This week, I am will comment on a paper by authors from the University of Maryland and Google Research on reference-free evaluation of machine translation, which seems quite disturbing to me and suggests there is a lot about current MT models we still don’t quite understand. The title of the paper is “Assessing Reference-Free Peer Evaluation for Machine Translation” and it will be published at this year’s NAACL conference. The standard evaluation of machine translation uses reference translations: translations that […]

Using pre-trained multilingual representation as a universal encoder for machine translation might seem like an obvious thing to try: train a decoder into one target language using one or several source languages and you get a translation from 100 languages into the target language. This sounds great, but this is not how it works. (Or it works somehow, but not really well, I tried it myself.) Recently, I came across a pre-print where the authors figured out how to do […]

This week, I will comment on a paper by my good old friends from Charles University in collaboration with the University of Edinburgh, the University of Sheffield, and the University of Tartu within the Bergamot project. The main goal of the project is to develop a high-quality machine translation that runs locally in an internet browser and unlike services such as Google Translate or Microsoft Translator does not send any (potentially sensitive) data to any server. This is a very […]

Today, I will comment on a paper on non-autoregressive machine translation that shows a neat trick for increasing output fluency. The title of the paper is Non-Autoregressive Translation by Learning Target Categorical Codes, has authors from several Chinese private and public institutions and will appear at this year’s NAACL Conference. Unlike standard, so-called autoregressive encoder-decoder architectures that decode output sequentially (and in theory in linear time), non-autoregressive models generate all outputs in parallel (and in theory in constant time, regardless […]

This week, I will have a look at a pre-print that describes an unconventional setup for zero-shot machine translation. The title of the pre-print is Self-Learning for Zero-Shot Neural Machine Translation and was written by authors from the University of Trento. First of all, I have some doubt about this being really an instance of zero-shot learning (but it is just nitpicking, the paper is interesting regardless of the terminology). In machine learning, zero-shot learning means that a model trained […]

This week, I will have a closer look at a recent pre-print introducing an alternative for parallel data filtering for machine translation training. The title of the pre-print is Gradient-guided Loss Masking for Neural Machine Translation and comes from CMU and Google. Training data cleanness is a surprisingly important factor for machine translation quality. A large part of the data that we use for training comes from crawling the Internet, so there is no quality guarantee. On the other hand, […]