  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.
