Exploration of various deep neural networks for Question Answering and Reading Comprehension
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Updated
Jan 22, 2017 - Python
Exploration of various deep neural networks for Question Answering and Reading Comprehension
Implementation of "Teaching Machines to Read and Comprehend"
Simplest end to end memory network implemented in tensorflow
Reading Comprehension Experiments repository.
CU Boulder | 5622-Project
A question answering dataset for machine comprehension of spoken content
AI Competition
Context Awareness-Enabled Summary Attentive Reader (CAESAR)
实现了Attention-over-Attention Neural Networks for Reading Comprehension
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.
QA&RC Experiment implement by tensorflow
Pytorch implementation of "Dynamic Coattention Networks For Question Answering"
Effective Subword Segmentation for Text Comprehension (TASLP 2019)
Subword-augmented Embedding for Cloze Reading Comprehension (COLING 2018)
The source code of ACL 2018 paper "Denoising Distantly Supervised Open-Domain Question Answering".
Machine Reading Comprehension and Question Answering
Tensorflow implementation of QANet.
Code and datasets of "Multilingual Extractive Reading Comprehension by Runtime Machine Translation"
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