Language representations are known to carry stereotypical biases and, as a
result, lead to biased predictions in downstream tasks. While existing methods
are effective at mitigating biases by linear projection, such methods are too
aggressive: they not only remove bias, but also erase valuable information from
word embeddings. We develop new measures for evaluating specific information
retention that demonstrate the tradeoff between bias removal and information
retention. To address this challenge, we propose OSCaR (Orthogonal Subspace
Correction and Rectification), a bias-mitigating method that focuses on
disentangling biased associations between concepts instead of removing concepts
wholesale. Our experiments on gender biases show that OSCaR is a well-balanced
approach that ensures that semantic information is retained in the embeddings
and bias is also effectively mitigated.

本文提出了一种名为 OSCaR 的新的降低偏见的方法，该方法专注于解开概念之间的偏见关联而非整体去除概念。实验结果表明，OSCaR 方法保证了嵌入中的语义信息被保留且能够有效地缓解偏见，特别是在性别偏见的情况下表现出良好的平衡性。

OSCaR: 词向量中正交子空间矫正及偏差校正

OSCaR: Orthogonal Subspace Correction and Rectification of Biases in  Word Embeddings

Large-scale pre-training methods of learning cross-modal representations on
image-text pairs are becoming popular for vision-language tasks. While existing
methods simply concatenate image region features and text features as input to
the model to be pre-trained and use self-attention to learn image-text semantic
alignments in a brute force manner, in this paper, we propose a new learning
method Oscar (Object-Semantics Aligned Pre-training), which uses object tags
detected in images as anchor points to significantly ease the learning of
alignments. Our method is motivated by the observation that the salient objects
in an image can be accurately detected, and are often mentioned in the paired
text. We pre-train an Oscar model on the public corpus of 6.5 million
text-image pairs, and fine-tune it on downstream tasks, creating new
state-of-the-arts on six well-established vision-language understanding and
generation tasks.

本文介绍了一种基于目标检测的图像 - 文本预训练学习方法 Oscar，通过使用在图像中检测到的目标标签作为锚点，显著简化模型中的图像与文本的语义对齐，使其在六个视觉 - 语言理解和生成任务中创造了新的最好成果。