Contrastive Learning for Many-to-many Multilingual Neural Machine Translation (2105.09501v3)

Abstract: Existing multilingual machine translation approaches mainly focus on English-centric directions, while the non-English directions still lag behind. In this work, we aim to build a many-to-many translation system with an emphasis on the quality of non-English language directions. Our intuition is based on the hypothesis that a universal cross-language representation leads to better multilingual translation performance. To this end, we propose mRASP2, a training method to obtain a single unified multilingual translation model. mRASP2 is empowered by two techniques: a) a contrastive learning scheme to close the gap among representations of different languages, and b) data augmentation on both multiple parallel and monolingual data to further align token representations. For English-centric directions, mRASP2 outperforms existing best unified model and achieves competitive or even better performance than the pre-trained and fine-tuned model mBART on tens of WMT's translation directions. For non-English directions, mRASP2 achieves an improvement of average 10+ BLEU compared with the multilingual Transformer baseline. Code, data and trained models are available at https://github.com/PANXiao1994/mRASP2.

An Academic Overview of "Contrastive Learning for Many-to-many Multilingual Neural Machine Translation"

The paper "Contrastive Learning for Many-to-many Multilingual Neural Machine Translation" by Xiao Pan et al. addresses a notable deficiency in multilingual neural machine translation (NMT) systems, focusing particularly on the non-English-centric translation directions which have typically lagged in performance. The paper's primary contribution is the introduction of mRASP2, a unified training methodology designed to enhance translation quality in many-to-many multilingual NMT contexts.

Core Contributions

1. Unified Model with Contrastive Learning: The authors propose mRASP2, which integrates contrastive learning to minimize the representational disparity between different languages. This approach aims to create a universal cross-language representation, theoretically enhancing performance across multiple language directions.
2. Data Augmentation Techniques: mRASP2 is further supported by innovative data augmentation strategies. These include parallel and monolingual data augmentations designed to better align token representations across languages.
3. Empirical Evaluation: Using established datasets, mRASP2 consistently demonstrated substantial improvements over existing models. For non-English translation directions, mRASP2 secured a significant gain, exceeding 10 BLEU points over multilingual Transformer baselines. Furthermore, in English-centric directions, mRASP2 not only outperformed the existing unified models but also displayed competence comparable to more complex models like mBART, achieving peak performance across WMT translation benchmarks.

Methodology

The paper's methodology hinges on two technical innovations:

• Contrastive Loss Implementation: By employing contrastive loss, mRASP2 articulately draws similar sentences across languages closer within the representational space. This feature exploits multilingual parallel corpora, and importantly, facilitates zero-shot translation.
• Aligned Augmentation (AA): Extending beyond previous augmentation practices, AA applies aligned token replacements using synonym dictionaries. This method creates pseudo sentence pairs for both parallel and monolingual data, enriching the training model.

Numerical Results

Significant results include a 10+ BLEU score increase for non-English directions and competitive results in English-centric directions, suggesting a robust capability of mRASP2 in handling a diverse set of languages without the distinct need for fine-tuning on individual language pairs.

Implications and Future Directions

The implications of this research are manifold:

• Practical Deployment: mRASP2's success in creating a more effective many-to-many translation framework could lead to more efficient deployment of multilingual NMT systems in practical applications, especially for low-resource languages.
• Extension to Broader Language Sets: The potential exists to extend this approach to larger language groups and more varied dialects, possibly involving architectures even more comprehensive than the current many-to-many model.
• Theoretical Insights: On a theoretical level, mRASP2's integration of contrastive learning into NMT models demonstrates the efficacy of bridging language representation gaps, a concept that could be expanded upon in future NLP research endeavors.

In conclusion, this paper represents a significant stride in multilingual NMT by offering a strategic framework that amalgamates contrastive learning and effective data augmentation. The outcomes underscore mRASP2's efficacy in both improving translation quality for underrepresented multilingual directions and providing a scalable solution applicable to an extensive range of language pairs. Future explorations may seek to refine these methods further, expanding applications and enhancing adaptability, which will be crucial as the field progresses toward inclusive and comprehensive global translation solutions.