Spurious correlations are brittle associations between certain attributes of
inputs and target variables, such as the correlation between an image
background and an object class. Deep image classifiers often leverage them for
predictions, leading to poor generalization on the data where the correlations
do not hold. Mitigating the impact of spurious correlations is crucial towards
robust model generalization, but it often requires annotations of the spurious
correlations in data -- a strong assumption in practice. In this paper, we
propose a novel learning framework based on meta-learning, termed SPUME --
SPUriousness-aware MEta-learning, to train an image classifier to be robust to
spurious correlations. We design the framework to iteratively detect and
mitigate the spurious correlations that the classifier excessively relies on
for predictions. To achieve this, we first propose to utilize a pre-trained
vision-language model to extract text-format attributes from images. These
attributes enable us to curate data with various class-attribute correlations,
and we formulate a novel metric to measure the degree of these correlations'
spuriousness. Then, to mitigate the reliance on spurious correlations, we
propose a meta-learning strategy in which the support (training) sets and query
(test) sets in tasks are curated with different spurious correlations that have
high degrees of spuriousness. By meta-training the classifier on these
spuriousness-aware meta-learning tasks, our classifier can learn to be
invariant to the spurious correlations. We demonstrate that our method is
robust to spurious correlations without knowing them a priori and achieves the
best on five benchmark datasets with different robustness measures.

通过元学习训练图像分类器以减少对伪相关性的依赖，以实现对伪相关性的鲁棒性，同时在不知道先验信息的情况下获得了五个基准数据集上最好的鲁棒性。

针对学习鲁棒分类器的伪装感知元学习

Spuriousness-Aware Meta-Learning for Learning Robust Classifiers

Multi-aspect controllable text generation aims to control the generated texts
in attributes from multiple aspects (e.g., "positive" from sentiment and
"sport" from topic). For ease of obtaining training samples, existing works
neglect attribute correlations formed by the intertwining of different
attributes. Particularly, the stereotype formed by imbalanced attribute
correlations significantly affects multi-aspect control. In this paper, we
propose MAGIC, a new multi-aspect controllable text generation method with
disentangled counterfactual augmentation. We alleviate the issue of imbalanced
attribute correlations during training using counterfactual feature vectors in
the attribute latent space by disentanglement. During inference, we enhance
attribute correlations by target-guided counterfactual augmentation to further
improve multi-aspect control. Experiments show that MAGIC outperforms
state-of-the-art baselines in both imbalanced and balanced attribute
correlation scenarios. Our source code and data are available at
this https URL

提出了一种新的多方面可控文本生成方法 MAGIC，通过解耦反事实特征向量（在属性潜在空间中）缓解了训练过程中不平衡属性相关性的问题，并通过目标导向的反事实增强在推理过程中增强了属性相关性，实验证明 MAGIC 在不平衡和平衡属性相关性的场景中优于现有的方法。

多方面可控文本生成与解耦式反事实增强

Multi-Aspect Controllable Text Generation with Disentangled  Counterfactual Augmentation

Entity alignment is the task of finding entities in two knowledge bases (KBs)
that represent the same real-world object. When facing KBs in different natural
languages, conventional cross-lingual entity alignment methods rely on machine
translation to eliminate the language barriers. These approaches often suffer
from the uneven quality of translations between languages. While recent
embedding-based techniques encode entities and relationships in KBs and do not
need machine translation for cross-lingual entity alignment, a significant
number of attributes remain largely unexplored. In this paper, we propose a
joint attribute-preserving embedding model for cross-lingual entity alignment.
It jointly embeds the structures of two KBs into a unified vector space and
further refines it by leveraging attribute correlations in the KBs. Our
experimental results on real-world datasets show that this approach
significantly outperforms the state-of-the-art embedding approaches for
cross-lingual entity alignment and could be complemented with methods based on
machine translation.

本文提出了一种联合属性保留嵌入模型以解决跨语言实体对齐的问题，将两个知识库的结构联合嵌入到一个统一的向量空间，通过利用知识库中的属性相关性进一步优化。实验结果表明，该方法显著优于现有的基于嵌入的方法。