Improving machine translation systems via isotopic replacement
Zeyu Sun, Jie M. Zhang, Yingfei Xiong, Mark Harman, Mike Papadakis, Lu Zhang
2022Proceedings of the 44th International Conference on Software Engineering42 citationsDOIOpen Access PDF
Abstract
Machine translation plays an essential role in people's daily international communication. However, machine translation systems are far from perfect. To tackle this problem, researchers have proposed several approaches to testing machine translation. A promising trend among these approaches is to use word replacement, where only one word in the original sentence is replaced with another word to form a sentence pair. However, precise control of the impact of word replacement remains an outstanding issue in these approaches.
Topics & Concepts
Machine translationComputer scienceSentenceWord (group theory)Translation (biology)Artificial intelligenceNatural language processingTransfer-based machine translationExample-based machine translationMachine translation software usabilityRule-based machine translationComputer-assisted translationSpeech recognitionLinguisticsChemistryBiochemistryGenePhilosophyMessenger RNATopic ModelingNatural Language Processing TechniquesMultimodal Machine Learning Applications