Daniel Varab

NLP Researcher


News

Resume

Resources

Publications

  • Daniel Varab and Yumo Xu. 2023. Abstractive Summarizers are Excellent Extractive Summarizers. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 330–339, Toronto, Canada. Association for Computational Linguistics.
  • Dennis Ulmer, Elisa Bassignana, Max Müller-Eberstein, Daniel Varab, Mike Zhang, Rob van der Goot, Christian Hardmeier, and Barbara Plank. 2022. Experimental Standards for Deep Learning in Natural Language Processing Research. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 2673–2692, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
  • 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, and Daniel Varab. 2021. The Danish Gigaword Corpus. In Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa), pages 413–421, Reykjavik, Iceland (Online). Linköping University Electronic Press, Sweden.
  • Daniel Varab and Natalie Schluter. 2021. MassiveSumm: a very large-scale, very multilingual, news summarisation dataset. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 10150–10161, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
  • Daniel Varab and Natalie Schluter. 2020. DaNewsroom: A Large-scale Danish Summarisation Dataset. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 6731–6739, Marseille, France. European Language Resources Association.
  • Daniel Varab and Natalie Schluter. 2019. UniParse: A universal graph-based parsing toolkit. In Proceedings of the 22nd Nordic Conference on Computational Linguistics, pages 406–410, Turku, Finland. Linköping University Electronic Press.
  • Natalie Schluter and Daniel Varab. 2018. When data permutations are pathological: the case of neural natural language inference. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4935–4939, Brussels, Belgium. Association for Computational Linguistics.

Education

Reviewing

Contact

🐣 @danielvarab 📨 danielvarab[at]gmail.com