Bi-directional Attention Flow (BiDAF) network is a multi-stage hierarchical process that represents context at different levels of granularity and uses a bi-directional attention flow mechanism to achieve a query-aware context representation without early summarization.

A Tensorflow implementation of QANet for machine reading comprehension

机器阅读理解 冠军/亚军代码及中文预训练MRC模型

The prime repository for state-of-the-art Multilingual Question Answering research and development.

An implementation of the BERT model and its related downstream tasks based on the PyTorch framework. @月来客栈

Tensorflow Implementation of R-Net

The official implementation of ICLR 2020, "Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question Answering".

A pytorch implementation of Reading Wikipedia to Answer Open-Domain Questions.

A Tensorflow Implementation of R-net: Machine reading comprehension with self matching networks

DAWNBench: An End-to-End Deep Learning Benchmark and Competition

ALBERT model Pretraining and Fine Tuning using TF2.0

R-net in PyTorch, with ELMo

Open R-NET (hy` առնետ 🐁) implementation and detailed analysis: https://git.io/vd8dx

A PyTorch implementation of Mnemonic Reader for the Machine Comprehension task

A PyTorch implemention of Match-LSTM, R-NET and M-Reader for Machine Reading Comprehension

A Pytorch implementation of QANet

R-NET implementation in TensorFlow.

An Unofficial Pytorch Implementation of Multi-Granularity Hierarchical Attention Fusion Networks for Reading Comprehension and Question Answering

TensorFlow Models for the Stanford Question Answering Dataset

Pytorch implementation of "Dynamic Coattention Networks For Question Answering"