Rule-Based Systems Originally Defined the MT Category
Benefits of Rule-Based Systems
Disadvantages of Rule-based Systems
Statistical MT Systems Raised the Acceptability of Machine Translation
Benefits of Statistical Systems
Disadvantages of Statistical Systems
Hybrid MT Systems Result from Merging RbMT and SMT
Benefits of Hybrid Systems
Disadvantages of Hybrid Systems
Neural Networks Add Machine Learning Techniques to MT
Benefits of Neural MT
Disadvantages of Neural Network MT
Adaptive MT Learns from Humans
Benefits of Adaptive MT
Disadvantages of Adaptive MT
The Choice between Cloud and On-Premises Installation
Comparing MT Approaches and Choosing a Solution
Machine Translation: Which Type Works Best? (2018)
28 Jun 2018
by
Donald A. DePalma, Dr. Arle Lommel
Just a few years ago it seemed that the decade-old battle between rule-based and statistical machine translation had ended, with statistical approaches dominating and rule-based systems occupying a niche role. Hybrid technology that combines the two had become common. Then along came neural and upended the market.
In this report we analyze the state of the various approaches currently in production or on the horizon. It discusses solutions in chronological order of development – rule-based, statistical, hybrid, and neural MT. It then discusses the role of adaptive MT, characterizes deployment choices, and compares the types and lists major providers for each one. Rather than take this journey through the MT epochs, you can go straight to the approach that interests you. For most people today, it will be neural MT.
This report is an update of “Machine Translation: Which Type Works Best?” published on July 28, 2016. CSA Research periodically re-examines and updates research to account for changing market and technological factors. We revised this report to reflect major developments in the machine translation market since 2016.
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