Neural machine translation (NMT) has progressed rapidly in the past few
years, promising improvements and quality translations for different languages.
Evaluation of this task is crucial to determine the quality of the translation.
Overall, insufficient emphasis is placed on the actual sense of the translation
in traditional methods. We propose a bidirectional semantic-based evaluation
method designed to assess the sense distance of the translation from the source
text. This approach employs the comprehensive multilingual encyclopedic
dictionary BabelNet. Through the calculation of the semantic distance between
the source and its back translation of the output, our method introduces a
quantifiable approach that empowers sentence comparison on the same linguistic
level. Factual analysis shows a strong correlation between the average
evaluation scores generated by our method and the human assessments across
various machine translation systems for English-German language pair. Finally,
our method proposes a new multilingual approach to rank MT systems without the
need for parallel corpora.

神经机器翻译在近年来取得了快速的进展，我们提出了一种双向语义评估方法，通过计算源文本与翻译的语义距离，实现了在同一语言层面上的句子比较，该方法在英德语对的多个机器翻译系统中得出的平均评估分数与人工评估之间存在强相关性，同时提出了一种新的多语言方法来对 MT 系统进行排序，无需平行语料库。

BiVert: 双向词汇评估机器翻译关系

BiVert: Bidirectional Vocabulary Evaluation using Relations for Machine  Translation

Text-guided image editing has recently experienced rapid development.
However, simultaneously performing multiple editing actions on a single image,
such as background replacement and specific subject attribute changes, while
maintaining consistency between the subject and the background remains
challenging. In this paper, we propose LayerDiffusion, a semantic-based layered
controlled image editing method. Our method enables non-rigid editing and
attribute modification of specific subjects while preserving their unique
characteristics and seamlessly integrating them into new backgrounds. We
leverage a large-scale text-to-image model and employ a layered controlled
optimization strategy combined with layered diffusion training. During the
diffusion process, an iterative guidance strategy is used to generate a final
image that aligns with the textual description. Experimental results
demonstrate the effectiveness of our method in generating highly coherent
images that closely align with the given textual description. The edited images
maintain a high similarity to the features of the input image and surpass the
performance of current leading image editing methods. LayerDiffusion opens up
new possibilities for controllable image editing.

本文提出了一种基于语义的分层控制图像编辑方法 ——LayerDiffusion，通过对大规模文本转图像模型的利用，结合分层控制优化策略和分层扩散训练，实现特定主题属性的非刚性编辑和属性修改，同时保持其独特的特征并无缝地融入新背景，而在扩散过程中采用迭代引导策略生成与文字描述相符的最终图像，在实验结果中发现 LayerDiffusion 能够生成高度一致、与给定文本描述密切符合的图像，同时保持与输入图像相似的特征，超越了当前领先的图像编辑方法，开启了图像编辑的新可能性。