We study cross-lingual stance detection, which aims to leverage labeled data
in one language to identify the relative perspective (or stance) of a given
document with respect to a claim in a different target language. In particular,
we introduce a novel contrastive language adaptation approach applied to memory
networks, which ensures accurate alignment of stances in the source and target
languages, and can effectively deal with the challenge of limited labeled data
in the target language. The evaluation results on public benchmark datasets and
comparison against current state-of-the-art approaches demonstrate the
effectiveness of our approach.

本文研究了跨语言立场检测，提出了一种新颖的对抗语言适应方法应用于记忆网络，确保源语言和目标语言之间的立场对齐，可以有效地处理目标语言中标注数据有限的挑战。实验结果表明，与当前最先进的方法相比，该方法的有效性得到了证明。

跨语种立场检测的对比语言适应

Contrastive Language Adaptation for Cross-Lingual Stance Detection

The ability to understand visual information from limited labeled data is an
important aspect of machine learning. While image-level classification has been
extensively studied in a semi-supervised setting, dense pixel-level
classification with limited data has only drawn attention recently. In this
work, we propose an approach for semi-supervised semantic segmentation that
learns from limited pixel-wise annotated samples while exploiting additional
annotation-free images. It uses two network branches that link semi-supervised
classification with semi-supervised segmentation including self-training. The
dual-branch approach reduces both the low-level and the high-level artifacts
typical when training with few labels. The approach attains significant
improvement over existing methods, especially when trained with very few
labeled samples. On several standard benchmarks - PASCAL VOC 2012,
PASCAL-Context, and Cityscapes - the approach achieves new state-of-the-art in
semi-supervised learning.

文章提出了一种利用一些有限的像素注释数据和一些无标签数据进行半监督语义分割的方法，并在 PASCAL VOC 2012，PASCAL-Context，和 Cityscapes 数据集上实现了最新的半监督学习的最佳性能。