Several weeks ago, I discussed a paper that showed how parallel data between two languages can be used to improve unsupervised translation between one of the two languages and a third one. This week, I will have a look at a similar idea applied in a different context. I am going to talk about multimodal translation: translation of image captions when you also have access to the original image. Multimodal translation was my Ph.D. topic, so I love to read any news from this topic.
In the paper I am going to discuess today, the setup is slightly different: we have monolingual data in two languages, but in addition to that, we have captioned images in the languages, but no parallel data. The goal is to learn to translate between the languages. The title of the paper that attempts to solve this task is Unsupervised Multimodal Neural Machine Translation with Pseudo Visual Pivoting, it has authors from CMU and Monash University and will appear at this year’s virtual ACL.
The main idea of the paper following: in the described data setup, we can train image captioning in both languages. Using the image captioning system, they can generate synthetic source sentences and use them to train a multimodal translation system using both image and source sentence as its input.
And these translation systems can be used to generate another synthetic data for standard back-translation (in the paper, they call it quite cryptically Pivoted Captioning for Back-Translation).
And besides, they add a third artificial task to keep everything together. They want captions of the same image to be translations of each other, i.e., get a high probability in the translation model (this is called Pivoted Captioning for Paired-Translation).
Everything sounds like a very straightforward application of ideas that are around for quite a while, but it has a secrete ingredient that makes it work efficiently. It is the way they represent the image that they call “Visual-Semantic Embedding”. They run an object detector for the images and represent each object by the penultimate layer of the object detection network. Then, they learn a projection of the object representations such that hidden states of the text encoder can be expressed as a linear combination of the projected object representations.
Compared to the standard unsupervised translation that relies on iterative back-translation, this way of training improves the translation quality quite a lot. On the other hand, the standard unsupervised methods are designed to work with much larger datasets and these data come from a quite narrow domain. When they integrate this training machinery into a supervised learning setup in addition to standard training examples, they reach the state-of-the-art results.
We can of course object, that these results only hold for the Multi30k dataset that consists mostly of simple sentences that use only concepts that do have a visual counterpart which is indeed a limited use language. Sentences like: “I am hungry.”, “I cannot have your pain.” or “All human beings are born free and equal in dignity and rights.” have no direct visual representation (in the sense that there is no photograph that could be captioned like this).
However, I believe that being able to ground even the simple concepts in vision has the potential to help to get truly multilingual language representations. The famous book Metaphors we live by promotes the idea that all concepts are partially understood in terms of other concepts and ultimately grounded in basic physical experience. Complex and abstract words are understood in terms of a more familiar concept that we have more direct experience with. For instance, a rational argument is partially understood as a war: you can attack your opponent, partially as a journey: it can lead nowhere, and partially as other concepts. If we succeed in grounding the basic concepts in the visual modality in a language-agnostic way, this might be the representation that we might use to build multilingual models covering even more abstract corners of human languages.