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Machine Reading Comprehension

Machine Reading Comprehension is one of the key problems in Natural Language Understanding, where the task is to read and comprehend a given text passage, and then answer questions based on it.

Source: Making Neural Machine Reading Comprehension Faster

Papers

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Showing 301–310 of 555 papers

TitleStatusHype
Machine Reading Comprehension with Enhanced Linguistic Verifiers—0
ChemistryQA: A Complex Question Answering Dataset from Chemistry—0
Coreference Reasoning in Machine Reading ComprehensionCode0
ECONET: Effective Continual Pretraining of Language Models for Event Temporal ReasoningCode1
SG-Net: Syntax Guided Transformer for Language Representation—0
Adaptive Bi-directional Attention: Exploring Multi-Granularity Representations for Machine Reading Comprehension—0
From Bag of Sentences to Document: Distantly Supervised Relation Extraction via Machine Reading ComprehensionCode0
Semantics Altering Modifications for Evaluating Comprehension in Machine ReadingCode0
KgPLM: Knowledge-guided Language Model Pre-training via Generative and Discriminative Learning—0
Reference Knowledgeable Network for Machine Reading ComprehensionCode0
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