Proceedings of the 1st Workshop on Open Community-Driven Machine Translation

Miquel Esplà-Gomis, Mikel L. Forcada, Taja Kuzman, Nikola Ljubešić, Rik van Noord, Gema Ramírez-Sánchez, Jörg Tiedemann, Antonio Toral (Editors)

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Tampere, Finland
European Association for Machine Translation
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Proceedings of the 1st Workshop on Open Community-Driven Machine Translation
Miquel Esplà-Gomis | Mikel L. Forcada | Taja Kuzman | Nikola Ljubešić | Rik van Noord | Gema Ramírez-Sánchez | Jörg Tiedemann | Antonio Toral

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Training and integration of neural machine translation with MTUOC
Antoni Oliver | Sergi Alvarez

In this paper the goals and main objectives of the project MTUOC are presented. This project aims to ease the process of training and integrating neural machine translation (NMT) systems into professional translation environments. The MTUOC project distributes a series of auxiliary tools that allow to perform parallel corpus compilation and preprocessing, as well as the training of NMT systems. The project also distributes a server that implements most of the communication protocols used in computer assisted translation tools.

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Design of an Open-Source Architecture for Neural Machine Translation
Séamus Lankford | Haithem Afli | Andy Way

adaptNMT is an open-source application that offers a streamlined approach to the development and deployment of Recurrent Neural Networks and Transformer models. This application is built upon the widely-adopted OpenNMT ecosystem, and is particularly useful for new entrants to the field, as it simplifies the setup of the development environment and creation of train, validation, and test splits. The application offers a graphing feature that illustrates the progress of model training, and employs SentencePiece for creating subword segmentation models. Furthermore, the application provides an intuitive user interface that facilitates hyperparameter customization. Notably, a single-click model development approach has been implemented, and models developed by adaptNMT can be evaluated using a range of metrics. To encourage eco-friendly research, adaptNMT incorporates a green report that flags the power consumption and kgCO2 emissions generated during model development. The application is freely available.

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Creating a parallel Finnish-Easy Finnish dataset from news articles
Anna Dmitrieva | Aleksandra Konovalova

Modern natural language processing tasks such as text simplification or summarization are typically formulated as monolingual machine translation tasks. This requires appropriate datasets to train, tune, and evaluate the models. This paper describes the creation of a parallel Finnish-Easy Finnish dataset from the Yle News archives. The dataset contains 1919 manually verified pairs of articles, each containing an article in Easy Finnish (selkosuomi) and a corresponding article from Standard Finnish news. Standard Finnish texts total 687555 words, and Easy Finnish texts have 106733 words. This new aligned resource was created automatically based on the Yle News archives from the Language Bank of Finland (Kielipankki) and manually checked by a human expert. The dataset is available for download from Kielipankki. This resource will allow for more effective Easy Language research and for creating applications for automatic simplification and/or summarization of Finnish texts.

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A Python Tool for Selecting Domain-Specific Data in Machine Translation
Javad Pourmostafa Roshan Sharami | Dimitar Shterionov | Pieter Spronck

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MutNMT, an open-source NMT tool for educational purposes
Gema Ramírez Sánchez