457 citations · 681 across the 10 of their papers we have counts for
6 papers · 1 filter
Multilingual is not enough: BERT for Finnish
Antti Virtanen, Jenna Kanerva, Rami Ilo +5
Deep learning-based language models pretrained on large unannotated text corpora have been demonstrated to allow efficient transfer learning for natural language processing, with r…
Is Multilingual BERT Fluent in Language Generation?
Samuel Rönnqvist, Jenna Kanerva, Tapio Salakoski +1
The multilingual BERT model is trained on 104 languages and meant to serve as a universal language model and tool for encoding sentences. We explore how well the model performs on…
Template-free Data-to-Text Generation of Finnish Sports News
Jenna Kanerva, Samuel Rönnqvist, Riina Kekki +2
News articles such as sports game reports are often thought to closely follow the underlying game statistics, but in practice they contain a notable amount of background knowledge,…
Leveraging Text Repetitions and Denoising Autoencoders in OCR Post-correction
Kai Hakala, Aleksi Vesanto, Niko Miekka +2
A common approach for improving OCR quality is a post-processing step based on models correcting misdetected characters and tokens. These models are typically trained on aligned pa…
Universal Lemmatizer: A Sequence to Sequence Model for Lemmatizing Universal Dependencies Treebanks
Jenna Kanerva, Filip Ginter, Tapio Salakoski
In this paper we present a novel lemmatization method based on a sequence-to-sequence neural network architecture and morphosyntactic context representation. In the proposed method…
Lexical Adaptation of Link Grammar to the Biomedical Sublanguage: a Comparative Evaluation of Three Approaches
Sampo Pyysalo, Tapio Salakoski, Sophie Aubin +1
We study the adaptation of Link Grammar Parser to the biomedical sublanguage with a focus on domain terms not found in a general parser lexicon. Using two biomedical corpora, we im…