In my role, I enjoy a unique perspective in viewing the relationship among three attributes of human cognition: monolingual text (X), audio or visual sensory signals, (Y) and multilingual (Z).
By maximizing the information-theoretic mutual information of these representations based on 50 billion unique query-documentation pairs as training data, X-code successfully learned the semantic relationships among queries and documents at web scale, and it demonstrated strong performance in various natural language processing tasks such as search ranking, ad click prediction, query-to-query similarity, and documentation grouping.
Z-code expands monolingual X-code by enabling text-based multilingual neural translation for a family of languages.
Because of transfer learning, and the sharing of linguistic elements across similar languages, we’ve dramatically improved the quality, reduced the costs, and improved efficiency for machine translation capability in Azure Cognitive Services (see Figure 4 for details).