Some Transformer-based models can perform cross-lingual transfer learning:
those models can be trained on a specific task in one language and give
relatively good results on the same task in another language, despite having
been pre-trained on monolingual tasks only. But, there is no consensus yet on
whether those transformer-based models learn universal patterns across
languages. We propose a word-level task-agnostic method to evaluate the
alignment of contextualized representations built by such models. We show that
our method provides more accurate translated word pairs than previous methods
to evaluate word-level alignment. And our results show that some inner layers
of multilingual Transformer-based models outperform other explicitly aligned
representations, and even more so according to a stricter definition of
multilingual alignment.

本文提出一种单词级别的任务无关方法来评估多语言 Transformers 模型构建的上下文表示的对齐情况，并表明一些多语言 Transformer 模型的内部层表现优于其他明确对齐表示，甚至更符合更严格的多语言对齐定义。

多语言变形金刚编码器：基于词级别的任务无关评估

Multilingual Transformer Encoders: a Word-Level Task-Agnostic Evaluation

Unsupervised image captioning is a challenging task that aims at generating
captions without the supervision of image-sentence pairs, but only with images
and sentences drawn from different sources and object labels detected from the
images. In previous work, pseudo-captions, i.e., sentences that contain the
detected object labels, were assigned to a given image. The focus of the
previous work was on the alignment of input images and pseudo-captions at the
sentence level. However, pseudo-captions contain many words that are irrelevant
to a given image. In this work, we investigate the effect of removing
mismatched words from image-sentence alignment to determine how they make this
task difficult. We propose a simple gating mechanism that is trained to align
image features with only the most reliable words in pseudo-captions: the
detected object labels. The experimental results show that our proposed method
outperforms the previous methods without introducing complex sentence-level
learning objectives. Combined with the sentence-level alignment method of
previous work, our method further improves its performance. These results
confirm the importance of careful alignment in word-level details.

本文主要探讨了通过提出一个简单的门控机制来从伪字幕中选择可靠的单词以提高图像字幕生成任务的准确性，并证明了在单词级别详细对齐方面的慎重对齐的重要性。