Contextualized word embeddings such as ELMo and BERT provide a foundation for strong performance across a range of natural language processing tasks, in part by pretraining on a large and topically-diverse corpus. However, the applicability of this approach is unknown when the target domain varies substantially from the text used during pretraining. Specifically, we are interested the scenario in which labeled data is available in only a canonical source domain such as newstext, and the target domain is distinct from both the labeled corpus and the pretraining data. To address this scenario, we propose domain-adaptive fine-tuning, in which the contextualized embeddings are adapted by masked language modeling on the target domain. We test this approach on the challenging domain of Early Modern English, which differs substantially from existing pretraining corpora. Domain-adaptive fine-tuning yields an improvement of 4\% in part-of-speech tagging accuracy over a BERT baseline, substantially improving on prior work on this task.

本文提出了一种基于掩码语言模型，在目标领域中调整上下文嵌入的方法，从而实现了序列标注的无人监督适应，获得了重要的改进，特别是在词汇表外的单词上。

无监督领域自适应的上下文嵌入序列标注