LuxEmbedder：一种增强卢森堡语句子嵌入的跨语言方法
LuxEmbedder: A Cross-Lingual Approach to Enhanced Luxembourgish Sentence Embeddings
AI总结：
针对卢森堡语平行数据稀缺问题，本文构建高质量小型跨语言平行数据集训练 LuxEmbedder，并提出卢森堡语复述检测基准，验证低资源语言对纳入训练的增益。
摘要：
句子嵌入模型在各类自然语言处理任务中发挥着关键作用，例如主题建模、文档聚类和推荐系统。然而，这些模型严重依赖平行数据，而对于包括卢森堡语在内的许多低资源语言而言，平行数据可能十分稀缺。这种稀缺性导致面向这些语言的单语和跨语言句子嵌入模型性能欠佳。为解决这一问题，我们编制了一个规模较小但高质量、由人工生成的跨语言平行数据集，用于训练 LuxEmbedder，这是一个增强型卢森堡语句子嵌入模型，并具备强大的跨语言能力。此外，我们提供的证据表明，在平行训练数据集中纳入低资源语言，相比仅依赖高资源语言对，可能更有利于其他低资源语言。进一步而言，鉴于低资源语言缺乏句子嵌入基准，我们专门为卢森堡语创建了一个复述检测基准，旨在部分填补这一空白并推动进一步研究。
英文摘要：
Sentence embedding models play a key role in various Natural Language Processing tasks, such as in Topic Modeling, Document Clustering and Recommendation Systems. However, these models rely heavily on parallel data, which can be scarce for many low-resource languages, including Luxembourgish. This scarcity results in suboptimal performance of monolingual and cross-lingual sentence embedding models for these languages. To address this issue, we compile a relatively small but high-quality human-generated cross-lingual parallel dataset to train LuxEmbedder, an enhanced sentence embedding model for Luxembourgish with strong cross-lingual capabilities. Additionally, we present evidence suggesting that including low-resource languages in parallel training datasets can be more advantageous for other low-resource languages than relying solely on high-resource language pairs. Furthermore, recognizing the lack of sentence embedding benchmarks for low-resource languages, we create a paraphrase detection benchmark specifically for Luxembourgish, aiming to partially fill this gap and promote further research.
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。
