Here are my monthly highlights from paper machine translation and multilinguality.

A preprint from the Nara Institute of Science and Technology shows that target-language-specific fully connected layers in the Transformer decoder improve multilingual and zero-shot MT compared to the current practice of using a special token to indicate what the target language is. A very similar idea is also in a preprint from Tianjin University, but in this case, they add language-specific parameters for the other part of the Transformer decoder – the self-attention sublayers. Of course, the improvement is reached at the expense of a higher number of parameters and as it very often happens, the pre-prints do not include baselines with the same numbers of parameters as their improved models. It makes it hard to assess, what the contribution of the methods really is, but in this case, I tend to believe, it is probably better.

A preprint from the Tokyo Institute of Technology extends the possibility of subword regularization to yet another segmentation algorithm, WordPiece (it is not used much nowadays, but .e.g, the original BERT uses it). The idea is the same as for BPE Dropout (see also MT Weekly 18: stochastically decide to forbid some subwords and thus produce more, smaller units. With MT, it works comparably well to BPE Dropout. The paper also shows experiments with finetuning BERT and using the dropout is better than using the tokenizer as is. (I fine-tuned RoBERTa and especially XLM-RoBERTa many times and it never occurred to me that I should use the subword regularization from SentencePiece, I definitely should consider it the next time.)

Folks from the University of Pennsylvania and Microsoft did a follow-up of the SiXT model. The original SiXT model (see also MT Weekly 91) is a many-to-English translator using XLM-R as a universal encoder. Here, they use the zero-shot translation capabilities of the model, use additional monolingual data, and create an English-to-many translator. They test it on 25 relatively high-resource train languages and 8 low-resource test languages and quite good results.

Folks from Darmstadt, Sheffield, and Bielefeld (which does not exist, if you did not know) elaborated on BERTScore. BERTScore (see also MT Weekly 2 is a text generation and machine translation evaluation metric based on comparing BERT embeddings and computing something as F-Score from it. This pre-print shows that it works much better when it uses byT5 as the underlying model instead of BERT (so it is byte-level instead of token-level), also earlier layers work better than later ones. Unfortunately, the paper does not directly compare with COMET, which is currently considered to be the best MT evaluation metric. (Hey, and what about byT5-based COMET!)

Speaking about evaluation metrics, there was also a pre-print from Amazon that presented a very simple (“embarrassingly easy” as they say in the paper title) way of adding document context to existing neural-network-based MT evaluation metric. Long story short: just add as much context as the underlying pre-trained models can handle and it will work fine.

Google created (and hopefully will also release) a test set for multilingual visual question answering in 13 languages and presented a model with decent results using an mT5-based model. For those, that think just applying mT5 is too easy, folks from the Univerity of Zurich have a bunch of tricks based on a very nuanced understanding and intuitions of how the language-and-vision encoders work, most of them too complex for me to give them time to really understand them.