Leveraging large amounts of unlabeled data using Transformer-like
architectures, like BERT, has gained popularity in recent times owing to their
effectiveness in learning general representations that can then be further
fine-tuned for downstream tasks to much success. However, training these models
can be costly both from an economic and environmental standpoint. In this work,
we investigate how to effectively use unlabeled data: by exploring the
task-specific semi-supervised approach, Cross-View Training (CVT) and comparing
it with task-agnostic BERT in multiple settings that include domain and task
relevant English data. CVT uses a much lighter model architecture and we show
that it achieves similar performance to BERT on a set of sequence tagging
tasks, with lesser financial and environmental impact.

本文探讨了如何有效地利用未标记数据 —— 通过探索任务特定的半监督方法 Cross-View Training (CVT)，并将其与任务不可知的 BERT 在包括领域和任务相关的英语数据的多个设置中进行比较。CVT 使用较轻的模型结构，并表明它在一组序列标记任务上实现了与 BERT 相似的性能，具有更小的经济和环境影响。