T-Projection: High Quality Annotation Projection for Sequence Labeling Tasks
This addresses the lack of high-quality training data for sequence labeling tasks in low-resource languages, though it is incremental as it builds on existing annotation projection strategies.
The paper tackles the problem of generating annotated data for sequence labeling tasks in low-resource languages by proposing T-Projection, a novel annotation projection method that outperforms previous approaches by a wide margin, as demonstrated in experiments across 13 languages including low-resource African ones.
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.