State-of-the-art NLP methods achieve human-like performance on many tasks,
but make errors nevertheless. Characterizing these errors in easily
interpretable terms gives insight into whether a classifier is prone to making
systematic errors, but also gives a way to act and improve the classifier. We
propose to discover those patterns of tokens that distinguish correct and
erroneous predictions as to obtain global and interpretable descriptions for
arbitrary NLP classifiers. We formulate the problem of finding a succinct and
non-redundant set of such patterns in terms of the Minimum Description Length
principle. Through an extensive set of experiments, we show that our method,
Premise, performs well in practice. Unlike existing solutions, it recovers
ground truth, even on highly imbalanced data over large vocabularies. In VQA
and NER case studies, we confirm that it gives clear and actionable insight
into the systematic errors made by NLP classifiers.

我们提出了一种方法，通过发现那些区分正确和错误预测的标记模式来获得全局且可解释的描述，从而改善 NLP 分类器的性能。通过一系列实验证明，我们的方法在实践中表现良好，能够在大词汇量的不平衡数据上恢复出真实情况，并对 NLP 分类器的系统错误提供明确可行的见解。

通过可解释的标记模式理解和减轻分类错误

Understanding and Mitigating Classification Errors Through Interpretable  Token Patterns

To address the problem of NLP classifiers learning spurious correlations
between training features and target labels, a common approach is to make the
model's predictions invariant to these features. However, this can be
counter-productive when the features have a non-zero causal effect on the
target label and thus are important for prediction. Therefore, using methods
from the causal inference literature, we propose an algorithm to regularize the
learnt effect of the features on the model's prediction to the estimated effect
of feature on label. This results in an automated augmentation method that
leverages the estimated effect of a feature to appropriately change the labels
for new augmented inputs. On toxicity and IMDB review datasets, the proposed
algorithm minimises spurious correlations and improves the minority group
(i.e., samples breaking spurious correlations) accuracy, while also improving
the total accuracy compared to standard training.

本文提出了一种利用因果推断的方法实现自动化数据增强，从而减少机器学习模型学习特征和标签之间虚假相关性的问题，并在毒性数据集和 IMDB 评论数据集上得到了明显提升。

控制学习效应以减少文本分类器中的错综相关性

Controlling Learned Effects to Reduce Spurious Correlations in Text  Classifiers

Machine learning has been proven to be susceptible to carefully crafted
samples, known as adversarial examples. The generation of these adversarial
examples helps to make the models more robust and gives us an insight into the
underlying decision-making of these models. Over the years, researchers have
successfully attacked image classifiers in both, white and black-box settings.
However, these methods are not directly applicable to texts as text data is
discrete. In recent years, research on crafting adversarial examples against
textual applications has been on the rise. In this paper, we present a novel
approach for hard-label black-box attacks against Natural Language Processing
(NLP) classifiers, where no model information is disclosed, and an attacker can
only query the model to get a final decision of the classifier, without
confidence scores of the classes involved. Such an attack scenario applies to
real-world black-box models being used for security-sensitive applications such
as sentiment analysis and toxic content detection.

本文介绍了一种针对自然语言处理分类器的黑盒硬标签攻击的新方法，其中没有模型信息被公开，攻击者只能查询模型以获得分类器的最终决策，该攻击场景适用于用于情感分析和有毒内容检测等安全敏感应用的真实世界黑盒模型。