To enable one-to-one translation from one language to another, Google AI has launched a technology that could allow users to utilise images to express words that may not have a direct translation into a target language.

MURAL is a representation model for image–text matching that uses multitask learning applied to image–text pairs in combination with translation pairs covering 100+ languages.

With current advances in neural machine translation and image recognition, it is possible to reduce ambiguity in translation by presenting a text paired with a supporting image. Prior research has made much progress in learning image–text joint representations for high-resource languages, such as English. These representation models strive to encode the image and text into vectors in a shared embedding space, such that the image and the text describing it are close to each other in that space. For example, ALIGN and CLIP have shown that training a dual-encoder model (i.e., one trained with two separate encoders) on image–text pairs using a contrastive learning loss works remarkably well when provided with ample training data. 

Unfortunately, such image–text pair data does not exist at the same scale for the majority of languages. In fact, more than 90% of this type of web data belongs to the top-10 highly-resourced languages, such as English and Chinese, with much less data for under-resourced languages. To overcome this issue, one could either try to manually collect image–text pair data for under-resourced languages, which would be prohibitively difficult due to the scale of the undertaking, or one could seek to leverage pre-existing datasets (e.g., translation pairs) that could inform the necessary learned representations for multiple languages.

This is important because for many concepts, there is no direct one-to-one translation from one language to another, and even when there is, such translations often carry different associations and connotations that are easily lost for a non-native speaker. In such cases, however, the meaning may be more obvious when grounded in visual examples. Take, for instance, the word "wedding". In English, one often associates a bride in a white dress and a groom in a tuxedo, but when translated into Hindi (शादी), a more appropriate association may be a bride wearing vibrant colors and a groom wearing a sherwani. What each person associates with the word may vary considerably, but if they are shown an image of the intended concept, the meaning becomes more clear.Empirically, MURAL shows consistent improvements over state-of-the-art models, other benchmarks, and competitive baselines across the board. Moreover, MURAL does remarkably well for the majority of the under-resourced languages on which it was tested. Additionally, MURAL representations can learn linguistic correlations;.

The MURAL architecture is based on the structure of ALIGN, but employed in a multitask fashion. Whereas ALIGN uses a dual-encoder architecture to draw together representations of images and associated text descriptions, MURAL employs the dual-encoder structure for the same purpose while also extending it across languages by incorporating translation pairs. The dataset of image–text pairs is the same as that used for ALIGN, and the translation pairs are those used for LaBSE.

MURAL solves two contrastive learning tasks: 1) image–text matching and 2) text–text (bitext) matching, with both tasks sharing the text encoder module. The model learns associations between images and text from the image–text data, and learns the representations of hundreds of diverse languages from the translation pairs. The idea is that a shared encoder will transfer the image–text association learned from high-resource languages to under-resourced languages. We find that the best model employs an EfficientNet-B7 image encoder and a BERT-large text encoder, both trained from scratch. The learned representation can be used for downstream visual and vision-language tasks.