Recent efforts in interpreting Convolutional Neural Networks (CNNs) focus on
translating the activation of CNN filters into stratified Answer Set
Programming (ASP) rule-sets. The CNN filters are known to capture high-level
image concepts, thus the predicates in the rule-set are mapped to the concept
that their corresponding filter represents. Hence, the rule-set effectively
exemplifies the decision-making process of the CNN in terms of the concepts
that it learns for any image classification task. These rule-sets help expose
and understand the biases in CNNs, although correcting the biases effectively
remains a challenge. We introduce a neurosymbolic framework called NeSyBiCor
for bias correction in a trained CNN. Given symbolic concepts that the CNN is
biased towards, expressed as ASP constraints, we convert the undesirable and
desirable concepts to their corresponding vector representations. Then, the CNN
is retrained using our novel semantic similarity loss that pushes the filters
away from the representations of concepts that are undesirable while pushing
them closer to the concepts that are desirable. The final ASP rule-set obtained
after retraining, satisfies the constraints to a high degree, thus showing the
revision in the knowledge of the CNN for the image classification task. We
demonstrate that our NeSyBiCor framework successfully corrects the biases of
CNNs trained with subsets of classes from the Places dataset while sacrificing
minimal accuracy and improving interpretability, by greatly decreasing the size
of the final bias-corrected rule-set w.r.t. the initial rule-set.

通过神经符号框架 NeSyBiCor，我们成功纠正了使用 Places 数据集子类训练的卷积神经网络的偏见，以最小的准确性损失和更好的可解释性，大大减少了最终偏见校正规则集的大小。

卷积神经网络中的偏差校正的神经符号框架

A Neurosymbolic Framework for Bias Correction in CNNs

In reading comprehension, generating sentence-level distractors is a
significant task, which requires a deep understanding of the article and
question. The traditional entity-centered methods can only generate word-level
or phrase-level distractors. Although recently proposed neural-based methods
like sequence-to-sequence (Seq2Seq) model show great potential in generating
creative text, the previous neural methods for distractor generation ignore two
important aspects. First, they didn't model the interactions between the
article and question, making the generated distractors tend to be too general
or not relevant to question context. Second, they didn't emphasize the
relationship between the distractor and article, making the generated
distractors not semantically relevant to the article and thus fail to form a
set of meaningful options. To solve the first problem, we propose a
co-attention enhanced hierarchical architecture to better capture the
interactions between the article and question, thus guide the decoder to
generate more coherent distractors. To alleviate the second problem, we add an
additional semantic similarity loss to push the generated distractors more
relevant to the article. Experimental results show that our model outperforms
several strong baselines on automatic metrics, achieving state-of-the-art
performance. Further human evaluation indicates that our generated distractors
are more coherent and more educative compared with those distractors generated
by baselines.

该论文提出了一种基于神经网络的方法来生成阅读理解中的干扰选项，采用了注意力机制和语义相似度损失来提高干扰选项的生成质量，实验结果表明，该方法的表现优于其他基线方法。