Machine Reading Comprehension
154 papers with code • 3 benchmarks • 39 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
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
Libraries
Use these libraries to find Machine Reading Comprehension models and implementations
MCTest
CBT
MRQA
CMRC
DREAM
DRCD
ReClor
CMRC 2018
Quoref
MuTual
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
•
•
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.
Multi-task Learning with Sample Re-weighting for Machine Reading Comprehension
xycforgithub/MultiTask-MRC
•
•
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
•
•
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.
Reinforced Mnemonic Reader for Machine Reading Comprehension
HKUST-KnowComp/MnemonicReader
•
•
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
•
•
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.
SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering
Microsoft/SDNet
•
•
Conversational question answering (CQA) is a novel QA task that requires understanding of dialogue context.
DUMA: Reading Comprehension with Transposition Thinking
pfZhu/duma_code
•
•
Multi-choice Machine Reading Comprehension (MRC) requires model to decide the correct answer from a set of answer options when given a passage and a question.
CLUE: A Chinese Language Understanding Evaluation Benchmark
CLUEbenchmark/CLUE
•
•
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.
