no code implementations • 2 Apr 2024 • Risto Luukkonen, Jonathan Burdge, Elaine Zosa, Aarne Talman, Ville Komulainen, Väinö Hatanpää, Peter Sarlin, Sampo Pyysalo
The pretraining of state-of-the-art large language models now requires trillions of words of text, which is orders of magnitude more than available for the vast majority of languages.
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.
1 code implementation • *SEM (NAACL) 2022 • Aarne Talman, Marianna Apidianaki, Stergios Chatzikyriakidis, Jörg Tiedemann
A central question in natural language understanding (NLU) research is whether high performance demonstrates the models' strong reasoning capabilities.
1 code implementation • NoDaLiDa 2021 • Aarne Talman, Marianna Apidianaki, Stergios Chatzikyriakidis, Jörg Tiedemann
We propose a new diagnostics test suite which allows to assess whether a dataset constitutes a good testbed for evaluating the models' meaning understanding capabilities.
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
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.
no code implementations • WS 2019 • Aarne Talman, Stergios Chatzikyriakidis
We show that models trained on a natural language inference dataset drawn from one benchmark fail to perform well in others, even if the notion of inference assumed in these benchmarks is the same or similar.
1 code implementation • 27 Aug 2018 • Aarne Talman, Anssi Yli-Jyrä, Jörg Tiedemann
We can show that the sentence embeddings learned in this way can be utilized in a wide variety of transfer learning tasks, outperforming InferSent on 7 out of 10 and SkipThought on 8 out of 9 SentEval sentence embedding evaluation tasks.
Ranked #5 on Natural Language Inference on SciTail