低资源自然语言处理的跨语言迁移
Cross-Lingual Transfer for Low-Resource Natural Language Processing
AI总结：
本论文研究低资源语言的跨语言迁移学习，提出T-Projection标注投影方法和约束解码算法以提升序列标注性能，并开发了首个多语言医学模型Medical mT5。
摘要：
近年来，自然语言处理（NLP）取得了显著进展，尤其是随着大型语言模型的出现，这些模型在许多任务上实现了前所未有的性能。然而，这些发展主要惠及英语等少数高资源语言。大多数语言由于训练数据和计算资源的稀缺，仍然面临重大挑战。为了解决这一问题，本论文聚焦于跨语言迁移学习，这一研究领域旨在利用高资源语言的数据和模型来提升低资源语言的NLP性能。具体而言，我们专注于序列标注任务，如命名实体识别（Named Entity Recognition）、观点目标抽取（Opinion Target Extraction）和论辩挖掘（Argument Mining）。本研究围绕三个主要目标展开：（1）通过改进的翻译和标注投影技术推进基于数据的跨语言迁移学习方法；（2）利用最先进的多语言模型开发增强的基于模型的迁移学习方法；（3）将这些方法应用于现实问题，同时创建开源资源以促进低资源NLP的未来研究。更具体地说，本论文提出了一种新方法，通过T-Projection改进基于数据的迁移，T-Projection是一种最先进的标注投影方法，利用文本到文本多语言模型和机器翻译系统。T-Projection以大幅优势显著超越了以往的标注投影方法。对于基于模型的迁移，我们引入了一种约束解码算法，在使用文本到文本模型的零样本设置中增强跨语言序列标注。最后，我们开发了Medical mT5，这是第一个多语言文本到文本医学模型，展示了我们的研究对现实世界应用的实际影响。
英文摘要：
Natural Language Processing (NLP) has seen remarkable advances in recent years, particularly with the emergence of Large Language Models that have achieved unprecedented performance across many tasks. However, these developments have mainly benefited a small number of high-resource languages such as English. The majority of languages still face significant challenges due to the scarcity of training data and computational resources. To address this issue, this thesis focuses on cross-lingual transfer learning, a research area aimed at leveraging data and models from high-resource languages to improve NLP performance for low-resource languages. Specifically, we focus on Sequence Labeling tasks such as Named Entity Recognition, Opinion Target Extraction, and Argument Mining. The research is structured around three main objectives: (1) advancing data-based cross-lingual transfer learning methods through improved translation and annotation projection techniques, (2) developing enhanced model-based transfer learning approaches utilizing state-of-the-art multilingual models, and (3) applying these methods to real-world problems while creating open-source resources that facilitate future research in low-resource NLP. More specifically, this thesis presents a new method to improve data-based transfer with T-Projection, a state-of-the-art annotation projection method that leverages text-to-text multilingual models and machine translation systems. T-Projection significantly outperforms previous annotation projection methods by a wide margin. For model-based transfer, we introduce a constrained decoding algorithm that enhances cross-lingual Sequence Labeling in zero-shot settings using text-to-text models. Finally, we develop Medical mT5, the first multilingual text-to-text medical model, demonstrating the practical impact of our research on real-world applications.
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。
