Cross-lingual text representations have gained popularity lately and act as
the backbone of many tasks such as unsupervised machine translation and
cross-lingual information retrieval, to name a few. However, evaluation of such
representations is difficult in the domains beyond standard benchmarks due to
the necessity of obtaining domain-specific parallel language data across
different pairs of languages. In this paper, we propose an automatic metric for
evaluating the quality of cross-lingual textual representations using images as
a proxy in a paired image-text evaluation dataset. Experimentally,
Backretrieval is shown to highly correlate with ground truth metrics on
annotated datasets, and our analysis shows statistically significant
improvements over baselines. Our experiments conclude with a case study on a
recipe dataset without parallel cross-lingual data. We illustrate how to judge
cross-lingual embedding quality with Backretrieval, and validate the outcome
with a small human study.

本文提出了一种使用图像作为代理的自动度量标准，评估跨语言文本表示的质量，实验表明，Backretrieval 能够高度相关地与标注数据的正确度量相关，是评估跨语言文本表示质量的一种有效方式。

Backretrieval: 一种基于图像的跨语言文本表示的评估度量方法，不需要平行语料库

Backretrieval: An Image-Pivoted Evaluation Metric for Cross-Lingual Text  Representations Without Parallel Corpora

A significant roadblock in multilingual neural language modeling is the lack
of labeled non-English data. One potential method for overcoming this issue is
learning cross-lingual text representations that can be used to transfer the
performance from training on English tasks to non-English tasks, despite little
to no task-specific non-English data. In this paper, we explore a natural setup
for learning cross-lingual sentence representations: the dual-encoder. We
provide a comprehensive evaluation of our cross-lingual representations on a
number of monolingual, cross-lingual, and zero-shot/few-shot learning tasks,
and also give an analysis of different learned cross-lingual embedding spaces.

探索了一种使用双编码器学习跨语言句子表示的自然环境，以克服多语言神经语言建模中标记化非英语数据的缺乏，并在许多单语、跨语言、零样本 / 少样本学习任务上对跨语言表示进行了全面评估，并分析了不同的跨语言嵌入空间。