Contrastive Document Representation Learning with Graph Attention Networks

• URL: http://arxiv.org/abs/2110.10778v1
• Date: Wed, 20 Oct 2021 21:05:02 GMT
• Title: Contrastive Document Representation Learning with Graph Attention Networks
• Authors: Peng Xu, Xinchi Chen, Xiaofei Ma, Zhiheng Huang, Bing Xiang
• Abstract summary: We propose to use a graph attention network on top of the available pretrained Transformers model to learn document embeddings. In addition, based on our graph document model, we design a simple contrastive learning strategy to pretrain our models on a large amount of unlabeled corpus.
• Score: 18.22722084624321
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent progress in pretrained Transformer-based language models has shown great success in learning contextual representation of text. However, due to the quadratic self-attention complexity, most of the pretrained Transformers models can only handle relatively short text. It is still a challenge when it comes to modeling very long documents. In this work, we propose to use a graph attention network on top of the available pretrained Transformers model to learn document embeddings. This graph attention network allows us to leverage the high-level semantic structure of the document. In addition, based on our graph document model, we design a simple contrastive learning strategy to pretrain our models on a large amount of unlabeled corpus. Empirically, we demonstrate the effectiveness of our approaches in document classification and document retrieval tasks.

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