@inproceedings{flansmose-mikkelsen-etal-2022-ddisco,
title = "{DD}is{C}o: A Discourse Coherence Dataset for {D}anish",
author = "Flansmose Mikkelsen, Linea and
Kinch, Oliver and
Jess Pedersen, Anders and
Lacroix, Oph{\'e}lie",
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.260/",
pages = "2440--2445",
abstract = "To date, there has been no resource for studying discourse coherence on real-world Danish texts. Discourse coherence has mostly been approached with the assumption that incoherent texts can be represented by coherent texts in which sentences have been shuffled. However, incoherent real-world texts rarely resemble that. We thus present DDisCo, a dataset including text from the Danish Wikipedia and Reddit annotated for discourse coherence. We choose to annotate real-world texts instead of relying on artificially incoherent text for training and testing models. Then, we evaluate the performance of several methods, including neural networks, on the dataset."
}
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%0 Conference Proceedings
%T DDisCo: A Discourse Coherence Dataset for Danish
%A Flansmose Mikkelsen, Linea
%A Kinch, Oliver
%A Jess Pedersen, Anders
%A Lacroix, Ophélie
%Y Calzolari, Nicoletta
%Y Béchet, Frédéric
%Y Blache, Philippe
%Y Choukri, Khalid
%Y Cieri, Christopher
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Isahara, Hitoshi
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Hélène
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Thirteenth Language Resources and Evaluation Conference
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F flansmose-mikkelsen-etal-2022-ddisco
%X To date, there has been no resource for studying discourse coherence on real-world Danish texts. Discourse coherence has mostly been approached with the assumption that incoherent texts can be represented by coherent texts in which sentences have been shuffled. However, incoherent real-world texts rarely resemble that. We thus present DDisCo, a dataset including text from the Danish Wikipedia and Reddit annotated for discourse coherence. We choose to annotate real-world texts instead of relying on artificially incoherent text for training and testing models. Then, we evaluate the performance of several methods, including neural networks, on the dataset.
%U https://aclanthology.org/2022.lrec-1.260/
%P 2440-2445
Markdown (Informal)
[DDisCo: A Discourse Coherence Dataset for Danish](https://aclanthology.org/2022.lrec-1.260/) (Flansmose Mikkelsen et al., LREC 2022)
ACL
- Linea Flansmose Mikkelsen, Oliver Kinch, Anders Jess Pedersen, and Ophélie Lacroix. 2022. DDisCo: A Discourse Coherence Dataset for Danish. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 2440–2445, Marseille, France. European Language Resources Association.