This is the first post from a series in which I will try to come up with summaries of some of the latest papers and other news on machine translation. The main goal of this exercise is to force myself to read new papers regularly and more importantly not to forget what they were about.

I will start with a paper called Synchronous Bidirectional Neural Machine Translation that appeared this week in the TAACL journal.

In the last few years, we almost got used to that there is an innovation that improves machine translation by quite a large margin every year. It was the attention model in 2015, training data processing innovations like subword units and back-translation in 2016, the game-changing Transformer architecture in 2017. The year 2018 brought some practical tips on how to train the Transformer model properly, some work on speeding-up the translation, and of course amazing work on unsupervised MT – but pretty much nothing changed in the good old supervised MT. This new paper seems to me to be a hot candidate to be an idea that might survive the year 2019 and became a part of MT best practices. The idea of the paper is very simple: don’t generate the target sentence left-to-right only, do it from both sides simultaneously.

Alternative ways of decoding the target sentence are among the most popular topic in MT research these days. Last year, there were several papers on methods for generating all target words in parallel and thus save some decoding time. Earlier this year, there were several groups that independently came with the idea to generate the target sentence in a random order, so they can generate the easier parts first and thus be more informed when generating the more difficult parts.

Synchronous Bidirectional Neural Machine Translation returns back to sequential text generation but does it from left and right at parallel. It is a well-known fact that beginnings of the sentences tend to end up better than what follows. Researchers from the University of Edinburgh used this observation already in their winning submission at WMT in 2016 when they generated a list of candidate translations using a left-to-right and re-scored them using a right-to-left model.

This model presented in this week’s paper follows a similar line of thinking. During the standard left-to-right decoding, a newly generated word is conditioned on the already generated left context (and of course on the source sentence). The proposed model uses two decoders working synchronously. The first decoder is conditioned not only on what it previously decoded but also on the output of the second decoder.

The decoding is shown in the following animation. Circles correspond to words, arrows denote conditioning.

Although this idea seems very simple, it requires non-trivial tricks to be trained properly. If ground truth words were used as simulated outputs of the other-side decoder, the only thing the model would learn would be copying from the other decoder as much as possible and do nothing on its own. Instead, the authors first train two teacher models, one left-to-right and one right-to-left, and use them while training the bidirectional model to simulate the decoder running from the other side.

Also, the decoding takes of course twice as long, which makes is kind of unsuitable in use cases when the latency is a critical issue. However, the improvement in the translation quality looks very promising. On the WMT14 data, they report 2 BLEU points improvement over the state-of-the-art Transformer model (which is quite a lot). Since last year, it is obvious that the translation quality of the standard Transformer model can improve a lot when trained properly (and for an incredibly long time). The main question for this cool new model is, how it will work when combined with the training tricks.

BibTeX reference

@article{zhou2019synchronous,
title = "Synchronous Bidirectional Neural Machine Translation",
author = "Zhou, Long  and
Zhang, Jiajun  and
Zong, Chengqing",
journal = "Transactions of the Association for Computational Linguistics",
volume = "7",
month = mar,
year = "2019",
url = "https://www.aclweb.org/anthology/Q19-1006",
doi = "10.1162/tacl_a_00256",
pages = "91--105",
}