A Tensorflow implementation of QANet for machine reading comprehension

😎 A curated list of the Question Answering (QA)

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.

QANet+DuReader中文机器阅读理解

ALBERT model Pretraining and Fine Tuning using TF2.0

Mining individual characters in multiparty dialogue

Survey on Machine Reading Comprehension

A PyTorch implementation of Mnemonic Reader for the Machine Comprehension task

An example for applying FusionNet to Natural Language Inference

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

A question answering dataset for machine comprehension of spoken content

R-NET implementation in TensorFlow.

Bidirectional Attention Flow for Machine Comprehension implemented in Keras 2

ODSQA: OPEN-DOMAIN SPOKEN QUESTION ANSWERING DATASET

A spoken question answering dataset on SQUAD

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

FlowDelta: Modeling Flow Information Gain in Reasoning for Conversational Machine Comprehension

Study for Natural Language Processing & Deep Learning Framework

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

中文维基百科问答语料采集系统

List of papers, datasets and other resources related to Machine Comprehension

Dataset for EMNLP'23 Paper "DocTrack: A Visually-Rich Document Dataset Really Aligned with Human Eye Movement for Machine Reading"

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Bi-Directional Attention Flow for Machine Comprehensions

Machine Comprehension using Squad and Triviqa Data sets