T-Projection：面向序列标注任务的高质量标注投影
T-Projection: High Quality Annotation Projection for Sequence Labeling Tasks
AI总结：
本文提出T-Projection，利用多语言T5模型和机器翻译技术，将标注投影分解为候选生成与选择两步，在多语种实验中显著优于现有方法，有效缓解序列标注任务缺乏高质量训练数据的问题。
摘要：
在缺乏可用于给定序列标注任务和语言的标注数据时，标注投影被提出作为一种自动生成标注数据的可能策略。标注投影通常被表述为在平行语料库上，将源语言中属于给定跨度的标签转移到目标语言中对应跨度的任务。在本文中，我们提出了T-Projection，一种利用大型预训练文本到文本语言模型和最先进机器翻译技术的标注投影新方法。T-Projection将标签投影任务分解为两个子任务：(i) 候选生成步骤，使用多语言T5模型生成一组投影候选；(ii) 候选选择步骤，根据翻译概率对生成的候选进行排序。我们在5种印欧语系和8种低资源非洲语言的内在和外在任务上进行了实验。我们证明T-Projection以显著优势优于以往的标注投影方法。我们相信T-Projection能帮助自动缓解序列标注任务缺乏高质量训练数据的问题。代码和数据已公开。
英文摘要：
In the absence of readily available labeled data for a given sequence labeling task and language, annotation projection has been proposed as one of the possible strategies to automatically generate annotated data. Annotation projection has often been formulated as the task of transporting, on parallel corpora, the labels pertaining to a given span in the source language into its corresponding span in the target language. In this paper we present T-Projection, a novel approach for annotation projection that leverages large pretrained text-to-text language models and state-of-the-art machine translation technology. T-Projection decomposes the label projection task into two subtasks: (i) A candidate generation step, in which a set of projection candidates using a multilingual T5 model is generated and, (ii) a candidate selection step, in which the generated candidates are ranked based on translation probabilities. We conducted experiments on intrinsic and extrinsic tasks in 5 Indo-European and 8 low-resource African languages. We demostrate that T-projection outperforms previous annotation projection methods by a wide margin. We believe that T-Projection can help to automatically alleviate the lack of high-quality training data for sequence labeling tasks. Code and data are publicly available.
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。
