Papers with Graph convolutional networks
BAG: Bi-directional Attention Entity Graph Convolutional Network for Multi-hop Reasoning Question Answering (N19-1)
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| Challenge: | Existing datasets for question answering and machine comprehension (MC) are limited to a single paragraph, or even part of it. |
| Approach: | They propose a bi-directional Attention Entity Graph Convolutional Network (BAG) that leverages relationships between nodes in an entity graph and attention information between a query and the entity graph to generate a prediction. |
| Outcome: | Experimental results show that the proposed network achieves state-of-the-art accuracy on the QAngaroo WIKIHOP dataset. |
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. |
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. |
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. |