基于Transformer的多语言可控词汇简化
Multilingual Controllable Transformer-Based Lexical Simplification
AI总结：
本文提出基于T5微调的多语言可控词汇简化系统mTLS，利用语言特定前缀、控制标记和掩码语言模型候选词，在多个LS数据集上超越现有最优模型，并在TSAR-2022多语言任务中表现优异。
摘要：
文本是迄今为止最普遍的知识和信息来源，应尽可能让更多人易于获取；然而，文本中常包含阻碍阅读理解和可及性的复杂词汇。因此，在不损害含义的前提下为复杂词汇建议更简单的替代词，将有助于向更广泛的受众传达信息。本文提出mTLS，一个基于T5模型微调的多语言可控Transformer词汇简化（Lexical Simplification, LS）系统。本工作的新颖之处在于使用语言特定前缀、控制标记以及从预训练掩码语言模型中提取的候选词来学习复杂词汇的简单替代词。在三个知名LS数据集——LexMTurk、BenchLS和NNSEval上的评估结果表明，我们的模型优于LSBert和ConLS等先前最先进的模型。此外，我们在最近的TSAR-2022多语言LS共享任务数据集的一部分上对本方法进行了进一步评估，结果显示，与参与英语LS的系统相比，我们的模型具有竞争力，在多个指标上甚至优于GPT-3模型。此外，我们的模型在西班牙语和葡萄牙语上也取得了性能提升。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
