Text is by far the most ubiquitous source of knowledge and information and
should be made easily accessible to as many people as possible; however, texts
often contain complex words that hinder reading comprehension and
accessibility. Therefore, suggesting simpler alternatives for complex words
without compromising meaning would help convey the information to a broader
audience. This paper proposes mTLS, a multilingual controllable
Transformer-based Lexical Simplification (LS) system fined-tuned with the T5
model. The novelty of this work lies in the use of language-specific prefixes,
control tokens, and candidates extracted from pre-trained masked language
models to learn simpler alternatives for complex words. The evaluation results
on three well-known LS datasets -- LexMTurk, BenchLS, and NNSEval -- show that
our model outperforms the previous state-of-the-art models like LSBert and
ConLS. Moreover, further evaluation of our approach on the part of the recent
TSAR-2022 multilingual LS shared-task dataset shows that our model performs
competitively when compared with the participating systems for English LS and
even outperforms the GPT-3 model on several metrics. Moreover, our model
obtains performance gains also for Spanish and Portuguese.

提出了一种基于 Transformer 的多语言可控词汇简化系统，使用语言特定的前缀、控制标记和候选词从预训练的掩蔽语言模型中提取较简单的替代词，该系统在三个公开的词汇简化数据集上优于之前的最先进模型，表现良好。针对最近的 TSAR-2022 多语言 LS 共享任务数据集也展现出很强的性能，且在西班牙语和葡萄牙语中获得性能提升。