Machine Translation (MT) Strategy
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A Machine Translation (MT) Strategy is an AI strategy focused on machine translation technologies.
- Context:
- It can address the development, deployment, and management of MT systems within an organization.
- It can include choosing between Rule-Based MT, Statistical MT, Example-Based MT, and Neural MT approaches.
- It can include strategies for MT Training Data Acquisition, such as Parallel Corpus Creation and Web Crawling for Parallel Data.
- It can include approaches for handling Out-of-Vocabulary Words and Rare Words in MT.
- It can address MT Evaluation strategies, such as using BLEU Score, METEOR, or Human Evaluation.
- It can include tactics for MT Domain Adaptation, such as Fine-Tuning on In-Domain Data.
- It can encompass leveraging MT for various use cases, such as Website Localization, Product Documentation Translation, Customer Support Translation, etc.
- Example(s):
- A Neural MT Strategy using Transformer Models and Back-Translation for data augmentation.
- A Hybrid MT Strategy combining Rule-Based MT for controllable translation with Neural MT for fluency.
- Google's MT Strategy centered around Google Translate and its underlying Google Neural Machine Translation (GNMT) System.
- Microsoft's MT Strategy focused on Microsoft Translator and its integration with Microsoft Office Products.
- Counter-Example(s):
- A Speech Recognition AI Strategy focused on converting Speech to Text.
- An Image Captioning AI Strategy focused on generating Natural Language Descriptions of images.
- See: AI Strategy, NLP Strategy, Translation Technology, MT Evaluation.