Lexical Simplification (LS) automatically replaces difficult to read words
for easier alternatives while preserving a sentence's original meaning. LS is a
precursor to Text Simplification with the aim of improving text accessibility
to various target demographics, including children, second language learners,
individuals with reading disabilities or low literacy. Several datasets exist
for LS. These LS datasets specialize on one or two sub-tasks within the LS
pipeline. However, as of this moment, no single LS dataset has been developed
that covers all LS sub-tasks. We present MultiLS, the first LS framework that
allows for the creation of a multi-task LS dataset. We also present MultiLS-PT,
the first dataset to be created using the MultiLS framework. We demonstrate the
potential of MultiLS-PT by carrying out all LS sub-tasks of (1). lexical
complexity prediction (LCP), (2). substitute generation, and (3). substitute
ranking for Portuguese. Model performances are reported, ranging from
transformer-based models to more recent large language models (LLMs).

自动将难以阅读的词替换为更易理解的替代词，以改善文本可访问性；通过创建多任务的 LS 数据集，MultiLS 框架和 MultiLS-PT 数据集的潜力得到展示，并报告了基于转换器和大型语言模型的模型性能。

MultiLS：一个多任务词汇简化框架

MultiLS: A Multi-task Lexical Simplification Framework

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。