University of Helsinki
Anther presentation of the Found in Translation project appeared at the LADAL webinars on December 5. The recording of the webinar is available from YouTube.
FoTran talk at AI day
Raul Vazquez from the Fotran team gave a talk at the AI Day 2022 on “A Closer Look at Parameter Contributions When Training Neural Language and Translation Models”. The paper is published at COLING 2022. This is joint work with Hande Celikkanat, Vinit Ravishankar, Mathias Creutz and Jörg Tiedemann and looks at the training dynamics of neural language and translation models using fine-grained loss-change allocation analyses.
2022 Steven Krauwer Award for OPUS-MT for Ukrainian
Helsinki-NLP received the 2022 Steven Krauwer award for CLARIN achievements for the work on open machine translation for Ukrainian. Thank you very much for this award but especially also thanks to everyone who contributed data, software and help with putting this all together! And let us continue to help people in need recognizing the importance of open and transparent language technology and the responsibilities we have in society. Thank you!
Talk by Thimothée Mickus “Linear structures in Transformer Embedding Spaces” available online
Thimothée Mickus from the Fotran team gave a talk in October in the Machine Learning Coffee Seminars on “Linear structures in Transformer Embedding Spaces”. The talk is available online.
Talk by Jörg Tiedemann “Translations as semantic mirrors – Representation learning with multilingual data” available online.
Jörg Tiedemann gave a talk at the Learning Machines Seminar 2022 in Sweden on “Translations as semantic mirrors – Representation learning with multilingual data”. The talk is available online.
HPLT and LumiNMT
Our new project on High-Performance Language Technologies (HPLT) has started and we will scale data sets, language models and neural MT to a new level. In relation to that, the language technology group in Helsinki has also been selected for one of the first Finnish extreme scale projects on the supercomputer LUMI.
Our project there will be called LumiNMT and the goal of the project is to train neural machine translation models on a large scale using state-of-the-art transformer models and novel modular multilingual setups. Our project will focus on increasing language coverage and efficient use of massively parallel data sets. Our research group wants to use LUMI’s extensive parallel computing capabilities to reduce training time and scale up a model size.
UnGroundNLP – presentations
The presentations from our workshop on Uncertainty and Grounding in Language and Translation Models (UnGroundNLP) are now on-line and can be access from our workshop website: https://blogs.helsinki.fi/fcai-sig-lsc/ungroundnlp-2022/
Thanks to all speakers and participants for a great day with plenty of interesting discussions!
Language tools for Ukrainian
In response to the on-going crisis in Ukraine we have started to collect language tools and resources that support the Ukrainian language. At Helsinki-NLP, we have especially focused on the development of open translation models and tools and we are currently working on improved models for more language pairs. Hopefully some of them can help communication and interaction with people in help.
Language (technology) is the key to intelligence and equality
An article about the importance of language technology has been published in “språkbruk” from the Institute for the Languages of Finland. The article discusses that we need to be careful about our language data and that we need to make en effort to develop transparent and open technology to handle valuable information we produce.
Detect, normalize and generate Finnish dialects
Finnish dialects create a lot of trouble when interacting with computers, since it is impossible to speak a language without speaking in a dialect of some sort. Mika Hämäläinen, Niko Partanen, Khalid Alnajjar and Jack Rueter from our language technology team have created software that can automatically detect, normalize and generate Finnish dialects. Their research made it to the news on our university website.
