How phrase-based translation (2003) extended IBM statistical MT to phrase-level learning, capturing idioms and collocations, while Minimum Error Rate Training optimized feature weights to directly maximize BLEU scores, establishing the dominant statistical MT paradigm
This article is part of the free-to-read History of Language AI book
Choose your expertise level to adjust how many terms are explained. Beginners see more tooltips, experts see fewer to maintain reading flow. Hover over underlined terms for instant definitions.
Reference
About the author: Michael Brenndoerfer
All opinions expressed here are my own and do not reflect the views of my employer.
Michael currently works as an Associate Director of Data Science at EQT Partners in Singapore, where he drives AI and data initiatives across private capital investments.
With over a decade of experience spanning private equity, management consulting, and software engineering, he specializes in building and scaling analytics capabilities from the ground up. He has published research in leading AI conferences and holds expertise in machine learning, natural language processing, and value creation through data.
Related Content
Hybrid Retrieval: Combining Sparse and Dense Methods for Effective Information Retrieval
A comprehensive guide to hybrid retrieval systems introduced in 2024. Learn how hybrid systems combine sparse retrieval for fast candidate generation with dense retrieval for semantic reranking, leveraging complementary strengths to create more effective retrieval solutions.
Structured Outputs: Reliable Schema-Validated Data Extraction from Language Models
A comprehensive guide covering structured outputs introduced in language models during 2024. Learn how structured outputs enable reliable data extraction, eliminate brittle text parsing, and make language models production-ready. Understand schema specification, format constraints, validation guarantees, practical applications, limitations, and the transformative impact on AI application development.
Multimodal Integration: Unified Architectures for Cross-Modal AI Understanding
A comprehensive guide to multimodal integration in 2024, the breakthrough that enabled AI systems to seamlessly process and understand text, images, audio, and video within unified model architectures. Learn how unified representations and cross-modal attention mechanisms transformed multimodal AI and enabled true multimodal fluency.
Stay updated
Get notified when I publish new articles on data and AI, private equity, technology, and more.