1 code implementation • 13 Apr 2021 • Andres Garcia-Silva, Cristian Berrio, Jose Manuel Gomez-Perez
In this paper we shed light on the impact of fine-tuning over social media data in the internal representations of neural language models.
no code implementations • 13 Apr 2021 • Andres Garcia-Silva, Ronald Denaux, Jose Manuel Gomez-Perez
In essence, embedding algorithms work by optimizing the distance between a word and its usual context in order to generate an embedding space that encodes the distributional representation of words.
no code implementations • EACL 2021 • Georg Rehm, Stelios Piperidis, Kalina Bontcheva, Jan Hajic, Victoria Arranz, Andrejs Vasi{\c{l}}jevs, Gerhard Backfried, Jose Manuel Gomez-Perez, Ulrich Germann, R{\'e}mi Calizzano, Nils Feldhus, Stefanie Hegele, Florian Kintzel, Katrin Marheinecke, Julian Moreno-Schneider, Dimitris Galanis, Penny Labropoulou, Miltos Deligiannis, Katerina Gkirtzou, Athanasia Kolovou, Dimitris Gkoumas, Leon Voukoutis, Ian Roberts, Jana Hamrlova, Dusan Varis, Lukas Kacena, Khalid Choukri, Val{\'e}rie Mapelli, Micka{\"e}l Rigault, Julija Melnika, Miro Janosik, Katja Prinz, Andres Garcia-Silva, Cristian Berrio, Ondrej Klejch, Steve Renals
Europe is a multilingual society, in which dozens of languages are spoken.
1 code implementation • 20 Jan 2021 • Andres Garcia-Silva, Jose Manuel Gomez-Perez
We compare and evaluate the subset of the most attended words with feature selection methods normally used for text classification in order to characterize self-attention as a possible feature selection approach.
1 code implementation • WS 2019 • Andres Garcia-Silva, Cristian Berrio, Jos{\'e} Manuel G{\'o}mez-P{\'e}rez
Fine-tuning pre-trained language models has significantly advanced the state of art in a wide range of NLP downstream tasks.
no code implementations • 27 Sep 2018 • Andres Garcia-Silva, Jose Manuel Gomez-Perez, Raul Palma, Marcin Krystek, Simone Mantovani, Federica Foglini, Valentina Grande, Francesco De Leo, Stefano Salvi, Elisa Trasati, Vito Romaniello, Mirko Albani, Cristiano Silvagni, Rosemarie Leone, Fulvio Marelli, Sergio Albani, Michele Lazzarini, Hazel J. Napier, Helen M. Glaves, Timothy Aldridge, Charles Meertens, Fran Boler, Henry W. Loescher, Christine Laney, Melissa A Genazzio, Daniel Crawl, Ilkay Altintas
Data-intensive science communities are progressively adopting FAIR practices that enhance the visibility of scientific breakthroughs and enable reuse.