ALEXSIS-PT: A New Resource for Portuguese Lexical Simplification

Kai North, Marcos Zampieri, Tharindu Ranasinghe

Research output: Contribution to journalConference articlepeer-review


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.

Original languageEnglish
Pages (from-to)6057-6062
Number of pages6
JournalProceedings - International Conference on Computational Linguistics, COLING
Issue number1
Publication statusPublished - 17 Oct 2022
Event29th International Conference on Computational Linguistics, COLING 2022 - Gyeongju, Korea, Republic of
Duration: 12 Oct 202217 Oct 2022

Bibliographical note

Materials published in or after 2016 are licensed on a Creative Commons Attribution 4.0 International License.


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