SMU Data Science Review
Abstract
Abstract.
Pristine Sentence Translation (PST) is a new approach to language translation based upon sentence-level granularity. Traditional translation approaches, including those utilizing advanced machine learning or neural network-based approaches, translate on a word-by-word or phrase-by-phrase basis; thereby, potentially missing the context or meaning of the complete sentence. Instead of these piecewise translations, PST utilizes deep learning and predictive modeling techniques to translate complete sentences from their source language into their target language. With these approaches we were able to translate sentences that closely conveyed the meaning of the original sentences. Our results demonstrated that PST’s method of translating an entire sentence is a robust approach to translations in some circumstances.
Recommended Citation
Ahluwalia, Meenu; Coari, Brian; and Brock, Ben
(2019)
"Pristine Sentence Translation: A New Approach to a Timeless Problem,"
SMU Data Science Review: Vol. 2:
No.
2, Article 4.
Available at:
https://scholar.smu.edu/datasciencereview/vol2/iss2/4
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