cMNMT: Complete MNMT

cMNMT stands for “Complete Multilingual Neural Machine Translation” which is a multilingual NMT model proposed by Google Research in 2020 and published in their paper: “Complete Multilingual Neural Machine Translation”. Multilingual Neural Machine Translation models are called complete when they are trained for all possible source-target pairs. The following figure shows the difference between the data of an English-Centric MNMT (left) and a complete MNMT (right) on a six-languages dataset:

The publishers proposed this model as they noticed that MNMT models are commonly trained on a joint set of bilingual corpora which is acutely English-Centric (English either as the source or target language). While direct data between two languages that are non-English is explicitly available at times but not used. That’s why they proposed a new way called “Multi-way Aligned Data” that can make use of these unused directions.

Multi-way Aligned Data

Multi-way Aligned Data is the method proposed by the researchers to align training examples from different language pairs when either their source or target sides are identical. So, if you have X↔Y parallel data and Z↔Y parallel data, then you can align them to get X↔Y↔Z as shown in the following example where X is “German”, Y “English” and Z “Spanish”:

In the paper, they used five more languages beside English (en) from the public WMT datasets:

  • Czech (cs): Data collected from WMT 2018.

  • French (fr): Data collected from WMT 2014.

  • German (de): Data collected from WMT 2014.

  • Spanish (es): Data collected from WMT 2013.

  • Russian (ru): Data collected from WMT 2018.

As you might now, some of the datasets provided in these benchmarks are multi-way parallel by construction. As shown in the following table, the vast majority (123 million) of the examples do only have translations into two languages while 10,000 sentences have translations in all 6 languages.

Using this data, the researchers were able to construct non-English bilingual training examples by pairing the non-English sides of two training examples with identical English translations.


cMNMT is a standard transformer-big model size that uses a vocabulary of 64,000 subword units trained for 500,000 updates using an average batch size of around 33,000 sentences (∼1 million tokens). Due to the data imbalance across languages, they used temperature-sampling to up-sample low-resource language pairs as seen in the following equation where $p$ is a language pair and $D\left( p \right)$ is the size of the available parallel data:

\[p_{p} = \left( \frac{D\left( p \right)}{\sum_{q}^{}{D\left( q \right)}} \right)^{\frac{1}{T}}\]

To study the performance of this model, it was compared with several baselines on the newstest2013 dataset; All bilingual baselines used a vocabulary of 32,000 subwords, while all multilingual baselines used a vocabulary of 64,000 subword units. All multilingual models were trained for 500,000 updates while bilingual models were trained for 400,000 steps as they converged earlier using a batch size of around 8,000 sentences (∼260,000 tokens):

  • Bilingual models:
    They trained two bilingual baselines (using either transformer-base or transformer-big) for each language pair. They experimented with several dropout rates and found that $dropout = 0.1$ works best for transformer-base while $dropout = 0.3$ works best for transformer-big. As can be seen from the following two tables, the translation quality of the non-English language pairs is far behind the ones for English-centric pairs:
  • MNMT based on English-centric data:
    **They trained a multilingual NMT model on the original WMT English-centric training data. All non-English language pairs are unseen during training and BLEU scores measure zero-shot performance. The following table shows that non-English language pairs are consistently lower than the ones for English-Centric pairs:
  • Bridging non-English language pairs:
    For the bridging approach, the source sentence cascades through the source→English and English→target systems to generate the target sentence. The following table shows that the BLEU scores for all non-English pairs are higher compared to all previous baselines:

The bridging process has several limitations:

  • Translation errors accumulate in the pipeline.

  • Decoding time gets doubled since inference has to be run twice.

  • Bridging through a morphologically low language (i.e. English), important information could be lost (i.e. gender).

The following table compares the performance of cMNMT with the best baseline model (the bridging one). As you can see, the BLEU scores for the non-English language pairs go up from at least 1.4 BLEU to 5.0 BLEU. And when comparing our cMNMT model to the English-centric baseline, we can see an average BLEU increase of 14.6 BLEU for all nonEnglish language pairs: