In the absence of readily available labeled data for a given task and
language, annotation projection has been proposed as one of the possible
strategies to automatically generate annotated data which may then be used to
train supervised systems. Annotation projection has often been formulated as
the task of projecting, on parallel corpora, some labels from a source into a
target language. In this paper we present T-Projection, a new approach for
annotation projection that leverages large pretrained text2text language models
and state-of-the-art machine translation technology. T-Projection decomposes
the label projection task into two subtasks: (i) The candidate generation step,
in which a set of projection candidates using a multilingual T5 model is
generated and, (ii) the candidate selection step, in which the candidates are
ranked based on translation probabilities. We evaluate our method in three
downstream tasks and five different languages. Our results show that
T-projection improves the average F1 score of previous methods by more than 8
points.

本文提出了一种新的基于预训练文本到文本语言模型和最先进的机器翻译技术的注释投影方法［T-Projection］，通过将标签投影任务分解成候选集生成步骤和候选集选择步骤来自动产生标注数据，得到的结果在三个下游任务和五种不同语言中的平均 F1 分数比以前的方法提高了 8 个百分点以上。