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1
Stage-wise Fine-tuning for Graph-to-Text Generation ...
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HySPA: Hybrid Span Generation for Scalable Text-to-Graph Extraction ...
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3
InfoSurgeon: Cross-Media Fine-grained Information Consistency Checking for Fake News Detection ...
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4
Learning Shared Semantic Space for Speech-to-Text Translation ...
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Joint Biomedical Entity and Relation Extraction with Knowledge-Enhanced Collective Inference ...
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6
Event-Centric Natural Language Processing ...
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7
VAULT: VAriable Unified Long Text Representation for Machine Reading Comprehension ...
Abstract: Read paper: https://www.aclanthology.org/2021.acl-short.131 Abstract: Existing models on Machine Reading Comprehension (MRC) require complex model architecture for effectively modeling long texts with paragraph representation and classification, thereby making inference computationally inefficient for production use. In this work, we propose VAULT: a light-weight and parallel-efficient paragraph representation for MRC based on contextualized representation from long document input, trained using a new Gaussian distribution-based objective that pays close attention to the partially correct instances that are close to the ground-truth. We validate our VAULT architecture showing experimental results on two benchmark MRC datasets that require long context modeling; one Wikipedia-based (Natural Questions (NQ)) and the other on TechNotes (TechQA). VAULT can achieve comparable performance on NQ with a state-of-the-art (SOTA) complex document modeling approach while being 16 times faster, demonstrating the ...
Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics
URL: https://underline.io/lecture/25746-vault-variable-unified-long-text-representation-for-machine-reading-comprehension
https://dx.doi.org/10.48448/8adt-3533
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8
Fine-grained Information Extraction from Biomedical Literature based on Knowledge-enriched Abstract Meaning Representation ...
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