Phanxuan Phuc

fakerphan

I’m a AI Engineer in Vin Bigdata. And I summarize research papers in these topics:

4 mins read

Published by: FAIR

mBART

mBART stands for “Multilingual Bidirectional Auto-regressive Transformer” which is a multilingual NMT model proposed by FacebookAI in 2020 and published in their paper: “Multilingual Denoising Pre-training for Neural Machine Translation”. The official code for this paper can be found in the fairseq GitHub repository: mbart. mBART is the first method for pre-training a complete sequence-to-sequence model by denoising full texts in multiple monolingual data, while previous approaches have focused only on the encoder/decoder.

The whole idea behind mBART is to apply the BART architecture to large-scale monolingual corpora across many languages where the input texts are noised by masking phrases and permuting sentences. This will create a universal language model that is able to denoise this input text which makes translation from one language to another achievable.

Pre-training

The data used for pre-training mBART is monolingual data of 25 different languages collected by common crawl. The table below shows the size of the collected data.

The model used in this paper is a standard sequence-to-sequence Transformer architecture from fairseq repository with:

  • 12 layers of encoder and 12 layers of decoder. The model’s dimension is 1024 on 16 heads (∼ 680M parameters).

  • They included an additional layer-normalization layer on top of both the encoder and decoder, which they found stabilized the training.

  • The model was trained for 500 steps using Adam optimizer with 0.000001 learning rate and 0.98 beta2 and linear learning rate decay. The training took around 2.4 weeks despite using 256 Nvidia V100 GPUs.

  • They started the training with dropout 0.1 and reduced it to 0.05 at 250K steps and 0 at 400K steps.

    Regarding the encoder:

  • For each instance, they packed as many consecutive sentences as possible sampled from the corresponding corpus of a certain language <LID>, until either it hits the document boundary or reaches the 512 max token length.

  • Sentences in the instance are separated by the end of sentence (</S>) token.

  • The language ID token is appended at the end of the instance.

Regarding the decoder:

  • The decoder input is the original text with one position offset which is the language id symbol <LID> as it’s used as the initial token to predict the sentence.

Following the BART paper, they uses two types of noise:

  • Text Infilling: They masked around 35% of the words in each instance by random sampling a span length according to a Poisson distribution (λ = 3.5).

  • Sentence Permutation: They also permuted the order of sentences within each instance.

Fine-tuning

We fine-tune our multilingual pre-trained models on a single pair of parallel data, feeding the source language into the encoder and decoding the target language. Note that they used monolingual data for pre-training and parallel corpus for fine-tuning. In the paper, they used different pre-trained models to compare between while fine-tuning:

a. BART-En/Ro: Baseline model for just one pair (English - > Romanian).

b. mBART25: Uses all 25 languages.

c. mBART06: Uses just 6 European languages [Ro (Romanian), It (Italian), Cs (Czech), Fr (French), Es (Spanish) and En(English)].

d. mBART02: They used 4 different versions of this model; one for -> each pair of the following pairs: ( English-German, -> English-Romanian, English-Italian).

e. Random: don’t know exactly :D.

For all these models, they trained using 0.3 dropout, 0.2 label smoothing, 2500 warm-up steps, 3e−5 maximum learning rate with a maximum of 40K training updates for all low and medium resource pairs and 100K for high resource pairs. The final models are selected based on validation likelihood. For decoding, we use beam-search with beam size 5 for all directions.

All models use the same vocabulary. Not all tokens will frequently occur in all pre-training corpora, but later experiments show that this large vocabulary can improve generalization in multilingual settings even for unseen languages.

The following table shows a comparison between mBART25 and Random baseline on low/medium-resource parallel corpora. Using mBART25 weights shows gains on all the low and medium resource pairs. While fine-tuning fails in extremely low-resource setting such as En-Gu, which only have roughly 10k instances:

The following table shows a comparison between mBART25 and Random baseline on high-resource parallel corpora in the direction of En --> X. As you can see, there weren’t consistent gains, and pre-training slightly hurts performance when >25M parallel sentence are available as if they wash out the pre-trained weights completely:

Note:
In the paper, they tried using back-translation with low-resource language and mBART25 and it did improve.

Analysis

In my opinion, this is one of the best parts about this paper where they try to answer the most common questions regarding their paper:

  • How many languages should you pre-train on?
    When monolingual data is plentiful, pre-training pre-training on multiple languages slightly hurts the final results (<1 BLEU). On the other hand, when monolingual data is limited, pre-training on more languages helps.

  • Is pre-training essential for using mBART?
    Without any pre-training, mBART tends to overfit and perform much worse than the baseline.

  • How many pre-training steps are needed?
    After just 25K steps, pre-trained models outperform the best baseline. The models keep improving by over 3 BLEU for the rest of steps and have not fully converged after 500K steps.

  • Is fine-tuning helps with unseen pre-trained languages?
    Surprisingly, yes! They performed an experiment where they used mBART02 and mBART06 to translate En-Ar (English-Arabic), En-De (English-German), and En-DI (English-Dutch) where they weren’t in the pre-training data. And these two models showed competitive results compared with mBART25. This result suggests that the pre-trained Transformer layers learn universal properties of language that generalize well even with minimal lexical overlap.

  • How does mBART behave with unseen Source or Target languages in the pre-training?
    If both sides are unseen, the performance (in terms of difference from mBART25) is worse than where at least one language is seen during pre-training. Fine-tuning unseen languages on source side is more difficult than the target side. Although mBART06 outperforms mBART02 by a margin on when the source side is missing.