By Tal August, Katharina Reinecke, and Noah A. Smith
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 gen- erated 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 in- troduce a novel reranking approach and find in human evaluations that it offers superior fluency while also controlling complexity, compared to several controllable generation base- lines.
We use two sources for scientific definitions:
For both sources, all terms and definitions are formatted as "What is (are) X?" or "Do you have more information about X" with the answer being the definition of X.
The data is avaliable on the HuggingFace dataset hub.
models/ has training scripts for our models. All training and finetuning was done on a NVIDIA Titan X 12GB GPU. Details on hyperparameter settings are in the paper, though the scripts include the best performing settings we used. You can download our best-performing model (BART finetuned with the support documents) from the HuggingFace Hub.
generation/ includes notebooks for generating and evaluating definitions at different levels of complexity.
If you use our work, please cite our paper
@inproceedings{august-2022-definition-complexity,
title={Generating Scientific Definitions with Controllable Complexity},
author={Tal August, Katharina Reinecke, and Noah A. Smith},
booktitle={ACL},
year={2022}
}