This week, I will have a look at a recent pre-print that presents an interesting method for document-level machine translation that is quite different from all previous ones. The title of the paper Capturing document context inside sentence-level neural machine translation models with self-training and has authors from New York University and Google DeepMind.
The method that they present is an off-line method: you need to provide the entire document to the model, then something is happening for quite a long time and then you get the whole translation at once. First, they translate the entire document with a model that was trained on sentence-level. The translated document is then used as training data for fine-tuning the model. This is iterated several times. At the end of the process, they take the translation of the document and discard the fine-tuned model. They thus create a document-specific model but still translate on the sentence level.
They call the method self-training, but I think more accurate would be calling the trick domain self-adaptation. They report some small BLEU score improvements, which is what people usually observe when they try to improve translation using the document-level context. But here, they also present a thorough user study that shows that the fine-tuned models work better with the study participants consistently preferring the translations from the document-specific models.
The interesting question is how does it happen. The information about the topic of the documents probably gets propagated into the model from the sentences where it is clear what the document is about. This probably allows domain-specific translation even in cases when a domain-specific translation is required, but it is not clear from the source sentence. Here, the necessary information gets somehow imprinted in the model during the refinement.
I believe that what the current models are missing to achieve good document-level translation quality is first, being aware of the overall message of the document (some kind of author’s master plan) and second, being able to track sparse long-distance relations between entities in the document.
Now, the question is: is this approach capable of reaching these goals? Models that explicitly use the context (like Doc Transformer) seem to be in theory capable of doing both, however, the paper that I discussed in MT Weekly 17 shows that if they only keep nouns from the context and discard their order, they get the same improvement as when considering the entire documents. No matter, how complex the document-context information on the input is, it only considers the nouns.
It thus seems that usual document-level models only do domain adaptation and help to improve vocabulary consistency. Now, the question is: is this approach capable of something more than domain adaptation based on the content words that appear in the text? I would worry that no, that this is only a computationally more demanding way of doing what document-level translation always did. With the notable exception that this paper approach does not require document-level training data.