Contextualized word embeddings derived from pre-trained language models (LMs)
show significant improvements on downstream NLP tasks. Pre-training on
domain-specific corpora, such as biomedical articles, further improves their
performance. In this paper, we conduct probing experiments to determine what
additional information is carried intrinsically by the in-domain trained
contextualized embeddings. For this we use the pre-trained LMs as fixed feature
extractors and restrict the downstream task models to not have additional
sequence modeling layers. We compare BERT, ELMo, BioBERT and BioELMo, a
biomedical version of ELMo trained on 10M PubMed abstracts. Surprisingly, while
fine-tuned BioBERT is better than BioELMo in biomedical NER and NLI tasks, as a
fixed feature extractor BioELMo outperforms BioBERT in our probing tasks. We
use visualization and nearest neighbor analysis to show that better encoding of
entity-type and relational information leads to this superiority.

本文研究使用预训练的语言模型作为固定的特征提取器，并限制下游任务模型没有额外的序列建模层，探索在域内训练后的上下文词嵌入中携带的附加信息。结果发现，BioELMo 在编码实体类型和关系信息方面优于 BioBERT。