An Evaluation (2020.acl-main)
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| 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. |
Aligning Cross-Lingual Entities with Multi-Aspect Information (D19-1)
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| Challenge: | Existing knowledge graphs that represent entities in different languages are not covered by existing systems. |
| Approach: | They propose two ways to embed entities from multilingual knowledge graphs into the same vector space, where equivalent entities are close to each other. |
| Outcome: | The proposed method significantly outperforms existing systems on two benchmark datasets. |
GraphBTM: Graph Enhanced Autoencoded Variational Inference for Biterm Topic Model (D18-1)
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| Challenge: | Conventional topic models suffer different problems in different settings. |
| Approach: | They propose a novel way to model word-pairs named biterms in the whole corpus and a Graph Convolutional Networks (GCNs) with residual connections to extract transitive features from biterm. |
| Outcome: | The proposed model generates more coherent topics compared with previous methods. |
A Structure-Aware Generative Adversarial Network for Bilingual Lexicon Induction (2023.findings-emnlp)
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| Challenge: | Bilingual lexicon induction (BLI) is the task of inducing word translations with a learned mapping function that aligns monolingual word embedding spaces in two different languages. |
| Approach: | They propose a model that explicitly captures multiple topological structure information to achieve accurate bilingual lexicon induction. |
| Outcome: | The proposed model captures multiple topological structure information to achieve accurate BLI on a public dataset. |
Bidirectional Skeleton-Based Isolated Sign Recognition using Graph Convolutional Networks (2022.lrec-1)
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| 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 . |
Lˆ2GC:Lorentzian Linear Graph Convolutional Networks for Node Classification (2024.lrec-main)
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| 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. |
The Efficiency vs. Accuracy Trade-off: Optimizing RAG-Enhanced LLM Recommender Systems Using Multi-Head Early Exit (2025.acl-long)
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Huixue Zhou, Hengrui Gu, Zaifu Zhan, Xi Liu, Kaixiong Zhou, Yongkang Xiao, Mingfu Liang, Srinivas Prasad Govindan, Piyush Chawla, Jiyan Yang, Xiangfei Meng, Huayu Li, Buyun Zhang, Liang Luo, Wen-Yen Chen, Yiping Han, Bo Long, Rui Zhang, Tianlong Chen
| Challenge: | Existing frameworks for Large Language Models (LLMs) for Click-Through Rate prediction require a careful balance between computational efficiency and predictive accuracy. |
| Approach: | They propose a framework that integrates Retrieval-Augmented Generation with a novel multi-head early exit architecture to address both challenges. |
| Outcome: | The proposed framework reduces retrieval time while maintaining high model performance. |