BiT: Bidirectional Training

BiT stands for “Bidirectional Training” which is a simple and effective pre-training strategy for neural machine translation. BiT was proposed by The University of Sydney in collaboration with Peking University and JD Explore Academy in 2021 and published in this paper: Improving Neural Machine Translation by Bidirectional Training.

The motivation behind this strategy is that when human learn foreign languages, knowing both directions $x_{i} \rightarrow y_{i}$ and $y_{i} \rightarrow x_{i}$ may help human easily master the bilingual knowledge. Simply put, they proposed using a system trained on bi-directional data as an initialization for a unidirectional system.

Specifically, they proposed pre-training NMT models using bilingual data created by reconstructing the training samples from ($\overrightarrow{B}:\ \text{source} \rightarrow \text{target}$) to ($\overset{\overleftrightarrow{}}{B}:source + target \rightarrow target + source$)”, where the training data was doubled as shown in the following formula:

\[\overset{\overleftrightarrow{}}{B} = \left\{ \left( x_{i},\ y_{i} \right) \cup \left( y_{i},\ x_{i} \right) \right\}_{i = 1}^{N}\]

Where the pre-training objective is:

\[\overset{\overleftrightarrow{}}{\mathcal{L}}\left( \theta \right) = \overrightarrow{\mathcal{L}}\left( \theta \right) + \overleftarrow{\mathcal{L}}\left( \theta \right)\] \[\overset{\overleftrightarrow{}}{\mathcal{L}}\left( \theta \right) = \underset{\theta}{\arg\max}\left( \log\left( p\left( y \middle| x;\theta \right) \right) \right) + \underset{\theta}{\arg\max}\left( \log\left( p\left( x \middle| y;\theta \right) \right) \right)\]

Pre-training lasts for 1/3 of the total training steps and fine-tuning is performs on the required direction $\overrightarrow{B}$ with the rest of 2/3 training steps.


In this paper, they tried five translation datasets whose data sizes range from 160K to 38M sentence-pairs as shown in the following table. All language pairs were trained on Transformer-BIG except IWSLT14 En↔De and WMT16 En↔Ro; they were trained on Transformer-BASE because of their extremely small data size. Also, the performance was measured by averaging the model’s last 10 checkpoints to avoid stochasticity.

The previous table show that BiT achieves significant improvements over strong baseline Transformer in 7 out of 10 directions, and the rest of 3 directions also show promising performance. This demonstrates the effectiveness and universality of BiT.

To dispel the doubt that BiT works also on distant language pairs, they trained two models on Zh↔En and Ja→En language pairs. The following table shows that BiT significantly and incrementally improves the translation quality in all cases.