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From GNNs to sparse transformers: graph-based architectures for multi-hop question answering
Multi-hop Question Answering (MHQA) is a challenging task in NLP which typically involves processing very long sequences of context information. Sparse Transformers [7] have surpassed Graph Neural Networks (GNNs) as the state-of-the-art architecture for MHQA. Noting that the Transformer [4] is a par...
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| Language: | English |
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Department of Computer Science
2024
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