After a short break, MT weekly is again here, and today I will talk about a paper “CSP: Code-Switching Pre-training for Neural Machine Translation” that will appear at this year’s virtual EMNLP. The paper proposes a new and surprisingly elegant way of monolingual pre-training for both supervised and unsupervised neural machine translation.

The idea is quite simple. The model they use is the standard Transformer; all the magic is how the model is trained. First, it is pre-trained on synthetic “half-translations” created from monolingual data. It is then trained either on parallel data in a supervised way or by iterative back-translation on monolingual data in the unsupervised setup. What I just called the “half-translations” are sentences where some of the words were replaced by their dictionary translations. They call the preparation of the synthetic data self-confidently code switching (therefore the paper title), but in real code switching, the foreign words would be used more cleverly. The good thing is that the bilingual dictionary can be generated from bilingual word embeddings that can be trained without bilingual supervision.

In the unsupervised setup, the pipeline is the following:

  • Train word embeddings for both the source and the target language.

  • Do an unsupervised mapping of the embeddings, and create a probabilistic dictionary of between the languages. For those who are not familiar with this: unsupervised bilingual embeddings assume that similar things are said similarly often across languages, so the word embedding spaces should also be similar. We only need to find how to rotate and scale the vector spaces so they match each other.

  • Use this dictionary to generate synthetic half-translated data: randomly replace a half of the words with their translation counterparts from the estimated dictionary.

  • Train a Transformer that translates from the synthetic half-translated data into the original sentences. This can be viewed as a sort of bilingual denoising. After this step, there is already a first-iteration translator that already can somehow translate between the languages.

  • Start iterative back-translation: Use the current translator to translate the monolingual data into the target language. In the next iteration, they will be used as training data in the opposite direction.

This procedure gets the state-of-the-art results in unsupervised machine translation. It is, in fact, quite similar to the previous best approach called MASS (Masked Sequence to Sequence Pre-training for Language Generation). MASS did the monolingual pre-training as a pure denoising autoencoder with some tokens from the input sentence replaces by [MASK] tokens. In MASS, the decoder learns well the target language. Simultaneously, the encoder is forced to represent both languages similarly because the encoder state poses an information bottleneck of the architecture. The bilingual supervision comes later only in the iterative back-translation phase. CSP is better (at least in my opinion) because the bilingual supervision comes from the bilingual embeddings and the model gets the supervision already during the pre-training state.

Similarly to MASS, CSP can also bring some supervised translation improvements, especially when only little training data is available. But I think the unsupervised use case is much more important in this paper.