Large pre-trained neural networks such as BERT have had great recent success
in NLP, motivating a growing body of research investigating what aspects of
language they are able to learn from unlabeled data. Most recent analysis has
focused on model outputs (e.g., language model surprisal) or internal vector
representations (e.g., probing classifiers). Complementary to these works, we
propose methods for analyzing the attention mechanisms of pre-trained models
and apply them to BERT. BERT's attention heads exhibit patterns such as
attending to delimiter tokens, specific positional offsets, or broadly
attending over the whole sentence, with heads in the same layer often
exhibiting similar behaviors. We further show that certain attention heads
correspond well to linguistic notions of syntax and coreference. For example,
we find heads that attend to the direct objects of verbs, determiners of nouns,
objects of prepositions, and coreferent mentions with remarkably high accuracy.
Lastly, we propose an attention-based probing classifier and use it to further
demonstrate that substantial syntactic information is captured in BERT's
attention.

本文通过分析 BERT 的注意力机制及其输出结果，提出了新的方法并应用于其内部结构的探究，证明 Bert 的 attention heads 明显与语言的语法和指代有关，其中某些 attention heads 可以高精度地表示动词的直接宾语、名词的限定词和介词的宾语。