23 citations · 38 across the 8 of their papers we have counts for
8 papers
Poro 34B and the Blessing of Multilinguality
Risto Luukkonen, Jonathan Burdge, Elaine Zosa +5
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 languag…
Uncertainty-Aware Natural Language Inference with Stochastic Weight Averaging
Aarne Talman, Hande Celikkanat, Sami Virpioja +2
This paper introduces Bayesian uncertainty modeling using Stochastic Weight Averaging-Gaussian (SWAG) in Natural Language Understanding (NLU) tasks. We apply the approach to standa…
How Does Data Corruption Affect Natural Language Understanding Models? A Study on GLUE datasets
Aarne Talman, Marianna Apidianaki, Stergios Chatzikyriakidis +1
A central question in natural language understanding (NLU) research is whether high performance demonstrates the models' strong reasoning capabilities. We present an extensive seri…
NLI Data Sanity Check: Assessing the Effect of Data Corruption on Model Performance
Aarne Talman, Marianna Apidianaki, Stergios Chatzikyriakidis +1
Pre-trained neural language models give high performance on natural language inference (NLI) tasks. But whether they actually understand the meaning of the processed sequences rema…
Predicting Prosodic Prominence from Text with Pre-trained Contextualized Word Representations
Aarne Talman, Antti Suni, Hande Celikkanat +3
In this paper we introduce a new natural language processing dataset and benchmark for predicting prosodic prominence from written text. To our knowledge this will be the largest p…
The University of Helsinki submissions to the WMT19 news translation task
Aarne Talman, Umut Sulubacak, Raúl Vázquez +5
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 Finnis…
Testing the Generalization Power of Neural Network Models Across NLI Benchmarks
Aarne Talman, Stergios Chatzikyriakidis
Neural network models have been very successful in natural language inference, with the best models reaching 90% accuracy in some benchmarks. However, the success of these models t…
Sentence Embeddings in NLI with Iterative Refinement Encoders
Aarne Talman, Anssi Yli-Jyrä, Jörg Tiedemann
Sentence-level representations are necessary for various NLP tasks. Recurrent neural networks have proven to be very effective in learning distributed representations and can be tr…