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。

多语言可控变压器模型词汇简化

Multilingual Controllable Transformer-Based Lexical Simplification

The processing of legal texts has been developing as an emerging field in
natural language processing (NLP). Legal texts contain unique jargon and
complex linguistic attributes in vocabulary, semantics, syntax, and morphology.
Therefore, the development of text simplification (TS) methods specific to the
legal domain is of paramount importance for facilitating comprehension of legal
text by ordinary people and providing inputs to high-level models for
mainstream legal NLP applications. While a recent study proposed a rule-based
TS method for legal text, learning-based TS in the legal domain has not been
considered previously. Here we introduce an unsupervised simplification method
for legal texts (USLT). USLT performs domain-specific TS by replacing complex
words and splitting long sentences. To this end, USLT detects complex words in
a sentence, generates candidates via a masked-transformer model, and selects a
candidate for substitution based on a rank score. Afterward, USLT recursively
decomposes long sentences into a hierarchy of shorter core and context
sentences while preserving semantic meaning. We demonstrate that USLT
outperforms state-of-the-art domain-general TS methods in text simplicity while
keeping the semantics intact.

本文介绍了一种针对法律文本的无监督简化方法（USLT），该方法通过检测复杂词汇并利用掩码变换模型生成候选词，选取候选词进行替换，并在保留语义的同时将长句递归地分解为较短的核心句和上下文句，从而优于最先进的通用领域文本简化方法。