Mohamed Anwar

I'm an ex-AI research resident at Meta

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3 mins read

Published by: Google Research

nmT5: NMT + Multilingual T5

nmT5 stands for “NMT + Multilingual Text-to-Text Transfer Transformer” which is an attempt to improve the performance of the mT5 model by incorporating parallel data into pre-training. This model was proposed by the same authors from Google Research as the mT5 paper. In 2021, it was published in this paper: nmT5 - Is parallel data still relevant for pre-training massively multilingual language models?.

A little bit of background: the mT5 model was pre-trained on mC4 dataset (a multilingual version of the C4 corpus) with a masked language modeling “span-corruption” objective, where the encoder is fed a chunk of text with random spans replaced with a mask token, and the decoder must reconstruct the masked-out tokens. In this paper, they are trying different objectives to incorporate parallel data into pre-training:

  • TLM (Translation Language Modeling):
    This objective was first proposed by the XLM model and was used for encoder only pre-training. In this paper, they extended it to the encoder-decoder setting.
  • NMT (Neural Machine Translation):
    The input is the source text and the target is its translation. A language code is prefixed to the input to inform the model of the target language.
  • Denoised-NMT:
    Similar to NMT, but with mask spans in the source sentence. The model must now learn to implicitly perform language modeling of the source language while translating into the target language.
  • Denoised-NMT+LM:
    Similar to Denoised-NMT, but instead of implicit language modeling, the model must explicitly predict the source text in addition to the translation. The target is a concatenation of the translation and source sentence, while the input is the masked source sentence.

Note:
nmT5 is the mT5 model with the NMT objective.

Results

In this paper, they used the mT5-Large model to perform the following experiments, which is a 24 layer encoder-decoder transformer model. Instead of training a new model from scratch, they started from the publicly available mT5-Large checkpoint - which has been trained for over 1 trillion tokens - and did a second stage pre-training with a mix of monolingual and parallel data.

For pre-training, they used monolingual data from mC4 and parallel data from OPUS-100 which contains 55M translations covering 100 languages. The mC4 corpus consists of unlabeled web text covering 101 languages, of which 81 overlap with the OPUS-100 languages.

Starting from publicly available mT5-Large checkpoints, they pre-trained for 100K steps with a mix of monolingual and parallel objectives. The parallel data is mixed into monolingual data at a $10\%$ ratio, which amounts to roughly 4 passes over the OPUS-100 corpus. Examples from each language pair were sampled using the same language sampling distribution as mT5 with $\alpha = 0.3$.

Pre-training was done with a batch size of 1M tokens and fine-tuned with $131,072$ tokens, with a constant learning rate of $0.001$. For fine-tuning, they fine-tuned for $10,000$ steps for TyDiQA, MTOP, NER and $25,000$ for WikiLingua, since it is a much larger dataset. Checkpoint selection is done based on the validation set.

The following table shows the results averaged across all the languages. Overall, adding parallel data through neural machine translation objectives improves scores for all 4 tasks, with the NMT objective performing the best.

From the past table, we can see that all NMT-based objectives shows gains over mT5 across all tasks. Among these, NMT averages the best among all other objectives leading to 7.2 higher scores averaging across all four tasks:

Model Size

Researchers of mT5 found out that cross-lingual performance of language models increases monotonically with model size, that’s why the mT5-XXL had the highest performance across five out of six tasks.

To study the impact of model capacity here, the researchers also experimented with larger model sizes. Using the mT5-XL size (3.7B params, 3× larger than mT5-Large), they observed gains for all tasks with nmT5. However, the magnitude of the gains is largely diminished, hinting that the need for parallel data reduces as model capacity increases.

This finding is particularly promising for low-resource languages, where it is difficult to obtain high-quality parallel data. At the same time, nmT5-Large substantially reduces the performance gap between mT5-Large and mT5-XL, covering 70% of the headroom. Since bigger models are expensive to train and even more expensive to deploy, this opens up avenues for effectively using parallel data to improve performance of smaller language models.