Enhancing Sentence Simplification in Portuguese: Leveraging Paraphrases, Context, and Linguistic Features
Arthur Scalercio, María José Bocorny Finatto, Aline Paes · 2024
Automatic text simplification focuses on transforming texts into a more comprehensible version without sacrificing their precision.However, automatic methods usually require (paired) datasets that can be rather scarce in languages other than English.This paper presents a new approach to automatic sentence simplification that leverages paraphrases, context, and linguistic attributes to overcome the absence of paired texts in Portuguese.We frame the simplification problem as a textual style transfer task and learn a style representation using the sentences around the target sentence in the document and its linguistic attributes.Moreover, unlike most unsupervised approaches that require style-labeled training data, we fine-tune strong pre-trained models using sentence-level paraphrases instead of annotated data.Our experiments show that our model achieves remarkable results, surpassing the current stateof-the-art (BART+ACCESS) while competitively matching a Large Language Model.