@inproceedings{ae15ebb5f01f42598a45d937ccbe5013,
title = "Generating Scientific Definitions with Controllable Complexity",
abstract = "Unfamiliar terminology and complex language can present barriers to understanding science. Natural language processing stands to help address these issues by automatically defining unfamiliar terms. We introduce a new task and dataset for defining scientific terms and controlling the complexity of generated definitions as a way of adapting to a specific reader's background knowledge. We test four definition generation methods for this new task, finding that a sequence-to-sequence approach is most successful. We then explore the version of the task in which definitions are generated at a target complexity level. We introduce a novel reranking approach and find in human evaluations that it offers superior fluency while also controlling complexity, compared to several controllable generation baselines.",
author = "Tal August and Katharina Reinecke and Smith, {Noah A.}",
note = "Publisher Copyright: {\textcopyright} 2022 Association for Computational Linguistics.; 60th Annual Meeting of the Association for Computational Linguistics, ACL 2022 ; Conference date: 22-05-2022 Through 27-05-2022",
year = "2022",
doi = "10.18653/v1/2022.acl-long.569",
language = "English (US)",
series = "Proceedings of the Annual Meeting of the Association for Computational Linguistics",
publisher = "Association for Computational Linguistics (ACL)",
pages = "8298--8317",
editor = "Smaranda Muresan and Preslav Nakov and Aline Villavicencio",
booktitle = "ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers)",
address = "United States",
}