no code implementations • NoDaLiDa 2021 • Leon Strømberg-Derczynski, Manuel Ciosici, Rebekah Baglini, Morten H. Christiansen, Jacob Aarup Dalsgaard, Riccardo Fusaroli, Peter Juel Henrichsen, Rasmus Hvingelby, Andreas Kirkedal, Alex Speed Kjeldsen, Claus Ladefoged, Finn Årup Nielsen, Jens Madsen, Malte Lau Petersen, Jonathan Hvithamar Rystrøm, Daniel Varab
Danish language technology has been hindered by a lack of broad-coverage corpora at the scale modern NLP prefers.
1 code implementation • EMNLP 2021 • Daniel Varab, Natalie Schluter
We present the first investigation on the efficacy of resource building from news platforms in the low-resource language setting.
no code implementations • 31 Aug 2024 • Daniel Varab, Christian Hardmeier
Recent work has suggested that end-to-end system designs for cross-lingual summarization are competitive solutions that perform on par or even better than traditional pipelined designs.
1 code implementation • 13 Apr 2022 • Dennis Ulmer, Elisa Bassignana, Max Müller-Eberstein, Daniel Varab, Mike Zhang, Rob van der Goot, Christian Hardmeier, Barbara Plank
The field of Deep Learning (DL) has undergone explosive growth during the last decade, with a substantial impact on Natural Language Processing (NLP) as well.
no code implementations • 7 May 2020 • Leon Strømberg-Derczynski, Manuel R. Ciosici, Rebekah Baglini, Morten H. Christiansen, Jacob Aarup Dalsgaard, Riccardo Fusaroli, Peter Juel Henrichsen, Rasmus Hvingelby, Andreas Kirkedal, Alex Speed Kjeldsen, Claus Ladefoged, Finn Årup Nielsen, Malte Lau Petersen, Jonathan Hvithamar Rystrøm, Daniel Varab
Danish language technology has been hindered by a lack of broad-coverage corpora at the scale modern NLP prefers.
no code implementations • LREC 2020 • Daniel Varab, Natalie Schluter
To support the comparison of future automatic summarisation systems for Danish, we include system performance on this dataset of strong well-established unsupervised baseline systems, together with an oracle extractive summariser, which is the first account of automatic summarisation system performance for Danish.
1 code implementation • EMNLP 2018 • Natalie Schluter, Daniel Varab
Consider two competitive machine learning models, one of which was considered state-of-the art, and the other a competitive baseline.
1 code implementation • WS (NoDaLiDa) 2019 • Daniel Varab, Natalie Schluter
This paper describes the design and use of the graph-based parsing framework and toolkit UniParse, released as an open-source python software package.