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.

Source: Making Neural Machine Reading Comprehension Faster

Libraries

Use these libraries to find Machine Reading Comprehension models and implementations
2 papers
1,264
2 papers
972

Most implemented papers

MS MARCO: A Human Generated MAchine Reading COmprehension Dataset

huseinzol05/malay-dataset 28 Nov 2016

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

kevinduh/san_mrc ACL 2018

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 24 Sep 2018

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 8 May 2017

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 10 Dec 2018

Conversational question answering (CQA) is a novel QA task that requires understanding of dialogue context.

DUMA: Reading Comprehension with Transposition Thinking

pfZhu/duma_code 26 Jan 2020

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.