Identifying complex words in texts is an important first step in text simplification (TS) systems. In this paper, we investigate the performance of binary comparative Lexical Complexity Prediction (LCP) models applied to a popular benchmark dataset — the CompLex 2.0 dataset used in SemEval-2021 Task 1. With the data from CompLex 2.0, we create a new dataset contain 1,940 sentences referred to as CompLex-BC. Using CompLex-BC, we train multiple models to differentiate which of two target words is more or less complex in the same sentence. A linear SVM model achieved the best performance in our experiments with an F1-score of 0.86.

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 potential 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-ES 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 three models for substitute generation on this dataset, namely mBERT, XLM-R, and BERTimbau. The latter achieved the highest performance across all evaluation metrics.

This paper describes team GMU-WLV submission to the TSAR shared-task on multilingual lexical simplification. The goal of the task is to automatically provide a set of candidate substitutions for complex words in context. The organizers provided participants with ALEXSIS a manually annotated dataset with instances split between a small trial set with a dozen instances in each of the three languages of the competition (English, Portuguese, Spanish) and a test set with over 300 instances in the three aforementioned languages. To cope with the lack of training data, participants had to either use alternative data sources or pre-trained language models. We experimented with monolingual models: BERTimbau, ELECTRA, and RoBERTA-largeBNE. Our best system achieved 1st place out of sixteen systems for Portuguese, 8th out of thirty-three systems for English, and 6th out of twelve systems for Spanish.

We report findings of the TSAR-2022 shared task on multilingual lexical simplification, organized as part of the Workshop on Text Simplification, Accessibility, and Readability TSAR-2022 held in conjunction with EMNLP 2022. The task called the Natural Language Processing research community to contribute with methods to advance the state of the art in multilingual lexical simplification for English, Portuguese, and Spanish. A total of 14 teams submitted the results of their lexical simplification systems for the provided test data. Results of the shared task indicate new benchmarks in Lexical Simplification with English lexical simplification quantitative results noticeably higher than those obtained for Spanish and (Brazilian) Portuguese.

This paper describes team LCP-RIT’s submission to the SemEval-2021 Task 1: Lexical Complexity Prediction (LCP). The task organizers provided participants with an augmented version of CompLex (Shardlow et al., 2020), an English multi-domain dataset in which words in context were annotated with respect to their complexity using a five point Likert scale. Our system uses logistic regression and a wide range of linguistic features (e.g. psycholinguistic features, n-grams, word frequency, POS tags) to predict the complexity of single words in this dataset. We analyze the impact of different linguistic features on the classification performance and we evaluate the results in terms of mean absolute error, mean squared error, Pearson correlation, and Spearman correlation.