Everyone who followed natural language processing on Twitter last week must have noticed a paper called BPE-Dropout: Simple and effective Subword Regularizations that introduces a simple way of adding stochastic noise into text segmentation to increase model robustness. It sounds complicated, but it is fairly easy.

As we already discussed here, two weeks ago, neural machine translation systems can only work with limited vocabulary with at most tens of thousands of entries. The standard way of dealing with this problem is using a vocabulary consisting of so-called sub-word units rather than “normal” words. Before training a system, we say in advance that we want a vocabulary of for instance 30,000 units and use a statistical heuristic that produces a sub-word vocabulary of the desired size.

The most commonly used algorithm is called byte-pair-encoding (BPE; invented in 1994 originally for data compressing, discovered for machine translation in 2016) that iteratively creates new symbols by merging the most frequent pairs of existing symbols until it reaches the desired number of symbols. In practice, it means that we start with isolated characters and gradually add the most frequent groups of characters into our vocabulary. A BPE vocabulary of 30,000 symbols contains the most frequent words intact. Less frequent words get split into smaller units that seem to at least partially reflect the language morphology. The least frequent words got split into characters.

A drawback of the approach is that the model learns how smaller units compose into words only using the relatively infrequent words and thus does not have much material to learn from. This observation motivates the main idea of the paper: if we from time to time forget that we can merge some groups of characters, the model will have a better chance to learn something about how the words are composed (morphology) and also something about transliteration. This is particularly useful for proper names that are not really translated, but they are differently inflected in different languages. It thus makes perfect sense to learn the declination patterns on frequent words (which the model memorizes anyway) and at the inference time, apply it to the rare words (which really need it).

The idea is not entirely new. A similar trick is also possible with the SentencePiece segmentation by Google from 2018. However, it is a rather complicated thing and to be honest I never found time to fully understand it. With BPE Dropout, this sampling is extremely simple. In the paper, they use a formulation with removing merges from a list of possible merges. I think a better description is: every time you are about to decide which two symbols you are going to merge, for each of the possible merges, flip a coin and based on that, keep it or remove it.

Try it yourself! The demo shows how the text gets segmented when using up to 32k merges trained WMT14 English-German parallel data and how the segmentation changes when the dropout is applied.

The results in terms of translation quality are quite good when the BPE-dropout is applied. It gives around 1 BLEU points improvement for most language pairs. But most importantly, models trained with BPE-dropout seem to be very robust towards misspellings. Normally, if you misspell a frequent word, it gets segmented in a way that was totally alien to the model. With BPE-dropout, it gets split into something similar to what the model had to deal with many times during training.

update on 14.11.2019: I removed a paragraph about my preliminary experiments. I had a bug both in the experiment and in the demo here, so claimed nonsense. Thank you, Дима, for pointing it out in the discussion and submitting PR fixing the demo.

BibTeX Reference

@misc{provilkov2019bpedropout,
title={BPE-Dropout: Simple and Effective Subword Regularization},
author={Ivan Provilkov and Dmitrii Emelianenko and Elena Voita},
year={2019},
eprint={1910.13267},
archivePrefix={arXiv},
primaryClass={cs.CL}
}