47 research outputs found
ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations
In order to simplify a sentence, human editors perform multiple rewriting
transformations: they split it into several shorter sentences, paraphrase words
(i.e. replacing complex words or phrases by simpler synonyms), reorder
components, and/or delete information deemed unnecessary. Despite these varied
range of possible text alterations, current models for automatic sentence
simplification are evaluated using datasets that are focused on a single
transformation, such as lexical paraphrasing or splitting. This makes it
impossible to understand the ability of simplification models in more realistic
settings. To alleviate this limitation, this paper introduces ASSET, a new
dataset for assessing sentence simplification in English. ASSET is a
crowdsourced multi-reference corpus where each simplification was produced by
executing several rewriting transformations. Through quantitative and
qualitative experiments, we show that simplifications in ASSET are better at
capturing characteristics of simplicity when compared to other standard
evaluation datasets for the task. Furthermore, we motivate the need for
developing better methods for automatic evaluation using ASSET, since we show
that current popular metrics may not be suitable when multiple simplification
transformations are performed.Comment: Accepted to ACL 2020 (camera-ready version