Mar 21, 2024 · We propose the Graph Receptive Transformer Encoder (GRTE), which combines graph neural networks (GNNs) with large-scale pre-trained models for text ...
Abstract—By employing attention mechanisms, transformers have made great improvements in nearly all NLP tasks, including text classification.
This repository contains the code for the "Graph Receptive Transformer Encoder for Text Classification" paper published in IEEE Transactions on Signal and ...
Mar 21, 2024 · GRTE retrieves global and contextual information by representing texts as graphs, offering significant performance improvements and computational savings.
Mar 22, 2024 · Happy to introduce GRTE (Graph Receptive Transformer Encoder) for Text Classification, published in IEEE Transactions on Signal and ...
Jul 4, 2024 · When fine-tuning the model for specific downstream tasks, classification heads are added after the encoder according to the task requirements.
Jul 1, 2024 · In this survey, we bring the coverage of methods up to 2023, including corpus-level and document-level graph neural networks.
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Unlike graph neural networks that restrict the information exchange between immediate neighborhood, we propose a new model, known as Graph Transformer, that.
We introduce a novel Transformer based heterogeneous graph neural network, namely Text Graph Transformer (TG-Transformer).
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Aug 14, 2024 · In this paper, we present a comprehensive review of GNNs and graph Transformers in computer vision from a task-oriented perspective.