数据有限的卢森堡语文本生成模型：一种平衡的多语言策略
Text Generation Models for Luxembourgish with Limited Data: A Balanced Multilingual Strategy
AI总结：
本文针对卢森堡语数据稀缺问题，提出基于T5架构并结合等量德语和法语数据的平衡多语言文本生成模型，并引入首个卢森堡语文本生成基准LuxGen进行评估。
摘要：
本文探讨了为代表性不足的语言开发语言模型所面临的挑战，重点关注卢森堡语。尽管卢森堡语处于活跃发展期，但它面临数字数据稀缺的问题，而卢森堡的多语言环境进一步加剧了这一情况。我们提出了一种基于T5架构的新型文本生成模型，将有限的卢森堡语数据与在规模和类型上等量的德语和法语数据相结合。我们假设，在卢森堡语、德语和法语上训练的模型将提升跨语言迁移学习能力，并优于单语和大型多语言模型。为验证这一点，本研究探讨了多语言或单语训练对卢森堡语生成是否更有利。在评估方面，我们引入了LuxGen，这是首个专为卢森堡语设计的文本生成基准。
英文摘要：
This paper addresses the challenges in developing language models for less-represented languages, with a focus on Luxembourgish. Despite its active development, Luxembourgish faces a digital data scarcity, exacerbated by Luxembourg's multilingual context. We propose a novel text generation model based on the T5 architecture, combining limited Luxembourgish data with equal amounts, in terms of size and type, of German and French data. We hypothesise that a model trained on Luxembourgish, German, and French will improve the model's cross-lingual transfer learning capabilities and outperform monolingual and large multilingual models. To verify this, the study at hand explores whether multilingual or monolingual training is more beneficial for Luxembourgish language generation. For the evaluation, we introduce LuxGen, a text generation benchmark that is the first of its kind for Luxembourgish.
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。
