MTL: Multi-task Learning

MTL stands for “Multi-task Learning” and it is a framework that jointly trains multilingual neural machine translation (MNMT) models on bitext data and monolingual data. The bitext data is used for the translation task while the monolingual data is used for the denoising language modeling tasks. This framework was proposed by Microsoft in 2020 and published in their paper: Multi-task Learning for Multilingual Neural Machine Translation.

Remember that multi-task learning (MTL) trains the model on several tasks to improve generalization performance. In this paper, the translation task is the main task combined with two denoising language modeling tasks that help improving the quality of the translation task. The two denoising tasks are:

  • Masked Language Model (MLM):
    This task was first introduced in the BERT model where tokens are randomly masked and fed into the model and the model attempts to predict the masked tokens based on their context. BERT was an encoder-only architecture. To adapt this idea to the encoder-decoder architecture, they added an additional output layer (LM Head) during training which was dropped during inference.
  • Denoising Auto-encoding (DAE):
    This task was first introduced in this paper back in 2008 where the target-side sentence is fed to the encoder along with the Language ID and the decoder attempts to reconstruct the original sentence. In this paper, they introduced three different types of noise functions:

    • Text Infilling: Same as mBART model, they randomly sampled text spans from the input with span lengths drawn from a Poisson distribution ($\lambda = 3.5$) and replaced all words in each span with a single masking token.

    • Word Drop & word blank: They randomly sampled words from the input sentence which were either removed or replaced with blanking tokens for each token position.

    • Word Swapping (Shuffling): Following this paper, they slightly shuffled the input sentence where a word at position $i$ can’t be further than either $i - k$ or $i + k$, and $k = 3$ is the maximum swapping distance.

In the training process, the two self-learning objectives are combined with the cross-entropy loss for the translation task:

\[\mathcal{L} = \mathcal{L}_{\text{MT}} + \mathcal{L}_{\text{MLM}} + \mathcal{L}_{\text{DAE}}\]

A language ID symbol $\lbrack LID\rbrack$ of the target language is appended to the input sentence in the translation and DAE tasks.

Dynamic Noise Ratio

Training algorithms perform better when starting with easier tasks. And gradually moving to harder ones and increasing the learning difficulty can potentially help avoid saturation. Therefore, the researchers of this paper has proposed a Dynamic Noise Ratio parameter that can balance the difficulty level of MLM and DAE tasks by using a noising ratio $R$ as a function of the training epochs $k$:

\[R\left( k \right) = \min\left( R_{m},\ \left( k - 1 \right)\frac{R_{m} - R_{0}}{M} + R_{0} \right)\]

Where $R_{0}$ and $R_{m}$ are the lower and upper bound for the noising ratio respectively and $M$ is the number of warm-up epochs.

In case of MLM, the noising ratio $R$ refers to the masking ratio in MLM; the masking ratio is how many sequences will be masked. In BERT, the masking ratio was 15%. In the case of DAE, the noising ratio $R$ is the length of the blank span that needs to be filled.

Dynamic Temperature Sampling

One serious yet common problem for MNMT is data imbalance across different languages. Training the model with uniform data distribution would starve the low-resource language pairs since they are less probable to be chosen. One approach to solve that issue is a temperature-based sampling . Temperature sampling is an effective heuristic to up-sample the the probability of low-resource pairs. It works like so; for language pair $l$ with bitext corpus $\left| D_{l} \right|$, the probability of sampling an instance of the same language pair is:

\[p_{l} \propto \left( \frac{\left| D_{l} \right|}{\sum_{k}^{}\left| D_{k} \right|} \right)^{\frac{1}{T}}\]

While this sampling method improves translation quality for low-resource languages, performance gradually decreases for high resource languages. To get over that, the researchers of this paper proposed a dynamic temperature data sampling method that samples more high-resource language pairs in the early stage of training and gradually shift more attention to the low-resource languages as shown in the following function where the temperature $T$ is a function of the number of training epochs $k$ and $N$ is the number of warm-up epochs. Also, they added a lower-bound $T_{0}$ and a higher-bound $T_{m}$ for the temperature.

\[T\left( k \right) = \min\left( T_{m},\ \left( k - 1 \right)\frac{T_{m} - T_{0}}{N} + T_{0} \right)\]

From the past equation the sampling temperature starts from a smaller value $T_{0}$, resulting in sampling leaning towards true data distribution and gradually increases low-resource languages more to avoid them getting starved.

