Open-Source Neural Machine Translation API Server

Open access

Abstract

We introduce an open-source implementation of a machine translation API server. The aim of this software package is to enable anyone to run their own multi-engine translation server with neural machine translation engines, supporting an open API for client applications. Besides the hub with the implementation of the client API and the translation service providers running in the background we also describe an open-source demo web application that uses our software package and implements an online translation tool that supports collecting translation quality comparisons from users.

Bird, Steven, Ewan Klein, and Edward Loper. Natural Language Processing with Python. O’Reilly Media, 2009.

Federmann, Christian and Andreas Eisele. MT Server Land: An Open-Source MT Architecure. The Prague Bulletin of Mathematical Linguistics, 94:57–66, 2010.

Forcada, Mikel L, Mireia Ginestí-Rosell, Jacob Nordfalk, Jim O’Regan, Sergio Ortiz-Rojas, Juan Antonio Pérez-Ortiz, Felipe Sánchez-Martínez, Gema Ramírez-Sánchez, and Francis M Tyers. Apertium: a free/open-source platform for rule-based machine translation. Machine translation, 25(2):127–144, 2011.

Helcl, Jindřich and Jindřich Libovický. Neural Monkey: An Open-source Tool for Sequence Learning. The Prague Bulletin of Mathematical Linguistics, (107):5–17, 2017.

Junczys-Dowmunt, Marcin, Tomasz Dwojak, and Hieu Hoang. Is Neural Machine Translation Ready for Deployment? A Case Study on 30 Translation Directions. CoRR, abs/1610.01108, 2016. URL http://arxiv.org/abs/1610.01108.

Koehn, Philipp, Hieu Hoang, Alexandra Birch, Chris Callison-Burch, Marcello Federico, Nicola Bertoldi, Brooke Cowan, Wade Shen, Christine Moran, Richard Zens, Chris Dyer, Ondrej Bojar, Alexandra Constantin, and Evan Herbst. Moses: Open Source Toolkit for Statistical Machine Translation. In Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions, pages 177–180, Prague, Czech Republic, 2007.

Sánchez-Cartagena, Víctor and Juan Pérez-Ortiz. ScaleMT: a free/open-source framework for building scalable machine translation web services. The Prague Bulletin of Mathematical Linguistics, 93:97–106, 2010.

Sennrich, Rico, Barry Haddow, and Alexandra Birch. Neural Machine Translation of Rare Words with Subword Units. CoRR, abs/1508.07909, 2015. URL http://arxiv.org/abs/1508.07909.

Sennrich, Rico, Orhan Firat, Kyunghyun Cho, Alexandra Birch, Barry Haddow, Julian Hitschler, Marcin Junczys-Dowmunt, Samuel Läubli, Antonio Valerio Miceli Barone, Jozef Mokry, and Maria Nadejde. Nematus: a Toolkit for Neural Machine Translation. CoRR, abs/1703.04357, 2017. URL http://arxiv.org/abs/1703.04357.

Tamchyna, Aleš, Ondřej Dušek, Rudolf Rosa, and Pavel Pecina. MTMonkey: A Scalable Infrastructure for a Machine Translation Web Service. The Prague Bulletin of Mathematical Linguistics, 100:31–40, 2013.

Vaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention Is All You Need. CoRR, abs/1706.03762, 2017. URL http://arxiv.org/abs/1706.03762.

The Prague Bulletin of Mathematical Linguistics

The Journal of Charles University

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