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Language barriers have always been a challenge in communication. With the advent of globalization, it has become increasingly important to bridge the gap between languages.…
Language barriers have always been a challenge in communication. With the advent of globalization, it has become increasingly important to bridge the gap between languages. Machine Translation (MT) is a form of artificial intelligence that is helping to break down language barriers by automatically translating text or speech from one language to another. This article will explore how MT works, its benefits and limitations, and its impact on various industries.
Machine Translation uses Natural Language Processing (NLP) algorithms to analyze and translate text or speech from one language to another. The process involves three main steps:
Machine Translation can be further divided into two categories: rule-based and statistical.
Recently, Neural Machine Translation (NMT) has emerged as a new paradigm for MT. NMT uses deep learning techniques to learn the relationship between words and their meaning in context. NMT has shown significant improvements in translation quality over RBMT and SMT and is now the dominant approach in MT.
I add one more here:
Kyunghyun Cho is an expert in machine translation. He is an associate professor of computer science and data science at New York University and CIFAR Fellow of Learning in Machines & Brains. He has published many papers on machine translation and related topics. He is also a senior director of frontier research at Genentech Research & Early Development (gRED) and a former research scientist at Facebook AI Research.
Graham Neubig is also an expert in machine translation. He is an associate professor at the Carnegie Mellon University Language Technology Institute and works with his lab NeuLab on natural language processing and machine learning, specifically machine translation, multi-lingual NLP, and spoken language processing. He has published many papers on machine translation and related topics. He is also the CEO of Inspired Cognition, a company that aims to make the development of AI systems more efficient and accessible
Alexander Rush is another expert in machine translation. He is a professor at the Massachusetts Institute of Technology and works on natural language processing and machine learning, specifically neural machine translation, structured prediction, and latent variable models. He has published many papers on machine translation and related topics. He is also the creator of Torch-Struct, a deep structured prediction library.
Philipp Koehn is a professor of computer science at Johns Hopkins University and a leading researcher in machine translation and natural language processing. He has developed and applied data-driven methods to solve long-standing, real-world challenges of machine translation and natural language understanding. He is also the author of the textbook “Statistical Machine Translation” and the founder of the Workshop on Statistical Machine Translation. He has received many awards for his contributions to machine translation research.
Heng Ji is a professor of computer science and electrical and computer engineering at University of Illinois at Urbana-Champaign and an Amazon Scholar. She received her B.A. and M.A. in Computational Linguistics from Tsinghua University, and her M.S. and Ph.D. in Computer Science from New York University1. She is an expert in natural language processing, especially in information extraction, cross-lingual information access, natural language understanding, and knowledge base population. She has published over 200 papers in top-tier conferences and journals and has received several awards for her research excellence.