The following graph shows the performance gain of data sampling strategies compared to the standard temperature sampling ($T = 5$) which clearly shows that dynamic temperature sampling has better performance on low-resource language pairs. All results are reported as ∆ BLEU relative to the corresponding bilingual baseline on validation sets:


As said earlier, the MTL framework jointly trains multilingual neural machine translation (MNMT) model on bitext data and monolingual data. The bitext data is used for the multilingual translation task and the monolingual data is used for the denoising tasks.

Bitext data training data comes from the WMT corpus; they concatenated all resources except WikiTitles provided by WMT of the latest available year and filtered out the duplicated pairs and pairs with the same source and target sentence.

Then, they tokenized all data with the SentencePiece model forming a vocabulary shared by all the source and target languages with $32k$ tokens for bilingual models ($16k$ for Hi and Gu) and $64k$ tokens for multilingual models. For validation, they randomly sampled $1,000$ sentence pairs from each individual validation set and concatenated them to construct a multilingual validation set.

The following figure shows a list of 10 languages ranked by the size of the bitext corpus translating to/from English:

Regarding the monolingual data, they mainly used data from NewsCrawl after applying a series of filtration rules to remove the low-quality sentences, including duplicated sentences, sentences with too many punctuation marks or invalid characters, sentences with too many or too few words, etc. Then, they randomly select $5M$ filtered sentences for each language. For low-resource languages without enough sentences from NewsCrawl, they leveraged data from CCNet.


Regarding the multilingual models, they used the fairseq implementation of the Transformer-big setting with a 6-layer encoder and decoder. The dimensions of word embeddings, hidden states, and non-linear layer were set as $1024$, $1024$ and $4096$ respectively, the number of heads for multi-head attention was set as $16$.

For the low-resource bilingual models (Tr, Hi, and GU), they used a smaller model setting with 3-encoder and decoder layers, $256$ embedding and hidden dimension to avoid overfitting and acquire better performance.

All models were optimized with Adam with $\beta_{1} = 0.9$, $\beta_{2} = 0.98$. They set the learning rate schedule as the standard transformer paper with initial learning rate $5 \times 10^{- 4}$. Label smoothing was adopted with $0.1$. And during inference, they used beam search with a beam size of $5$ and length penalty $1.0$.

The following table shows the BLEU scores of 10 languages → English translation with bilingual, X→En (many-to-English MNMT) and X→X (many-to-many MNMT) systems. The languages are arranged from high-resource (left) to low-resource (right):

The following table shows the same as the previous one but when considering English → 10 languages translation:

The results from the past two tables show that models trained with multitask learning (+MTL) significantly outperform the multilingual and bilingual baselines demonstrating the effectiveness of the proposed framework.

BT means “Back-translation” where they used target-to-source bilingual models to back translate the target-side monolingual sentences into the source domain for each language pair. They used the same monolingual data for back-translation as the multi-task learning.

They further evaluated the proposed approach on zero-shot translation of non English-Centric language pairs. The following table shows that MTL framework significantly improves the zero-shot translation quality of the X→X system especially for low-resource language pairs, further demonstrating the effectiveness of the proposed approach:

For the pivoting method, the source language was translated into English first, and then translated into the target language.

At the end, the researchers of this paper compared the MTL framework with mBART, the state-of-the-art multilingual pre-training method for NMT. They pre-trained mBART on CC25 corpus and fine-tuned it on the same bitext training data used in MTL. As shown in the following figure, MTL outperforms mBART on all language pairs:

This suggests that in the scenario of NMT, jointly training the model with MT task and self-supervised learning tasks could be a better task design than the separated pre-training and fine-tuning stages.

Also, It is worth noting that mBART is utilizing much more monolingual data; for example, it uses 55B English tokens and 10B French tokens, while this approach is using just 100M tokens each. This indicates that MTL is more data efficient.