Papers with paragraph representation

2 papers
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.
Topic-Guided Coherence Modeling for Sentence Ordering by Preserving Global and Local Information (D19-1)

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Challenge: Existing methods for sentence ordering are based on pairwise strategies.
Approach: They propose a topic-guided coherence modeling (TGCM) for sentence ordering that utilizes sentence vectors in a permutation-invariant manner.
Outcome: The proposed model outperforms state-of-the-art models from various perspectives.

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