Machine Reading Comprehension
206 papers with code • 4 benchmarks • 43 datasets
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.
Benchmarks
These leaderboards are used to track progress in Machine Reading Comprehension
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Libraries
Use these libraries to find Machine Reading Comprehension models and implementations
LogiQA
MRQA
MCTest
CBT
ReClor
CMRC
DREAM
Belebele
MuTual
C3
Most implemented papers
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
The size of the dataset and the fact that the questions are derived from real user search queries distinguishes MS MARCO from other well-known publicly available datasets for machine reading comprehension and question-answering.
A Unified MRC Framework for Named Entity Recognition
ShannonAI/mrc-for-flat-nested-ner
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ACL 2020
Instead of treating the task of NER as a sequence labeling problem, we propose to formulate it as a machine reading comprehension (MRC) task.
Stochastic Answer Networks for Machine Reading Comprehension
We propose a simple yet robust stochastic answer network (SAN) that simulates multi-step reasoning in machine reading comprehension.
SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering
Microsoft/SDNet
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Conversational question answering (CQA) is a novel QA task that requires understanding of dialogue context.
Multi-task Learning with Sample Re-weighting for Machine Reading Comprehension
xycforgithub/MultiTask-MRC
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NAACL 2019
We propose a multi-task learning framework to learn a joint Machine Reading Comprehension (MRC) model that can be applied to a wide range of MRC tasks in different domains.
Stochastic Answer Networks for SQuAD 2.0
kevinduh/san_mrc
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This paper presents an extension of the Stochastic Answer Network (SAN), one of the state-of-the-art machine reading comprehension models, to be able to judge whether a question is unanswerable or not.
Dice Loss for Data-imbalanced NLP Tasks
ShannonAI/dice_loss_for_NLP
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ACL 2020
Many NLP tasks such as tagging and machine reading comprehension are faced with the severe data imbalance issue: negative examples significantly outnumber positive examples, and the huge number of background examples (or easy-negative examples) overwhelms the training.
KLUE: Korean Language Understanding Evaluation
We introduce Korean Language Understanding Evaluation (KLUE) benchmark.
Reinforced Mnemonic Reader for Machine Reading Comprehension
HKUST-KnowComp/MnemonicReader
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In this paper, we introduce the Reinforced Mnemonic Reader for machine reading comprehension tasks, which enhances previous attentive readers in two aspects.
DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applications
PaddlePaddle/PaddleNLP
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WS 2018
Experiments show that human performance is well above current state-of-the-art baseline systems, leaving plenty of room for the community to make improvements.
