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Register a new userAn Empirical Accuracy Law for Sequential Machine Translation: the Case of Google Translate
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Added by
Lucas Nunes Sequeira
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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In this research, we have established, through empirical testing, a law that relates the number of translating hops to translation accuracy in sequential machine translation in Google Translate. Both accuracy and size decrease with the number of hops; the former displays a decrease closely following a power law. Such a law allows one to predict the behavior of translation chains that may be built as society increasingly depends on automated devices.
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