no code implementations • COLING 2022 • Raúl Vázquez, Hande Celikkanat, Vinit Ravishankar, Mathias Creutz, Jörg Tiedemann
We analyze the learning dynamics of neural language and translation models using Loss Change Allocation (LCA), an indicator that enables a fine-grained analysis of parameter updates when optimizing for the loss function.
no code implementations • EMNLP (BlackboxNLP) 2020 • Hande Celikkanat, Sami Virpioja, Jörg Tiedemann, Marianna Apidianaki
Contextualized word representations encode rich information about syntax and semantics, alongside specificities of each context of use.
1 code implementation • 10 Apr 2023 • Aarne Talman, Hande Celikkanat, Sami Virpioja, Markus Heinonen, Jörg Tiedemann
This paper introduces Bayesian uncertainty modeling using Stochastic Weight Averaging-Gaussian (SWAG) in Natural Language Understanding (NLU) tasks.
no code implementations • ACL 2021 • Ra{\'u}l V{\'a}zquez, Hande Celikkanat, Mathias Creutz, J{\"o}rg Tiedemann
Various studies show that pretrained language models such as BERT cannot straightforwardly replace encoders in neural machine translation despite their enormous success in other tasks.
1 code implementation • WS (NoDaLiDa) 2019 • Aarne Talman, Antti Suni, Hande Celikkanat, Sofoklis Kakouros, Jörg Tiedemann, Martti Vainio
In this paper we introduce a new natural language processing dataset and benchmark for predicting prosodic prominence from written text.
Ranked #1 on Prosody Prediction on Helsinki Prosody Corpus