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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 71–80 of 555 papers

Title	Date	Tasks	Status	Hype
TREC CAsT 2019: The Conversational Assistance Track Overview	Mar 30, 2020	Conversational SearchLearning-To-Rank	CodeCode Available	1
ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning	Feb 11, 2020	Logical ReasoningLogical Reasoning Question Answering	CodeCode Available	1
Retrospective Reader for Machine Reading Comprehension	Jan 27, 2020	Machine Reading ComprehensionQuestion Answering	CodeCode Available	1
Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpus	Jan 27, 2020	Answer GenerationChatbot	CodeCode Available	1
DUMA: Reading Comprehension with Transposition Thinking	Jan 26, 2020	Language ModelingLanguage Modelling	CodeCode Available	1
Coreference Resolution as Query-based Span Prediction	Nov 5, 2019	coreference-resolutionCoreference Resolution	CodeCode Available	1
A Unified MRC Framework for Named Entity Recognition	Oct 25, 2019	Chinese Named Entity RecognitionEntity Extraction using GAN	CodeCode Available	1
Interactive Language Learning by Question Answering	Aug 28, 2019	Deep Reinforcement LearningMachine Reading Comprehension	CodeCode Available	1
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset	Nov 28, 2016	BenchmarkingMachine Reading Comprehension	CodeCode Available	1
Interpretable Traces, Unexpected Outcomes: Investigating the Disconnect in Trace-Based Knowledge Distillation	May 20, 2025	Information RetrievalKnowledge Distillation	—Unverified	0

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