Adaptação Lexical Automática em Textos Informativos do Português Brasileiro para o Ensino Fundamental

Hartmann, Nathan Siegle; Aluísio, Sandra Maria

doi:10.21814/lm.12.2.323

Cited by 2 publications

(2 citation statements)

References 35 publications

(47 reference statements)

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“…It performs several transformations at the same time: lexical simplification, word and clause reordering, syntactic simplification, etc. The other two systems (Hartmann and Aluísio, 2020 ; Hartmann et al, 2020 ) are lexical simplification systems which are particularly designed to simplify texts for children.…”

Section: Related Workmentioning

confidence: 99%

“…Evaluation datasets for lexical simplification systems only exist for a handful of languages: English (Specia et al, 2012 ; Horn et al, 2014 ; Paetzold and Specia, 2016a ), French (Rolin et al, 2021 ), Portuguese (Hartmann and Aluísio, 2020 ), Spanish (Alarcón, 2021 ), Japanese (Kajiwara and Yamamoto, 2015 ), and Chinese (Qiang et al, 2021 ). The SemEval-2012 dataset (Specia et al, 2012 ) only evaluates one aspect of English lexical simplification systems, the ranking of the substitution candidates.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Lexical simplification benchmarks for English, Portuguese, and Spanish

Štajner¹,

Ferrés²,

Shardlow³

et al. 2022

Front. Artif. Intell.

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Even in highly-developed countries, as many as 15–30% of the population can only understand texts written using a basic vocabulary. Their understanding of everyday texts is limited, which prevents them from taking an active role in society and making informed decisions regarding healthcare, legal representation, or democratic choice. Lexical simplification is a natural language processing task that aims to make text understandable to everyone by replacing complex vocabulary and expressions with simpler ones, while preserving the original meaning. It has attracted considerable attention in the last 20 years, and fully automatic lexical simplification systems have been proposed for various languages. The main obstacle for the progress of the field is the absence of high-quality datasets for building and evaluating lexical simplification systems. In this study, we present a new benchmark dataset for lexical simplification in English, Spanish, and (Brazilian) Portuguese, and provide details about data selection and annotation procedures, to enable compilation of comparable datasets in other languages and domains. As the first multilingual lexical simplification dataset, where instances in all three languages were selected and annotated using comparable procedures, this is the first dataset that offers a direct comparison of lexical simplification systems for three languages. To showcase the usability of the dataset, we adapt two state-of-the-art lexical simplification systems with differing architectures (neural vs. non-neural) to all three languages (English, Spanish, and Brazilian Portuguese) and evaluate their performances on our new dataset. For a fairer comparison, we use several evaluation measures which capture varied aspects of the systems' efficacy, and discuss their strengths and weaknesses. We find that a state-of-the-art neural lexical simplification system outperforms a state-of-the-art non-neural lexical simplification system in all three languages, according to all evaluation measures. More importantly, we find that the state-of-the-art neural lexical simplification systems perform significantly better for English than for Spanish and Portuguese, thus posing a question if such an architecture can be used for successful lexical simplification in other languages, especially the low-resourced ones.

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Section: Related Workmentioning

confidence: 99%