An Empirical Accuracy Law for Sequential Machine Translation: the Case of Google Translate
Lucas Nunes Sequeira, Bruno Seravali Moreschi, Fábio Gagliardi Cozman, Bernardo Fontes · arXiv (Cornell University) · 2020
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.