ALEXSIS-PT: A New Resource for Portuguese Lexical Simplification

TL;DR

ALEXSIS-PT is a pioneering multi-candidate dataset for Portuguese lexical simplification, enabling improved model training and cross-lingual research with real newspaper text and multiple substitution options.

Contribution

It introduces the first multi-candidate LS dataset for Brazilian Portuguese, following a new protocol and including newspaper articles, facilitating advanced model development.

Findings

BERTimbau outperformed other models in substitution tasks.

The dataset contains 9,605 candidate substitutions for 387 complex words.

ALEXSIS-PT enables cross-lingual LS research and model evaluation.

Abstract

Lexical simplification (LS) is the task of automatically replacing complex words for easier ones making texts more accessible to various target populations (e.g. individuals with low literacy, individuals with learning disabilities, second language learners). To train and test models, LS systems usually require corpora that feature complex words in context along with their candidate substitutions. To continue improving the performance of LS systems we introduce ALEXSIS-PT, a novel multi-candidate dataset for Brazilian Portuguese LS containing 9,605 candidate substitutions for 387 complex words. ALEXSIS-PT has been compiled following the ALEXSIS protocol for Spanish opening exciting new avenues for cross-lingual models. ALEXSIS-PT is the first LS multi-candidate dataset that contains Brazilian newspaper articles. We evaluated four models for substitute generation on this dataset, namely mDistilBERT, mBERT, XLM-R, and BERTimbau. BERTimbau achieved the highest performance across all evaluation metrics.