no code implementations • EMNLP 2021 • Alessandro Raganato, Raúl Vázquez, Mathias Creutz, Jörg Tiedemann
In this paper, we investigate the benefits of an explicit alignment to language labels in Transformer-based MNMT models in the zero-shot context, by jointly training one cross attention head with word alignment supervision to stress the focus on the target language label.
1 code implementation • WMT (EMNLP) 2020 • Yves Scherrer, Alessandro Raganato, Jörg Tiedemann
This paper reports on our participation with the MUCOW test suite at the WMT 2020 news translation task.
no code implementations • EAMT 2022 • Raúl Vázquez, Michele Boggia, Alessandro Raganato, Niki A. Loppi, Stig-Arne Grönroos, Jörg Tiedemann
We describe the enhancement of a multilingual NMT toolkit developed as part of the FoTran project.
no code implementations • 12 Mar 2024 • Timothee Mickus, Elaine Zosa, Raúl Vázquez, Teemu Vahtola, Jörg Tiedemann, Vincent Segonne, Alessandro Raganato, Marianna Apidianaki
This paper presents the results of the SHROOM, a shared task focused on detecting hallucinations: outputs from natural language generation (NLG) systems that are fluent, yet inaccurate.
1 code implementation • 12 Mar 2024 • Timothee Mickus, Stig-Arne Grönroos, Joseph Attieh, Michele Boggia, Ona de Gibert, Shaoxiong Ji, Niki Andreas Lopi, Alessandro Raganato, Raúl Vázquez, Jörg Tiedemann
NLP in the age of monolithic large language models is approaching its limits in terms of size and information that can be handled.
no code implementations • 4 Dec 2022 • Jörg Tiedemann, Mikko Aulamo, Daria Bakshandaeva, Michele Boggia, Stig-Arne Grönroos, Tommi Nieminen, Alessandro Raganato, Yves Scherrer, Raul Vazquez, Sami Virpioja
This paper presents the OPUS ecosystem with a focus on the development of open machine translation models and tools, and their integration into end-user applications, development platforms and professional workflows.
no code implementations • NAACL 2021 • Iacer Calixto, Alessandro Raganato, Tommaso Pasini
Further adding extra languages lead to improvements in most tasks up to a certain point, but overall we found it non-trivial to scale improvements in model transferability by training on ever increasing amounts of Wikipedia languages.
1 code implementation • EMNLP 2020 • Alessandro Raganato, Tommaso Pasini, Jose Camacho-Collados, Mohammad Taher Pilehvar
The ability to correctly model distinct meanings of a word is crucial for the effectiveness of semantic representation techniques.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Alessandro Raganato, Yves Scherrer, Jörg Tiedemann
Transformer-based models have brought a radical change to neural machine translation.
no code implementations • WS 2019 • Aarne Talman, Umut Sulubacak, Raúl Vázquez, Yves Scherrer, Sami Virpioja, Alessandro Raganato, Arvi Hurskainen, Jörg Tiedemann
In this paper, we present the University of Helsinki submissions to the WMT 2019 shared task on news translation in three language pairs: English-German, English-Finnish and Finnish-English.
1 code implementation • WS 2019 • Raúl Vázquez, Alessandro Raganato, Jörg Tiedemann, Mathias Creutz
In this paper, we propose a multilingual encoder-decoder architecture capable of obtaining multilingual sentence representations by means of incorporating an intermediate {\em attention bridge} that is shared across all languages.
no code implementations • LREC 2016 • José Camacho Collados, Claudio Delli Bovi, Alessandro Raganato, Roberto Navigli
Linking concepts and named entities to knowledge bases has become a crucial Natural Language Understanding task.