Machine Reading Comprehension
115 papers with code • 2 benchmarks • 37 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
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CBT
MCTest
CMRC
DRCD
DREAM
DuoRC
MuTual
Quoref
Holl-E
Greatest papers with code
DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applications
PaddlePaddle/models
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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.
Pre-Training with Whole Word Masking for Chinese BERT
In this technical report, we adapt whole word masking in Chinese text, that masking the whole word instead of masking Chinese characters, which could bring another challenge in Masked Language Model (MLM) pre-training task.
CLUE: A Chinese Language Understanding Evaluation Benchmark
CLUEbenchmark/CLUE
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COLING 2020
The advent of natural language understanding (NLU) benchmarks for English, such as GLUE and SuperGLUE allows new NLU models to be evaluated across a diverse set of tasks.
DuReader_robust: A Chinese Dataset Towards Evaluating Robustness and Generalization of Machine Reading Comprehension in Real-World Applications
Machine reading comprehension (MRC) is a crucial task in natural language processing and has achieved remarkable advancements.
Knowledge Aware Conversation Generation with Explainable Reasoning over Augmented Graphs
PaddlePaddle/Research
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IJCNLP 2019
Two types of knowledge, triples from knowledge graphs and texts from documents, have been studied for knowledge aware open-domain conversation generation, in which graph paths can narrow down vertex candidates for knowledge selection decision, and texts can provide rich information for response generation.
DuReader\_robust: A Chinese Dataset Towards Evaluating Robustness and Generalization of Machine Reading Comprehension in Real-World Applications
Machine reading comprehension (MRC) is a crucial task in natural language processing and has achieved remarkable advancements.
Sogou Machine Reading Comprehension Toolkit
In this paper, we present a Sogou Machine Reading Comprehension (SMRC) toolkit that can be used to provide the fast and efficient development of modern machine comprehension models, including both published models and original prototypes.
KLUE: Korean Language Understanding Evaluation
We introduce Korean Language Understanding Evaluation (KLUE) benchmark.
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.
Ranked #2 on
Named Entity Recognition
on ACE 2005
(using extra training data)
Chinese Named Entity Recognition
Entity Extraction using GAN
+4
A Span-Extraction Dataset for Chinese Machine Reading Comprehension
ymcui/cmrc2018
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IJCNLP 2019
Machine Reading Comprehension (MRC) has become enormously popular recently and has attracted a lot of attention.