FewShotTextGCN: K-hop neighborhood regularization for few-shot learning on graphs

• URL: http://arxiv.org/abs/2301.10481v1
• Date: Wed, 25 Jan 2023 09:30:32 GMT
• Title: FewShotTextGCN: K-hop neighborhood regularization for few-shot learning on graphs
• Authors: Niels van der Heijden, Ekaterina Shutova and Helen Yannakoudakis
• Abstract summary: FewShotTextGCN is designed to effectively utilize the properties of word-document graphs for improved learning in low-resource settings. We introduce K-hop Neighbourhood Regularization, a regularizer for heterogeneous graphs, and show that it stabilizes and improves learning when only a few training samples are available.
• Score: 25.88292419660444
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present FewShotTextGCN, a novel method designed to effectively utilize the properties of word-document graphs for improved learning in low-resource settings. We introduce K-hop Neighbourhood Regularization, a regularizer for heterogeneous graphs, and show that it stabilizes and improves learning when only a few training samples are available. We furthermore propose a simplification in the graph-construction method, which results in a graph that is $\sim$7 times less dense and yields better performance in little-resource settings while performing on par with the state of the art in high-resource settings. Finally, we introduce a new variant of Adaptive Pseudo-Labeling tailored for word-document graphs. When using as little as 20 samples for training, we outperform a strong TextGCN baseline with 17% in absolute accuracy on average over eight languages. We demonstrate that our method can be applied to document classification without any language model pretraining on a wide range of typologically diverse languages while performing on par with large pretrained language models.

Related papers

• Pre-Training and Prompting for Few-Shot Node Classification on Text-Attributed Graphs [35.44563283531432]
  Text-attributed graph (TAG) is one kind of important real-world graph-structured data with each node associated with raw texts. For TAGs, traditional few-shot node classification methods directly conduct training on the pre-processed node features. We propose P2TAG, a framework designed for few-shot node classification on TAGs with graph pre-training and prompting.
  arXiv  Detail & Related papers  (2024-07-22T07:24:21Z)
• Parameter-Efficient Tuning Large Language Models for Graph Representation Learning [62.26278815157628]
  We introduce Graph-aware. Efficient Fine-Tuning - GPEFT, a novel approach for efficient graph representation learning. We use a graph neural network (GNN) to encode structural information from neighboring nodes into a graph prompt. We validate our approach through comprehensive experiments conducted on 8 different text-rich graphs, observing an average improvement of 2% in hit@1 and Mean Reciprocal Rank (MRR) in link prediction evaluations.
  arXiv  Detail & Related papers  (2024-04-28T18:36:59Z)
• Language Models for Text Classification: Is In-Context Learning Enough? [54.869097980761595]
  Recent foundational language models have shown state-of-the-art performance in many NLP tasks in zero- and few-shot settings. An advantage of these models over more standard approaches is the ability to understand instructions written in natural language (prompts) This makes them suitable for addressing text classification problems for domains with limited amounts of annotated instances.
  arXiv  Detail & Related papers  (2024-03-26T12:47:39Z)
• Connecting the Dots: What Graph-Based Text Representations Work Best for Text Classification Using Graph Neural Networks? [25.898812694174772]
  This work extensively investigates graph representation methods for text classification. We compare different graph construction schemes using a variety of GNN architectures and setups. Two Transformer-based large language models are also included to complement the study.
  arXiv  Detail & Related papers  (2023-05-23T23:31:24Z)
• To Augment or Not to Augment? A Comparative Study on Text Augmentation Techniques for Low-Resource NLP [0.0]
  We investigate three categories of text augmentation methodologies which perform changes on the syntax. We compare them on part-of-speech tagging, dependency parsing and semantic role labeling for a diverse set of language families. Our results suggest that the augmentation techniques can further improve over strong baselines based on mBERT.
  arXiv  Detail & Related papers  (2021-11-18T10:52:48Z)
• Hierarchical Heterogeneous Graph Representation Learning for Short Text Classification [60.233529926965836]
  We propose a new method called SHINE, which is based on graph neural network (GNN) for short text classification. First, we model the short text dataset as a hierarchical heterogeneous graph consisting of word-level component graphs. Then, we dynamically learn a short document graph that facilitates effective label propagation among similar short texts.
  arXiv  Detail & Related papers  (2021-10-30T05:33:05Z)
• Promoting Graph Awareness in Linearized Graph-to-Text Generation [72.83863719868364]
  We study the ability of linearized models to encode local graph structures. Our findings motivate solutions to enrich the quality of models' implicit graph encodings. We find that these denoising scaffolds lead to substantial improvements in downstream generation in low-resource settings.
  arXiv  Detail & Related papers  (2020-12-31T18:17:57Z)
• Neural Language Modeling for Contextualized Temporal Graph Generation [49.21890450444187]
  This paper presents the first study on using large-scale pre-trained language models for automated generation of an event-level temporal graph for a document.
  arXiv  Detail & Related papers  (2020-10-20T07:08:00Z)
• Investigating Pretrained Language Models for Graph-to-Text Generation [55.55151069694146]
  Graph-to-text generation aims to generate fluent texts from graph-based data. We present a study across three graph domains: meaning representations, Wikipedia knowledge graphs (KGs) and scientific KGs. We show that the PLMs BART and T5 achieve new state-of-the-art results and that task-adaptive pretraining strategies improve their performance even further.
  arXiv  Detail & Related papers  (2020-07-16T16:05:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.