Automation of stuff that does not need to be automated at all is one of my most favorite procrastination activities. As an experienced (and most of the time unsuccessful) submitter to conferences organized by ACL (ACL, NAACL, EACL, EMNLP), I spent a lot of procrastinating time improving the Makefile compiling the papers. Here are few commented snippets from the Makefiles. Hopefully, someone finds that useful. The normal LaTeX stuff I compile the paper using latexmk. main.pdf: $(FILES) latexmk -pdflatex=”$(LATEX) %O […]

This week I will comment on a preprint Cross-lingual hate speech detection based on multilingual domain-specific word embeddings by authors from the University of Chile. The pre-print evaluates the possibility of cross-lingual transfer of models for hate speech detection, i.e., training a model in one language and testing it in a different language. Hate speech detection is a particularly tough task for model transfer because many of the words have a different meaning or at least different connotations when used […]

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 […]