Update README.md

README.md

@@ -53,11 +53,11 @@ task_categories:
 
 ### Dataset Summary
 
-This is a classification dataset created from a subset of the [Norwegian Parliament Speeches](https://www.nb.no/sprakbanken/en/resource-catalogue/oai-nb-no-sbr-58/). This dataset contains text phrases from the political parties Fremskrittspartiet and Sosialistisk Venstreparti. The dataset is annotated with the party the speaker, as well as a timestamp. The classification task is to, simply by looking at the text, being able to predict is the speech was done by a representative from Fremskrittspartiet or from SV.
+This is a classification dataset created from a subset of the [Talk of Norway](https://www.nb.no/sprakbanken/ressurskatalog/oai-repo-clarino-uib-no-11509-123/). This dataset contains text phrases from the political parties Fremskrittspartiet and Sosialistisk Venstreparti. The dataset is annotated with the party the speaker, as well as a timestamp. The classification task is to, simply by looking at the text, being able to predict is the speech was done by a representative from Fremskrittspartiet or from SV.
 
 ### Supported Tasks and Leaderboards
 
-[More Information Needed]
+This dataset is meant for classification. Results can for instance be viewed in [this article](https://arxiv.org/abs/2104.09617).
 
 ### Languages
 
@@ -69,7 +69,7 @@ The text in the dataset is in Norwegian.
 
 Example of one instance in the dataset.
 
-```{'label': 0, 'text': 'Verre er det med slagsmålene .'}```
+```{'label': 0, 'text': 'Verre er det med slagsmålene .', 'date': '2016-01-15'}```
 
 ### Data Fields
 
@@ -95,19 +95,32 @@ The dataset is balanced on political party.
 This dataset is based on the publicly available information by Norwegian Parliament (Storting) and created by the National Library of Norway AI-Lab to benchmark their language models.
 
 ## Additional Information
+The [Talk of Norway dataset] is also available [here](https://www.nb.no/sprakbanken/ressurskatalog/oai-repo-clarino-uib-no-11509-123/).
 
 ### Licensing Information
 
-This work is licensed under a Creative Commons Attribution 4.0 International License
+This work is licensed under a Creative Commons Attribution 4.0 International License.
 
-### Citation Information
 
-```latex
-@misc{--,
-      title={--},
-      author={--},
-      year={2021},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL}
-}
+### Citation Information
+The following article can be quoted when referring to this dataset, since it is the first study that are using the dataset for evaluating a language model:
 ```
+@inproceedings{kummervold-etal-2021-operationalizing,
+title = {Operationalizing a National Digital Library: The Case for a {N}orwegian Transformer Model},
+author = {Kummervold, Per E  and
+De la Rosa, Javier  and
+Wetjen, Freddy  and
+Brygfjeld, Svein Arne",
+booktitle = {Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)},
+year = "2021",
+address = "Reykjavik, Iceland (Online)",
+publisher = {Link{"o}ping University Electronic Press, Sweden},
+url = "https://aclanthology.org/2021.nodalida-main.3",
+pages = "20--29",
+abstract = "In this work, we show the process of building a large-scale training set from digital and digitized collections at a national library.
+The resulting Bidirectional Encoder Representations from Transformers (BERT)-based language model for Norwegian outperforms multilingual BERT (mBERT) models
+in several token and sequence classification tasks for both Norwegian Bokm{aa}l and Norwegian Nynorsk. Our model also improves the mBERT performance for other
+languages present in the corpus such as English, Swedish, and Danish. For languages not included in the corpus, the weights degrade moderately while keeping strong multilingual properties. Therefore,
+we show that building high-quality models within a memory institution using somewhat noisy optical character recognition (OCR) content is feasible, and we hope to pave the way for other memory institutions to follow.",
+}
+```