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

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