bidaf

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.

Important paper implementations for Question Answering using PyTorch

2018百度机器阅读理解技术竞赛

Bidirectional Attention Flow for Machine Comprehension implemented in Keras 2

Machine Reading Comprehension in Tensorflow

Using QANet and BiDAF on DuReader datasets

2018百度机器阅读理解竞赛

Question Answering System using BiDAF Model on SQuAD v2.0

Machine Comprehension using Squad and Triviqa Data sets

Multiple Sentences Bi-directional Attention Flow (Multi-BiDAF) network is a model designed to fit the BiDAF model of Seo et al. (2017) for the Multi-RC dataset. This implementation is built on the AllenNLP library.

Implementation of the machine comprehension model in our ACL 2019 paper: Augmenting Neural Networks with First-order Logic.

State of the art of Neural Question Answering using PyTorch.

BI-DIRECTIONAL ATTENTION FLOW FOR MACHINE COMPREHENSION

Bi-Directional Attention Flow (BiDAF) question answering model enhanced by multi-layer convolutional neural network character embeddings.

Answering a query about a given context paragraph using a model based on recurrent neural networks and attention.

tf bidaf and qanet for dureader dataset

Implementation of Bidirectional Attention Flow as illustrated in the paper from Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, Hannaneh Hajishirzi

Question answering on the SQuAD dataset, for NLP class at UNIBO

Usage example for the AllenNLP BiDAF pre-trained model

CS224N, Stanford, Winter 2018

Implementing the Bidirectional Attention Flow model using pytorch

Pytorch实现Bidaf基于原文提取的机器阅读理解算法