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Multilingual Machine Translation Systems from Microsoft for WMT21 Shared Task

Categories: Text & Writing  |  Pricing: Free  |  Official Website ↗

This report details Microsoft's machine translation systems for the WMT21 shared task, achieving top ranks in multilingual translation.

This report describes the machine translation systems developed by Microsoft for the WMT21 shared task on large-scale multilingual machine translation. Microsoft participated in all three evaluation tracks: the unconstrained Large Track and two fully constrained Small Tracks. The models submitted to the shared task were initialized using DeltaLM, a pre-trained multilingual encoder-decoder model. These models were then fine-tuned with extensive parallel data and other allowed data sources, incorporating progressive learning and iterative back-translation to enhance performance. Microsoft's final submissions achieved first place in three tracks based on automatic evaluation metrics.

Key Features

• Multilingual machine translation
• DeltaLM pre-training
• Fine-tuning with parallel data
• Progressive learning
• Iterative back-translation

Pros

• Achieved first rank in three WMT21 shared task tracks
• Utilizes a generic pre-trained multilingual encoder-decoder model (DeltaLM)
• Incorporates advanced techniques like progressive learning and iterative back-translation
• Demonstrates high performance in large-scale multilingual translation

Cons

• This is a research paper, not a commercial product
• Specific implementation details may require deep technical understanding
• Performance is evaluated in a specific shared task context
• Not directly available as a user-facing tool

Use Cases

• Advancing machine translation research
• Developing high-performance multilingual translation systems
• Benchmarking machine translation models
• Understanding state-of-the-art MT techniques

Best For

• AI researchers
• NLP practitioners
• Machine translation developers
• Academics studying multilingual models

Platforms: Web

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