SiTSE：僧伽罗语文本简化数据集与评估
SiTSE: Sinhala Text Simplification Dataset and Evaluation
AI总结：
本文构建含1,000个复杂句及3,000个简化句的僧伽罗语人工简化数据集，并基于mT5、mBART验证ITTL可提升零资源文本简化效果。
摘要：
文本简化是一项在低资源语言中极少被探索的任务。因此，目前只有少量人工整理的数据集。本文提出了一个人工整理的僧伽罗语句子级文本简化数据集。我们的评估数据集包含1,000个复杂句子以及由三名不同人工标注者生成的相应3,000个简化句子。我们在多语言语言模型mT5和mBART上将文本简化任务建模为零样本、零资源的序列到序列（seq-seq）任务。我们利用来自相关seq-seq任务的辅助数据，并探索使用中间任务迁移学习（ITTL）的可能性。分析表明，ITTL优于先前提出的用于文本简化的零资源方法。我们的发现还强调了评估文本简化系统的挑战，并支持呼吁改进用于衡量自动化文本简化系统质量的指标，使其同样适用于低资源语言。我们的代码和数据公开发布于：https://github.com/brainsharks-fyp17/Sinhala-Text-Simplification-Dataset-and-Evaluation
英文摘要：
Text Simplification is a task that has been minimally explored for low-resource languages. Consequently, there are only a few manually curated datasets. In this paper, we present a human curated sentence-level text simplification dataset for the Sinhala language. Our evaluation dataset contains 1,000 complex sentences and corresponding 3,000 simplified sentences produced by three different human annotators. We model the text simplification task as a zero-shot and zero resource sequence-to-sequence (seq-seq) task on the multilingual language models mT5 and mBART. We exploit auxiliary data from related seq-seq tasks and explore the possibility of using intermediate task transfer learning (ITTL). Our analysis shows that ITTL outperforms the previously proposed zero-resource methods for text simplification. Our findings also highlight the challenges in evaluating text simplification systems, and support the calls for improved metrics for measuring the quality of automated text simplification systems that would suit low-resource languages as well. Our code and data are publicly available: https://github.com/brainsharks-fyp17/Sinhala-Text-Simplification-Dataset-and-Evaluation
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。
