Publication

ODIANLP's Participation in WAT2020

Petr Motlicek
2020
Conference Papers

Abstract

This paper describes the team (“ODI-ANLP”)’s submission to WAT 2020. We have participated in the English→HindiMultimodal task and Indic task. We have used the state-of-the-art Transformer model for the translation task and Incep-tionResNetV2 for the Hindi Image Captioning task. Our submission tops in English→Hindi Multimodal task in its track and Odia↔English translation tasks. Also, our submissions performed well in the Indic Multilingual tasks.

Official source

https://infoscience.epfl.ch/entities/publication/76adc4a6-e7b8-46b2-b217-c70c3daa15e0

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Petr Motlicek
2020
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/76adc4a6-e7b8-46b2-b217-c70c3daa15e0

About this result

Related concepts (12)

Translation

Translation is the communication of the meaning of a source-language text by means of an equivalent target-language text. The English language draws a terminological distinction (which does not exist in every language) between translating (a written text) and interpreting (oral or signed communication between users of different languages); under this distinction, translation can begin only after the appearance of writing within a language community.

Google Translate

Google Translate is a multilingual neural machine translation service developed by Google to translate text, documents and websites from one language into another. It offers a website interface, a mobile app for Android and iOS, as well as an API that helps developers build browser extensions and software applications. As of 2022, Google Translate supports languages at various levels; it claimed over 500 million total users , with more than 100 billion words translated daily, after the company stated in May 2013 that it served over 200 million people daily.

Neural machine translation

Neural machine translation (NMT) is an approach to machine translation that uses an artificial neural network to predict the likelihood of a sequence of words, typically modeling entire sentences in a single integrated model. They require only a fraction of the memory needed by traditional statistical machine translation (SMT) models. Furthermore, unlike conventional translation systems, all parts of the neural translation model are trained jointly (end-to-end) to maximize the translation performance.

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