More than half a year ago (in MT Weekly 10), I discussed massively multilingual models by Google. They managed to train two large models: one translating from 102 languages into English, the other one from English into 102 languages. This approach seemed to help a lot for low-resourced languages that probably can benefit from the presence of related languages in the training data.
In a new preprint of their ACL 2020 paper, guys from the University of Edinburgh show several tricks that not only improve the multilingual translation but also make the models reasonably large. The title of the paper is Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation. What I personally value the most about the paper are the strong intuitions about what is going on inside the models.
The tricks are based on introducing language-specific components in the model. Ultimately, all model parameters can be language-specific, but this would that model is not really multilingual and cannot benefit from synergies between related languages. The opposite extreme is what Google did in its original multilingual approach: brute force with data and computation power without modifying the Transformer architecture. The new Edinburgh paper succeeds in setting a middle course between those two extremes and introduces language-specific layer normalization and encoder state projection.
Especially the trick with layer normalization is quite a surprise for me. I always viewed layer normalization as a purely technical trick to force the neuron activation to stay in a reasonable range, so we can connect layers with residual connections (and thus avoid the vanishing gradient problem). After normalizing the activations to have a zero mean and unit variance, layer normalization adds a learned bias vector. Here, this serves as a kind of language embedding, constantly reminding the following layers, how they should treat the input. The encoder is followed by a linear projection.
My interpretation is: let the encoder do some language-specific things, but not too much, just give the layers a small hint, what they should do with the input. After the encoding finishes, just get rid of the language-specific stuff and project it to a language-neutral form, just as the decoder likes it.
Unlike Google, these experiments were done on publicly available data. I believe this is an important signal to researchers that these stunningly interesting experiments can be done everywhere, not only with Google-scale equipment. It is tempting to say that what was considered sci-fi a few years ago, can now run even on your desktop machine.
The multilingual model is better than bilingual models on average, but for the high-resource languages, the translation quality is much worse than the quality of the specialized bilingual models. To match the performance of the bilingual models in the multilingual setup, they need a 4 times bigger model, but still with approximately half of the parameters of Google’s model.