A Tensorflow implementation of QANet for machine reading comprehension
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Updated
May 30, 2018 - Python
A Tensorflow implementation of QANet for machine reading comprehension
Tensorflow Implementation of R-Net
multi_task_NLP is a utility toolkit enabling NLP developers to easily train and infer a single model for multiple tasks.
ALBERT model Pretraining and Fine Tuning using TF2.0
Mining individual characters in multiparty dialogue
An example for applying FusionNet to Natural Language Inference
A PyTorch implementation of Mnemonic Reader for the Machine Comprehension task
Code for Yuanfudao at SemEval-2018 Task 11: Three-way Attention and Relational Knowledge for Commonsense Machine Comprehension
A PyTorch implemention of Match-LSTM, R-NET and M-Reader for Machine Reading Comprehension
R-NET implementation in TensorFlow.
A question answering dataset for machine comprehension of spoken content
Bidirectional Attention Flow for Machine Comprehension implemented in Keras 2
FlowDelta: Modeling Flow Information Gain in Reasoning for Conversational Machine Comprehension
Code & data accompanying the IJCAI 2020 paper "GraphFlow: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine Comprehension"
ReCO: A Large Scale Chinese Reading Comprehension Dataset on Opinion
Machine Comprehension Train on MSMARCO with S-NET Extraction Modification
My implementation of the FusionNet for machine comprehension
Pytorch implementation of the RaSoR paper "Learning Recurrent Span Representations for Extractive Question Answering" (Lee et al. 2016) and experiments with various neural components
Bi-Directional Attention Flow for Machine Comprehensions
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