A Framework for German-English Machine Translation with GRU RNN

Levi Corallo, Guanghui Li, Kenna Reagan, Abhishek Saxena, Aparna S. Varde, Brandon Wilde

Research output: Contribution to journalConference articlepeer-review

Abstract

Machine translation (MT) using Gated Recurrent Units (GRUs) is a popular model used in industry-level web translators because of the efficiency with which it handles sequential data compared to Long Short-Term Memory (LSTM) in language modeling with smaller datasets. Motivated by this, a deep learning GRU based Recurrent Neural Network (RNN) is modeled as a framework in this paper, utilizing WMT2021’s English-German data-set that originally contains 400,000 strings from German news with parallel English translations. Our framework serves as a pilot approach in translating strings from German news media into English sentences, to build applications and pave the way for further work in the area. In real-life scenarios, this framework can be useful in developing mobile applications (apps) for quick translation where efficiency is crucial. Furthermore, our work makes broader impacts on a UN SDG (United Nations Sustainable Development Goal) of Quality Education, since offering education remotely by leveraging technology, as well as seeking equitable solutions and universal access are significant objectives there. Our framework for German-English translation in this paper can be adapted to other similar language translation tasks.

Original languageEnglish
JournalCEUR Workshop Proceedings
Volume3135
StatePublished - 2022
Event2022 Workshops of the EDBT/ICDT Joint Conference, EDBT/ICDT-WS 2022 - Edinburgh, United Kingdom
Duration: 29 Mar 2022 → …

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