Another early-stage project is coming from Jörg Tiedemann at the University of Helsinki, who is working with colleagues to develop open translation models for the Grid. These models use deep neural networks—layered software architectures that implement complex mathematical functions—to map text into numeric representations. Using data sets to train the models to find the best ways to solve problems takes a lot of computing power and is expensive. Making the models available for re-use will help developers build tools for low-density languages. “Minority languages get too little attention because they are not commercially interesting,” Tiedemann says. “This gap needs to be closed.”
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The integration of our machine translation models in huggingface created big attention world-wide. Now FCAI is also sharing a news item about our developments and the contributions to open science and accessible NLP for many languages. Here is the news item: https://fcai.fi/news/2020/6/15/huge-contribution-to-democratizing-natural-language-processing-1
There is a news article at CSC that features our research in FoTran and on multilingual machine translation: https://www.csc.fi/en/-/seeking-to-understand-language-by-learning-from-translations. Check it out …
Our NMT models from OPUS-MT are in the news as they are now ported and integra
Alessandro presents FoTran during the Poster session of the AI Day 2019 organised by FCAI. And a presentation about the FCAI special interest group for language speech and cognition was also part of the programme. Here is a another summary of the event.
There were two presentations from our group at HELDIG Summit, one about FoTran and one about the new Special Interest Group on Language, Speech and Cognition under the umbrella of FCAI.
Tommi Jouhiainen is defending his PhD thesis in language technology on “Language Identification in Texts” on Tuesday, May, 28 at 12 in auditorio XII in the main building of the university. The thesis is available at http://urn.fi/URN:ISBN:978-951-51-5131-5 and the opponent is Nikola Ljubešić from the Jožef Stefan Institute in Ljubljana and the University of Zagreb.
In both the news and multimodal translation tasks, the best systems from the Language Technology group utilised state-of-the-art neural machine translation models.
System papers describing the models will be presented at EMNLP 2018 Third Conference on Machine Translation (WMT18) later this year.
Congratulations to our team!