Explainable Prediction of Text Complexity: The Missing Preliminaries for Text Simplification
Cristina
Garbacea
author
Mengtian
Guo
author
Samuel
Carton
author
Qiaozhu
Mei
author
2021-08
text
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Chengqing
Zong
editor
Fei
Xia
editor
Wenjie
Li
editor
Roberto
Navigli
editor
Association for Computational Linguistics
Online
conference publication
Text simplification reduces the language complexity of professional content for accessibility purposes. End-to-end neural network models have been widely adopted to directly generate the simplified version of input text, usually functioning as a blackbox. We show that text simplification can be decomposed into a compact pipeline of tasks to ensure the transparency and explainability of the process. The first two steps in this pipeline are often neglected: 1) to predict whether a given piece of text needs to be simplified, and 2) if yes, to identify complex parts of the text. The two tasks can be solved separately using either lexical or deep learning methods, or solved jointly. Simply applying explainable complexity prediction as a preliminary step, the out-of-sample text simplification performance of the state-of-the-art, black-box simplification models can be improved by a large margin.
garbacea-etal-2021-explainable
10.18653/v1/2021.acl-long.88
https://aclanthology.org/2021.acl-long.88
2021-08
1086
1097