Papers with light-weight and parallel-efficient paragraph representation
VAULT: VAriable Unified Long Text Representation for Machine Reading Comprehension (2021.acl-short)
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| Challenge: | Existing models on Machine Reading Comprehension (MRC) require complex model architecture for effectively modeling long texts with paragraph representation and classification, making inference computationally inefficient for production use. |
| Approach: | They propose a novel Gaussian distribution-based paragraph representation for Machine Reading Comprehension (MRC) that is light-weight and parallel-efficient. |
| Outcome: | The proposed model can achieve comparable performance on Wikipedia-based (NQ) and TechNotes (TechQA) with a state-of-the-art (SOTA) complex document modeling approach while being 16 times faster, demonstrating the efficiency of the proposed model. |