YoVDO

Improving Machine Translation by Propagating Uncertainty - 2009

Offered By: Center for Language & Speech Processing(CLSP), JHU via YouTube

Tags

Computational Linguistics Courses Probabilistic Models Courses Language Models Courses

Course Description

Overview

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
Explore a comprehensive lecture on enhancing machine translation through uncertainty propagation, presented by Chris Dyer from the Center for Language & Speech Processing at Johns Hopkins University in 2009. Delve into advanced techniques for improving the accuracy and reliability of automated translation systems by incorporating uncertainty measures throughout the translation process. Gain insights into the challenges of machine translation and learn about innovative approaches to addressing ambiguity and improving overall translation quality. Discover how propagating uncertainty can lead to more robust and adaptable translation models, potentially revolutionizing the field of natural language processing.

Syllabus

Improving machine translation by propagating uncertainty - Chris Dyer (2009)


Taught by

Center for Language & Speech Processing(CLSP), JHU

Related Courses

Fundamentals of Quantitative Modeling
University of Pennsylvania via Coursera
Теория вероятностей – наука о случайности
Tomsk State University via Stepik
Statistics and Data Science
Massachusetts Institute of Technology via edX
Natural Language Processing with Probabilistic Models
DeepLearning.AI via Coursera
Natural Language Processing
DeepLearning.AI via Coursera