文章基本信息

标题：Supervised and Unsupervised Neural Approaches to Text Readability
本地全文：下载
作者：Matej Martinc ; Senja Pollak ; Marko Robnik-Šikonja 等
期刊名称：Computational Linguistics
印刷版ISSN：0891-2017
电子版ISSN：1530-9312
出版年度：2021
卷号：47
期号：1
页码：141-179
DOI：10.1162/coli_a_00398
语种：English
出版社：MIT Press
摘要：AbstractWe present a set of novel neural supervised and unsupervised approaches for determining the readability of documents. In the unsupervised setting, we leverage neural language models, whereas in the supervised setting, three different neural classification architectures are tested. We show that the proposed neural unsupervised approach is robust, transferable across languages, and allows adaptation to a specific readability task and data set. By systematic comparison of several neural architectures on a number of benchmark and new labeled readability data sets in two languages, this study also offers a comprehensive analysis of different neural approaches to readability classification. We expose their strengths and weaknesses, compare their performance to current state-of-the-art classification approaches to readability, which in most cases still rely on extensive feature engineering, and propose possibilities for improvements.