Bidirectional Encoder Representations from Transformers (BERT) reach
state-of-the-art results in a variety of Natural Language Processing tasks.
However, understanding of their internal functioning is still insufficient and
unsatisfactory. In order to better understand BERT and other Transformer-based
models, we present a layer-wise analysis of BERT's hidden states. Unlike
previous research, which mainly focuses on explaining Transformer models by
their attention weights, we argue that hidden states contain equally valuable
information. Specifically, our analysis focuses on models fine-tuned on the
task of Question Answering (QA) as an example of a complex downstream task. We
inspect how QA models transform token vectors in order to find the correct
answer. To this end, we apply a set of general and QA-specific probing tasks
that reveal the information stored in each representation layer. Our
qualitative analysis of hidden state visualizations provides additional
insights into BERT's reasoning process. Our results show that the
transformations within BERT go through phases that are related to traditional
pipeline tasks. The system can therefore implicitly incorporate task-specific
information into its token representations. Furthermore, our analysis reveals
that fine-tuning has little impact on the models' semantic abilities and that
prediction errors can be recognized in the vector representations of even early
layers.

我们对 BERT 的隐藏状态进行逐层分析，揭示了这些状态所包含的有价值的信息，包括 QA 任务 fine-tuned 的模型如何转换令牌向量以找到正确的答案。通过应用一组用于揭示每个表示层中存储信息的一般和 QA 特定探测任务，我们的分析显示 BERT 的转换经历了与传统 pipeline 任务相关的阶段，且细微调整对模型的语意能力影响不大，即使在早期层也可以识别出预测错误。

BERT 是如何回答问题的：对 Transformer 表示进行逐层分析

How Does BERT Answer Questions? A Layer-Wise Analysis of Transformer  Representations

We introduce a set of nine challenge tasks that test for the understanding of
function words. These tasks are created by structurally mutating sentences from
existing datasets to target the comprehension of specific types of function
words (e.g., prepositions, wh-words). Using these probing tasks, we explore the
effects of various pretraining objectives for sentence encoders (e.g., language
modeling, CCG supertagging and natural language inference (NLI)) on the learned
representations. Our results show that pretraining on language modeling
performs the best on average across our probing tasks, supporting its
widespread use for pretraining state-of-the-art NLP models, and CCG
supertagging and NLI pretraining perform comparably. Overall, no pretraining
objective dominates across the board, and our function word probing tasks
highlight several intuitive differences between pretraining objectives, e.g.,
that NLI helps the comprehension of negation.

本文通过构造一组挑战任务，测试了句子编码器中对功能词语的理解能力，结果显示，在句子编码器的预训练中，语言建模性能表现最佳，支持其广泛用于现有的 NLP 模型预训练，而 CCG 超标记和自然语言推理的预训练表现相当。

探究不同 NLP 任务对机器关于虚词理解方面的学习

Probing What Different NLP Tasks Teach Machines about Function Word  Comprehension

Despite an ever growing number of word representation models introduced for a
large number of languages, there is a lack of a standardized technique to
provide insights into what is captured by these models. Such insights would
help the community to get an estimate of the downstream task performance, as
well as to design more informed neural architectures, while avoiding extensive
experimentation which requires substantial computational resources not all
researchers have access to. A recent development in NLP is to use simple
classification tasks, also called probing tasks, that test for a single
linguistic feature such as part-of-speech. Existing studies mostly focus on
exploring the linguistic information encoded by the continuous representations
of English text. However, from a typological perspective the morphologically
poor English is rather an outlier: the information encoded by the word order
and function words in English is often stored on a morphological level in other
languages. To address this, we introduce 15 type-level probing tasks such as
case marking, possession, word length, morphological tag count and pseudoword
identification for 24 languages. We present a reusable methodology for creation
and evaluation of such tests in a multilingual setting. We then present
experiments on several diverse multilingual word embedding models, in which we
relate the probing task performance for a diverse set of languages to a range
of five classic NLP tasks: POS-tagging, dependency parsing, semantic role
labeling, named entity recognition and natural language inference. We find that
a number of probing tests have significantly high positive correlation to the
downstream tasks, especially for morphologically rich languages. We show that
our tests can be used to explore word embeddings or black-box neural models for
linguistic cues in a multilingual setting.

该研究介绍了在 24 种语言中引入 15 种类型级别的探究任务，测试诸如情况标记、单词长度、形态标记计数和伪词识别等特定语法特征和语言提示的分类任务，以便探索词嵌入或黑盒神经模型的多语言语言线索，发现许多探究测试具有与子任务的显着高正相关性，特别是对于形态丰富的语言。