# Neural Generation of Diverse Questions using Answer Focus, Contextual and Linguistic Features

arXiv: https://arxiv.org/abs/1809.02637 · HTML: https://mlsanity.com/paper/1809.02637/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2018.

## Built on by (most cited first, 10 of 10)

- [Question Generation for Reading Comprehension Assessment by Modeling How and What to Ask](https://mlsanity.com/paper/2204.02908/) (2022)
- [Controlling Personality Style in Dialogue with Zero-Shot Prompt-Based Learning](https://mlsanity.com/paper/2302.03848/) (2023)
- [Downstream Transformer Generation of Question-Answer Pairs with Preprocessing and Postprocessing Pipelines](https://mlsanity.com/paper/2205.07387/) (2022)
- [Controllable Generation of Dialogue Acts for Dialogue Systems via Few-Shot Response Generation and Ranking](https://mlsanity.com/paper/2307.14440/) (2023)
- [Survey of Natural Language Processing for Education: Taxonomy, Systematic Review, and Future Trends](https://mlsanity.com/paper/2401.07518/) (2024)
- [Automatic Generation of Multiple-Choice Questions](https://mlsanity.com/paper/2303.14576/) (2023)
- [Are Large Language Models Fit For Guided Reading?](https://mlsanity.com/paper/2305.10645/) (2023)
- [Tag-Set-Sequence Learning for Generating Question-Answer Pairs](https://mlsanity.com/paper/2210.11608/) (2022)
- [Automating Turkish Educational Quiz Generation Using Large Language Models](https://mlsanity.com/paper/2406.03397/) (2024)
- [Exploring Answer Information Methods for Question Generation with Transformers](https://mlsanity.com/paper/2312.03483/) (2023)

## Abstract (arXiv)

Question Generation is the task of automatically creating questions from textual input. In this work we present a new Attentional Encoder--Decoder Recurrent Neural Network model for automatic question generation. Our model incorporates linguistic features and an additional sentence embedding to capture meaning at both sentence and word levels. The linguistic features are designed to capture information related to named entity recognition, word case, and entity coreference resolution. In addition our model uses a copying mechanism and a special answer signal that enables generation of numerous diverse questions on a given sentence. Our model achieves state of the art results of 19.98 Bleu_4 on a benchmark Question Generation dataset, outperforming all previously published results by a significant margin. A human evaluation also shows that these added features improve the quality of the generated questions.