Due to their similarity-based learning objectives, pretrained sentence
encoders often internalize stereotypical assumptions that reflect the social
biases that exist within their training corpora. In this paper, we describe
several kinds of stereotypes concerning different communities that are present
in popular sentence representation models, including pretrained next sentence
prediction and contrastive sentence representation models. We compare such
models to textual entailment models that learn language logic for a variety of
downstream language understanding tasks. By comparing strong pretrained models
based on text similarity with textual entailment learning, we conclude that the
explicit logic learning with textual entailment can significantly reduce bias
and improve the recognition of social communities, without an explicit
de-biasing process

本文中，我们描述了几种针对不同社区的刻板印象，这些社区存在于受欢迎的句子表示模型（包括预训练的下个句子预测和对比句子表示模型）中。通过比较基于文本相似性的强预训练模型与学习语言逻辑的文本蕴涵模型，我们得出结论：与显式去偏见流程相比，使用文本蕴涵显式逻辑学习可以显著减少偏见并提高社区的识别。