Papers with Graph Convolutional Networks
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). |
Layerwise Relevance Visualization in Convolutional Text Graph Classifiers (D19-53)
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| 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. |
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. |
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. |
Joint Constrained Learning with Boundary-adjusting for Emotion-Cause Pair Extraction (2023.acl-long)
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| Challenge: | Emotion-Cause Pair Extraction (ECPE) aims to identify the document’s emotion clauses and corresponding cause clauses. |
| Approach: | They propose a constrained learning framework with boundary-adjusting for Emotion-Cause Pair Extraction that summarizes prior rules and forces the model to take them into consideration in optimization. |
| Outcome: | The proposed framework achieves competitive results compared with state-of-the-art methods on unbalanced data and proves robustness on unbalancing data. |
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. |
Joint Learning of Representations for Web-tables, Entities and Types using Graph Convolutional Network (2021.eacl-main)
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| Challenge: | Existing approaches for table annotation with entities and types capture the syntactic structure of tables using graphical models or learn embeddings of table entries without accounting for the complete syntaktic structure. |
| Approach: | They propose a Graph Convolutional Network that captures the complete structure of tables, knowledge graph and the training annotations and jointly learns embeddings for table elements as well as the entities and types. |
| Outcome: | The proposed model significantly outperforms state-of-the-art methods on 5 benchmark datasets while showing promising performance on downstream table-related applications. |
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%. |
Semi-supervised User Geolocation via Graph Convolutional Networks (P18-1)
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| 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. |
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. |
VICTR: Visual Information Captured Text Representation for Text-to-Vision Multimodal Tasks (2020.coling-main)
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| Challenge: | Existing text-to-image generation models focus on generating high resolution images and neglect understanding text descriptions. |
| Approach: | They propose a visual contextual text representation which captures rich visual semantic information of objects from text input. |
| Outcome: | The proposed visual contextual text representation improves on the state-of-the-art models. |
Discourse-Aware Neural Extractive Text Summarization (2020.acl-main)
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| Challenge: | Recent studies have shown that sentence-based extractive models result in redundant or uninformative phrases in the extracted summaries. |
| Approach: | They propose a discourse-aware neural summarization model that extracts sub-sentential discourse units as candidates for extractive selection on a finer granularity. |
| Outcome: | Experiments show that the proposed model outperforms state-of-the-art models on popular summarization benchmarks. |
Global Context-enhanced Graph Convolutional Networks for Document-level Relation Extraction (2020.coling-main)
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| 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. |
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. |
Dependency Graph Enhanced Dual-transformer Structure for Aspect-based Sentiment Classification (2020.acl-main)
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| 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. |
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. |