Lightweight and Efficient Neural Natural Language Processing with Quaternion Networks (P19-1)
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| Challenge: | Existing models for natural language processing are heavily parameterized and memory inefficient. |
| Approach: | They propose a series of lightweight and memory efficient neural architectures for NLP tasks . they propose quaternion algebra and hypercomplex spaces for computation . |
| Outcome: | The proposed models enable expressive inter-component interactions and significantly reduce parameter size without loss of performance. |
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| Challenge: | Using a new architecture, alignment pairs are compared, compressed and then propagated to upper layers for enhanced representation learning. |
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Marcos Treviso, Ji-Ung Lee, Tianchu Ji, Betty van Aken, Qingqing Cao, Manuel R. Ciosici, Michael Hassid, Kenneth Heafield, Sara Hooker, Colin Raffel, Pedro H. Martins, André F. T. Martins, Jessica Zosa Forde, Peter Milder, Edwin Simpson, Noam Slonim, Jesse Dodge, Emma Strubell, Niranjan Balasubramanian, Leon Derczynski, Iryna Gurevych, Roy Schwartz
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| Challenge: | Existing studies have shown that RNNs can efficiently generate bounded hierarchical languages with high syntactic fidelity, but their success is not well-understood theoretically. |
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A Survey on Dynamic Neural Networks for Natural Language Processing (2023.findings-eacl)
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| Challenge: | Dynamic neural networks can scale up pretrainable models with sub-linear increases in computation and time. |
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