Neural question generation using transformers
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Updated
Apr 5, 2024 - Jupyter Notebook
Neural question generation using transformers
Question generation using state-of-the-art Natural Language Processing algorithms
A Heterogeneous Benchmark for Information Retrieval. Easy to use, evaluate your models across 15+ diverse IR datasets.
Generating multiple choice questions from text using Machine Learning.
A summary of must-read papers for Neural Question Generation (NQG)
An NLP system for generating reading comprehension questions
Intelligent Q&A system(第七届中软杯,智能问答系统)
Text2Text: Crosslingual NLP/G toolkit
This repository contains code and models for the paper: Semantic Graphs for Generating Deep Questions (ACL 2020).
Multilingual/multidomain question generation datasets, models, and python library for question generation.
Easy to use and understand multiple-choice question generation algorithm using T5 Transformers.
This is a smart Quiz Generator that generates a dynamic quiz from any uploaded text/PDF document using NLP. This can be used for self-analysis, question paper generation, and evaluation, thus reducing human effort.
[ACL 2019]: Interconnected Question Generation with Coreference Alignment and Conversation Flow Modeling
[EMNLP 2019] Mixture Content Selection for Diverse Sequence Generation (Question Generation / Abstractive Summarization)
Code & data accompanying the ICLR 2020 paper "Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation"
Codes for NAACL 2021 Paper "Unsupervised Multi-hop Question Answering by Question Generation"
Code & data accompanying the paper "Toward Subgraph Guided Knowledge Graph Question Generation with Graph Neural Networks"
Mimix: A Text Generation Tool and Pretrained Chinese Models
[ICCV 2021 Oral + TPAMI] Just Ask: Learning to Answer Questions from Millions of Narrated Videos
question generation model with KorQuAD dataset
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