Abstract
Typical Lexical Simplification systems replace single words with simpler alternatives. We introduce the task of Phrase-Level Simplification, a variant of Lexical Simplification where sequences of words are replaced as a whole, allowing for the substitution of compositional expressions. We tackle this task with a novel pipeline approach by generating candidate replacements with lexicon-retrofitted POS-aware phrase embedding models, selecting them through an unsupervised comparison-based method, then ranking them with rankers trained with features that capture phrase simplicity more effectively than other popularly used feature sets. We train and evaluate this approach using BenchPS, a new dataset we created for the task that focuses on annotations on the needs of non-native English speakers. Our methods and resources result in a state-of-the-art phrase simplifier that correctly simplifies complex phrases 61% of the time.
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Paetzold, G.H., Specia, L. (2023). Phrase-Level Simplification for Non-native Speakers. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2019. Lecture Notes in Computer Science, vol 13451. Springer, Cham. https://doi.org/10.1007/978-3-031-24337-0_30
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