Pre-trained multilingual representations promise to make the current best NLP model available even for low-resource languages. With a truly language-neutral pre-trained multilingual representation, we could train a task-specific model for English (or another language with available training data) and such a model would work for all languages the representation model can work with. (Except that by doing so, the models might transfer Western values into low-resource language applications.)
There are several multilingual contextual embeddings models (such as multilingual BERT or XLM-R) covering over one hundred languages that claim to be capable of being language-neutral enough to work in this so-called zero-shot learning setup (i.e., a model is trained on one language, applied on another one). The models are very good indeed, but they are still quite far from being language neutral. A recent pre-print from the Technical University Darmstadt and the University of Copenhagen offers several remedies for that. The title of the paper is Inducing language-agnostic multilingual representations.
The paper offers three options on how to do that:
Fine-tune the model on parallel data with an added constraint that matching word across languages should receive similar representations. (This requires some parallel data for training.)
Normalize the resulting vectors, in this case using batch-normalization.
Normalize the input text. (This requires knowing the language identity and creating language-specific rules for the text normalization. I am not really sure what the takeaways from the experiments should be, so I do not discuss it further.)
Both multilingual BERT and XLM-R are trained on monolingual data only. The pre-training model gets a noisy input with some of the words masked out and it is supposed to reconstruct what were the words that were masked. There is actually nothing telling the model that something is the equivalent (or similar) across languages. When training the models, we silently assume that given the limited capacity of the model, it is efficient to find patterns with that repeat and apply across languages and therefore arrive at some common representation. This, however, only leads to limited language neutrality and this is what this paper tries to fix by model finetuning.
In the paper, they select two tasks to test out the suggested approaches: reference-free machine translation evaluation and cross-lingual natural language inference.
The reference-free MT evaluation is a task of estimating how good machine translation is without having access to the reference translations. This sounds similar to MT quality estimation, but surprisingly, it is a different task. All quality estimation and reference-free evaluation systems can be used for both. The difference between the tasks is in how these two are evaluated (and what they are trained for if they are trained). Machine translation evaluation is evaluated by computing correlation between human judgment about the translation quality (when the evaluators can check the reference translation), quality estimation is evaluated by computing a correlation with how many edit operations are required to post-edit the output sentence to make it correct.
The other task this paper uses to evaluate the language neutrality of the multilingual representations is cross-lingual natural language inference. To be honest, I do not like this task much. The natural language inference itself is quite an artificial task. The goal of the task is to say for a pair of sentences if the second one entails from the first one in a sort of predicate logic view. For instance a sentence “Three dogs are running on a beach.” entails “There are two dogs on the beach.” Technically, it is true, but it is not how language is usually used, a normal person would react: “No, look, there are three of them.” Therefore, I would worry that representation evaluation using such a task would prefer representations that do not capture how language is naturally used. The cross-lingual version of the task is even less natural: the two sentences are in different languages.
Anyway, back to the paper: it seems that the finetuning on parallel data that enforces representation similarity of aligned words together with batch normalization of the output improves the representation language neutrality by a great deal for both of the tasks. Moreover, the batch normalization and the finetuning using word alignment seems to be complementary. The use of batch normalization is quite clever: previous work showed that centered (towards a language-specific zero mean) representations are more language-neutral. Finetuning with the batch normalization layer has the same effect, but it no longer requires knowing the language identity in advance.
One important question that the paper leaves unanswered is how the finetuning affects the languages that are not in the parallel data for finetuning. Do they get also represented in a more language-agnostic way? If yes, it would be great news. Also, it would be interesting to see how much the effect depends on the size of the parallel data.
Anyway, it is great to see multilingual contextual embeddings getting more language-neutral. I am really looking forward to seeing the language-neutral representation applied in zero-shot machine translation (such as any of 100 mBERT languages into English) and unsupervised machine translation.