How to Ask Better Questions? a Large-Scale Multi-Domain Dataset for Rewriting Ill-Formed Questions
Abstract
We present a large-scale dataset for the task of rewriting an ill-formed natural language question to a well-formed one. Our multi-domain question rewriting (MQR) dataset is constructed from human contributed Stack Exchange question edit histories. The dataset contains 427,719 question pairs which come from 303 domains. We provide human annotations for a subset of the dataset as a quality estimate. When moving from ill-formed to well-formed questions, the question quality improves by an average of 45 points across three aspects. We train sequence-to-sequence neural models on the constructed dataset and obtain an improvement of 13.2% in BLEU-4 over baseline methods built from other data resources. We release the MQR dataset to encourage research on the problem of question rewriting.1
Cite
Text
Chu et al. "How to Ask Better Questions? a Large-Scale Multi-Domain Dataset for Rewriting Ill-Formed Questions." AAAI Conference on Artificial Intelligence, 2020. doi:10.1609/AAAI.V34I05.6258Markdown
[Chu et al. "How to Ask Better Questions? a Large-Scale Multi-Domain Dataset for Rewriting Ill-Formed Questions." AAAI Conference on Artificial Intelligence, 2020.](https://mlanthology.org/aaai/2020/chu2020aaai-ask/) doi:10.1609/AAAI.V34I05.6258BibTeX
@inproceedings{chu2020aaai-ask,
title = {{How to Ask Better Questions? a Large-Scale Multi-Domain Dataset for Rewriting Ill-Formed Questions}},
author = {Chu, Zewei and Chen, Mingda and Chen, Jing and Wang, Miaosen and Gimpel, Kevin and Faruqui, Manaal and Si, Xiance},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2020},
pages = {7586-7593},
doi = {10.1609/AAAI.V34I05.6258},
url = {https://mlanthology.org/aaai/2020/chu2020aaai-ask/}
}