# Learning to Generate Questions with Adaptive Copying Neural Networks

arXiv: https://arxiv.org/abs/1909.08187 · HTML: https://mlsanity.com/paper/1909.08187/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2019.

## Built on by (most cited first, 1 of 1)

- [Obj2Sub: Unsupervised Conversion of Objective to Subjective Questions](https://mlsanity.com/paper/2206.11848/) (2022)

## Abstract (arXiv)

Automatic question generation is an important problem in natural language processing. In this paper we propose a novel adaptive copying recurrent neural network model to tackle the problem of question generation from sentences and paragraphs. The proposed model adds a copying mechanism component onto a bidirectional LSTM architecture to generate more suitable questions adaptively from the input data. Our experimental results show the proposed model can outperform the state-of-the-art question generation methods in terms of BLEU and ROUGE evaluation scores.