Incorporating BERT into Neural Machine Translation

Abstract: The recently proposed BERT has shown great power on a variety of natural language understanding tasks, such as text classification, reading comprehension, etc. However, how to effectively apply BERT to neural machine translation (NMT) lacks enough exploration. While BERT is more commonly used as fine-tuning instead of contextual embedding for downstream language understanding tasks, in NMT, our preliminary exploration of using BERT as contextual embedding is better than using for fine-tuning. This motivates us to think how to better leverage BERT for NMT along this direction. We propose a new algorithm named BERT-fused model, in which we first use BERT to extract representations for an input sequence, and then the representations are fused with each layer of the encoder and decoder of the NMT model through attention mechanisms. We conduct experiments on supervised (including sentence-level and document-level translations), semi-supervised and unsupervised machine translation, and achieve state-of-the-art results on seven benchmark datasets. Our code is available at \url{https://github.com/bert-nmt/bert-nmt}.

• The paper presents a BERT-fused model that integrates context-aware representations into both encoder and decoder layers using innovative attention mechanisms.
• Experimental evaluations demonstrate significant BLEU score improvements across supervised, document-level, semi-supervised, and unsupervised translation tasks.
• The study underscores BERT’s potential in enhancing NMT performance while paving the way for future research on model efficiency and broader AI applications.

Incorporating BERT into Neural Machine Translation

The paper investigates the integration of BERT, a prominent pre-trained LLM, into Neural Machine Translation (NMT). The authors propose a BERT-fused model that utilizes BERT's ability to provide context-aware representations, aiming to enhance NMT performance across various settings, including supervised, semi-supervised, and unsupervised translation tasks.

Research Motivation

BERT has significantly improved results in natural language understanding tasks; however, its application in NMT remains underexplored. Traditional strategies like using BERT for initializing NMT models or as input embeddings have shown limited success. This study explores an alternative approach by fusing BERT representations with the encoder and decoder layers of the NMT model through attention mechanisms.

Methodology

The proposed BERT-fused model extracts representations from BERT and incorporates them into all layers of the NMT encoder and decoder. Attention mechanisms are employed to effectively blend BERT's features with NMT's inherent sequential processing capabilities. This design addresses issues such as differing tokenization schemes between BERT and NMT modules. Additionally, the paper introduces a novel drop-net trick to regularize training by probabilistically using BERT-encoder or self-attention outputs, thereby reducing overfitting.

Experimental Evaluation

The authors conduct extensive experiments across different translation tasks to validate their approach:

• Supervised NMT: Experiments cover low-resource (IWSLT datasets) and high-resource (WMT datasets) scenarios. The BERT-fused model achieved a BLEU score of 36.11 on the IWSLT'14 De-to-En task, surpassing previous methods.
• Document-Level NMT: Utilizing BERT's sentence relations, the model improves translation by incorporating document-level context.
• Semi-Supervised NMT: The approach demonstrates significant improvements when combined with back-translation methods, achieving state-of-the-art results on the WMT'16 Romanian-to-English task.
• Unsupervised NMT: The model outperforms previous unsupervised methods on En↔Fr and En↔Ro tasks by effectively leveraging XLM, a variant of BERT.

Results and Implications

The BERT-fused model consistently outperformed baselines across all tested datasets, setting new benchmarks for various translation tasks. This work illustrates the practical benefits of combining pre-trained LLMs with NMT, reaffirming BERT's utility beyond mere language understanding. The study maintains a comprehensive code repository, facilitating reproducibility and further research.

Future Directions

The authors acknowledge potential improvements in reducing the additional storage and inference time costs introduced by BERT integration. Future work could focus on optimizing such trade-offs and exploring BERT incorporation in other AI domains, such as question answering. Additionally, investigating model compression techniques could lead to more efficient deployment of BERT-enhanced NMT systems.

This research contributes substantially to the field of NMT by effectively exploiting pre-trained LLMs, particularly highlighting BERT's potential in enhancing translation quality through innovative architectural integration.