Neural Question Generation from Text: A Preliminary Study

Code

• github.com/magic282/NQG
  Officialpytorch★ 0
• github.com/zeaver/multifactor
  pytorch★ 18
• github.com/gouqi666/rast
  pytorch★ 16
• github.com/YuxiXie/Neural-Question-Generation
  pytorch★ 0
• github.com/iambabao/copynet
  tf★ 0
• github.com/neineis/multi-head-attention
  pytorch★ 0

Abstract

Automatic question generation aims to generate questions from a text passage where the generated questions can be answered by certain sub-spans of the given passage. Traditional methods mainly use rigid heuristic rules to transform a sentence into related questions. In this work, we propose to apply the neural encoder-decoder model to generate meaningful and diverse questions from natural language sentences. The encoder reads the input text and the answer position, to produce an answer-aware input representation, which is fed to the decoder to generate an answer focused question. We conduct a preliminary study on neural question generation from text with the SQuAD dataset, and the experiment results show that our method can produce fluent and diverse questions.

Tasks

Benchmark Results

Reproductions