BERT, as one of the pretrianed language models, attracts the most attention
in recent years for creating new benchmarks across GLUE tasks via fine-tuning.
One pressing issue is to open up the blackbox and explain the decision makings
of BERT. A number of attribution techniques have been proposed to explain BERT
models, but are often limited to sequence to sequence tasks. In this paper, we
adapt existing attribution methods on explaining decision makings of BERT in
sequence classification tasks. We conduct extensive analyses of four existing
attribution methods by applying them to four different datasets in sentiment
analysis. We compare the reliability and robustness of each method via various
ablation studies. Furthermore, we test whether attribution methods explain
generalized semantics across semantically similar tasks. Our work provides
solid guidance for using attribution methods to explain decision makings of
BERT for downstream classification tasks.

本文对 BERT 在序列分类任务中的决策方法进行解释，并对现有的四种属性方法进行了广泛的分析，以应用于四个不同的情感分析数据集。通过各种消融研究比较了每种方法的可靠性和稳健性，并测试属性方法是否能解释语义上相似的任务的广义语义。

关于 BERT 说明解释的实证研究：序列分类

On Explaining Your Explanations of BERT: An Empirical Study with  Sequence Classification

We study the problem of learning differentiable functions expressed as
programs in a domain-specific language. Such programmatic models can offer
benefits such as composability and interpretability; however, learning them
requires optimizing over a combinatorial space of program "architectures". We
frame this optimization problem as a search in a weighted graph whose paths
encode top-down derivations of program syntax. Our key innovation is to view
various classes of neural networks as continuous relaxations over the space of
programs, which can then be used to complete any partial program. This relaxed
program is differentiable and can be trained end-to-end, and the resulting
training loss is an approximately admissible heuristic that can guide the
combinatorial search. We instantiate our approach on top of the A-star
algorithm and an iteratively deepened branch-and-bound search, and use these
algorithms to learn programmatic classifiers in three sequence classification
tasks. Our experiments show that the algorithms outperform state-of-the-art
methods for program learning, and that they discover programmatic classifiers
that yield natural interpretations and achieve competitive accuracy.

研究解决学习特定领域语言中表达的可微分函数的问题，通过将神经网络的不同类视为对程序空间的松弛，并在一系列分类任务中实例化遗传算法和分支界限搜索来发现自然解释和有竞争力的程序分类器。

使用可接受的神经启发式学习可微分程序

Learning Differentiable Programs with Admissible Neural Heuristics

The attention mechanism has quickly become ubiquitous in NLP. In addition to
improving performance of models, attention has been widely used as a glimpse
into the inner workings of NLP models. The latter aspect has in the recent
years become a common topic of discussion, most notably in work of Jain and
Wallace, 2019; Wiegreffe and Pinter, 2019. With the shortcomings of using
attention weights as a tool of transparency revealed, the attention mechanism
has been stuck in a limbo without concrete proof when and whether it can be
used as an explanation. In this paper, we provide an explanation as to why
attention has seen rightful critique when used with recurrent networks in
sequence classification tasks. We propose a remedy to these issues in the form
of a word level objective and our findings give credibility for attention to
provide faithful interpretations of recurrent models.

研究关注机制在 NLP 中的应用，提出了一个针对循环网络序列分类任务中存在的问题的解决方法，并给出了证明，说明关注机制可以提供循环模型的可靠解释。