Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation (1611.04558v2)

Abstract: We propose a simple solution to use a single Neural Machine Translation (NMT) model to translate between multiple languages. Our solution requires no change in the model architecture from our base system but instead introduces an artificial token at the beginning of the input sentence to specify the required target language. The rest of the model, which includes encoder, decoder and attention, remains unchanged and is shared across all languages. Using a shared wordpiece vocabulary, our approach enables Multilingual NMT using a single model without any increase in parameters, which is significantly simpler than previous proposals for Multilingual NMT. Our method often improves the translation quality of all involved language pairs, even while keeping the total number of model parameters constant. On the WMT'14 benchmarks, a single multilingual model achieves comparable performance for English$\rightarrow$French and surpasses state-of-the-art results for English$\rightarrow$German. Similarly, a single multilingual model surpasses state-of-the-art results for French$\rightarrow$English and German$\rightarrow$English on WMT'14 and WMT'15 benchmarks respectively. On production corpora, multilingual models of up to twelve language pairs allow for better translation of many individual pairs. In addition to improving the translation quality of language pairs that the model was trained with, our models can also learn to perform implicit bridging between language pairs never seen explicitly during training, showing that transfer learning and zero-shot translation is possible for neural translation. Finally, we show analyses that hints at a universal interlingua representation in our models and show some interesting examples when mixing languages.

Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation

The paper "Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation" introduces a streamlined approach to Neural Machine Translation (NMT) that facilitates translation across multiple languages using a single model. The authors present a technique that does not necessitate architectural alterations to traditional NMT models, thereby retaining simplicity and scalability. This essay provides an overview of the proposed method, experimental outcomes, and implications of the research.

Simplifying Multilingual NMT

The central innovation described in the paper is the addition of an artificial token at the beginning of the input sequence to specify the target language. This modification allows a single model to handle translations between multiple language pairs with a shared encoder, decoder, and attention mechanism. By maintaining a shared wordpiece vocabulary, the system avoids the need for multiple models and complex adjustments to handle different languages.

Key Benefits and Findings

• Simplicity: The method preserves the architecture and training procedure of standard NMT models. It enables seamless scaling to additional languages by merely incorporating new data and specifying new tokens.
• Low-Resource Language Improvements: The multilingual model leverages shared parameters to generalize across language boundaries, significantly enhancing translation quality for low-resource language pairs.
• Zero-Shot Translation: The model demonstrates the ability to translate between language pairs that were not explicitly trained on, showcasing an example of transfer learning within NMT. A model trained on Portuguese$\rightarrow$English and English$\rightarrow$Spanish data can perform reasonably well on Portuguese$\rightarrow$Spanish translations.

Experimental Results

The authors conducted several experiments to evaluate the performance of the multilingual NMT system across different configurations: many-to-one, one-to-many, and many-to-many.

Many-to-One and One-to-Many Translations

Multilingual models generally outperformed or matched the performance of baseline single language pair models. For instance, a many-to-one model combining German$\rightarrow$English and French$\rightarrow$English saw an increase in BLEU scores compared to single models. However, one-to-many models exhibited mixed results, with some translations slightly declining in quality due to the increased complexity of translating into multiple target languages.

Many-to-Many Translations

In the many-to-many configuration, multilingual models displayed a modest reduction in translation quality compared to single language pair models, yet the trade-off was deemed acceptable given the substantial reduction in the number of models needed and the associated computational efficiencies.

Large-Scale Experiments

A large-scale model combining 12 language pairs highlighted that, even with considerably fewer parameters than the combined single language pair models, the multilingual model achieved reasonable performance. Moreover, this approach significantly reduced the training resources and time required, underscoring the practical benefits of the method.

Zero-Shot Translation and Implicit Bridging

One of the paper's notable contributions is the demonstration of zero-shot translation, where the model learns to translate between previously unseen language pairs. The experiments confirmed that the multilingual model could produce quality translations in zero-shot scenarios, such as Portuguese$\rightarrow$Spanish, with scores above 20 BLEU. Additionally, incremental training with small amounts of parallel data for the zero-shot language pair further improved translation quality.

Visual Analysis and Shared Representations

The authors explored the internal representations of the model using t-SNE projections to visualize the context vectors. The analysis revealed evidence of a universal interlingua representation, where semantically identical sentences from different languages clustered together. This finding indicates that the model learns shared embeddings across languages, facilitating effective zero-shot translations.

Implications and Future Directions

The research presented in this paper bears significant implications for both practical and theoretical aspects of NMT and multilingual systems:

• Practical Advantages: The approach simplifies deployment and scaling for systems like Google Translate by reducing the number of models required and allowing for efficient handling of multilingual data.
• Theoretical Insights: The findings provide insights into the potential of transfer learning and shared representations in NMT, opening avenues for further exploration into the mechanisms underpinning multilingual and zero-shot translation.

Conclusion

The paper demonstrates that a unified multilingual NMT model can effectively manage multiple languages and enable zero-shot translation without modifying the underlying architecture. This approach simplifies the training and deployment process, improves translation quality for low-resource languages, and showcases practical transfer learning in NMT. The insights gleaned from this research are poised to inform future developments in AI-driven translation technologies, advancing both the efficiency and scalability of multilingual systems.