Intermediate-task training---fine-tuning a pretrained model on an
intermediate task before fine-tuning again on the target task---often improves
model performance substantially on language understanding tasks in monolingual
English settings. We investigate whether English intermediate-task training is
still helpful on non-English target tasks. Using nine intermediate
language-understanding tasks, we evaluate intermediate-task transfer in a
zero-shot cross-lingual setting on the XTREME benchmark. We see large
improvements from intermediate training on the BUCC and Tatoeba sentence
retrieval tasks and moderate improvements on question-answering target tasks.
MNLI, SQuAD and HellaSwag achieve the best overall results as intermediate
tasks, while multi-task intermediate offers small additional improvements.
Using our best intermediate-task models for each target task, we obtain a 5.4
point improvement over XLM-R Large on the XTREME benchmark, setting the state
of the art as of June 2020. We also investigate continuing multilingual MLM
during intermediate-task training and using machine-translated
intermediate-task data, but neither consistently outperforms simply performing
English intermediate-task training.

本研究主要研究在非英语任务上英语中间任务训练（Intermediate-task training）是否有助于跨语言理解学习迁移，通过在 XTREME 基准测试上得到大幅度的改进，我们的最佳模型超过了 XLM-R Large，成为 2020 年 6 月的最新技术水平，并对多语言 MLM 和机器翻译的中间任务数据进行了探讨。