Neural machine translation benefits from semantically rich representations.
Considerable progress in learning such representations has been achieved by
language modelling and mutual information maximization objectives using
contrastive learning. The language-dependent nature of language modelling
introduces a trade-off between the universality of the learned representations
and the model's performance on the language modelling tasks. Although
contrastive learning improves performance, its success cannot be attributed to
mutual information alone. We propose a novel Context Enhancement step to
improve performance on neural machine translation by maximizing mutual
information using the Barlow Twins loss. Unlike other approaches, we do not
explicitly augment the data but view languages as implicit augmentations,
eradicating the risk of disrupting semantic information. Further, our method
does not learn embeddings from scratch and can be generalised to any set of
pre-trained embeddings. Finally, we evaluate the language-agnosticism of our
embeddings through language classification and use them for neural machine
translation to compare with state-of-the-art approaches.

通过最大化巴洛双生损失来提高神经机器翻译性能，从而得到含义丰富的表示，并通过语言分类和与最先进的方法进行对比，评估我们表示的语言无关性。

通过对比增强上下文

Enhancing Context Through Contrast

Semantic image segmentation is a critical component in many computer vision
systems, such as autonomous driving. In such applications, adverse conditions
(heavy rain, night time, snow, extreme lighting) on the one hand pose specific
challenges, yet are typically underrepresented in the available datasets.
Generating more training data is cumbersome and expensive, and the process
itself is error-prone due to the inherent aleatoric uncertainty. To address
this challenging problem, we propose BTSeg, which exploits image-level
correspondences as weak supervision signal to learn a segmentation model that
is agnostic to adverse conditions. To this end, our approach uses the Barlow
twins loss from the field of unsupervised learning and treats images taken at
the same location but under different adverse conditions as "augmentations" of
the same unknown underlying base image. This allows the training of a
segmentation model that is robust to appearance changes introduced by different
adverse conditions. We evaluate our approach on ACDC and the new challenging
ACG benchmark to demonstrate its robustness and generalization capabilities.
Our approach performs favorably when compared to the current state-of-the-art
methods, while also being simpler to implement and train. The code will be
released upon acceptance.

通过利用图像级对应作为弱监督信号，我们提出了 BTSeg，它可以学习对不良条件不敏感的分割模型，通过不同不良条件下的图像来增强训练数据，它在 ACDC 和 ACG 基准测试中表现优秀，且相比现有的最先进方法更简单易用。