Language-Agnostic Bert Sentence Embedding
Typical ML-based paper where everything is about machine learning and nothing is about languages... The biggest issue is nothing is said about how the languages were selected. That’s the basis when dealing with multilingual languages as the features they share or not is important and will impact the results. In this respect the last figure is the more interesting one: it mentions ido and interlingua, two constructed languages. Why does they get included since NLP is supposed to deal with natural languages? It not a bad choice per se, but the selection criteria needs to be explain more than just picking languages to get a large number on a research paper.<p>There is also no reflection on why some languages got better results than others. Again looking at the last figure in the top three best performing languages with no data two are  Sinitic languages (Cantonese and Wu) which likely happens since their closeness to standard Chinese for which there is huge ressources available for training. On the reverse Breton for which there is probably very few apparented language data in the initial models and training set besides Irish get very poor results, which tends to show the model actually don’t transfer very well if at all...