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https://ceur-ws.org/Vol-3033/keynote1.pdf
Returning the L in NLP: Why Language (Variety) Matters
and How to Embrace it in Our Models
Barbara Plank
Computer Science Department
IT University of Copenhagen
Abstract
NLP’s success today is driven by advances in modeling together with huge amounts of unla-
beled data to train language models. However, for many application scenarios like low-resource
languages, non-standard data and dialects we do not have access to labeled resources and even
unlabeled data might be scarce. Moreover, evaluation today largely focuses on standard splits,
yet language varies along many dimensions [3]. What is more is that for almost every NLP task,
the existence of a single perceived gold answer is at best an idealization.
In this talk, I will emphasize the importance of language variation in inputs and outputs and
its impact on NLP. I will outline ways on how to go about it. This includes recent work on how
to transfer models to low-resource languages and language variants [5, 6], the use of incidental
(or fortuitous) learning signals such as genre for dependency parsing [2] and learning beyond a
single ground truth [1, 3, 4].
Biography. Barbara Plank is Professor in the Computer Science Department at ITU (IT Univer-
sity of Copenhagen). She is also the Head of the Master in Data Science Program. She received
her PhD in Computational Linguistics from the University of Groningen. Her research interests
focus on Natural Language Processing, in particular transfer learning and adaptations, learn-
ing from beyond the text, and in general learning under limited supervision and fortuitous data
sources. She (co)-organised several workshops and international conferences, amongst which
the PEOPLES workshop (since 2016) and the first European NLP Summit (EurNLP 2019). Bar-
bara was general chair of the 22nd Northern Computational Linguistics conference (NoDaLiDa
2019) and workshop chair for ACL in 2019. Barbara is member of the advisory board of the
European Association for Computational Linguistics (EACL) and vice-president of the Northern
European Association for Language Technology (NEALT).
References
[1] Tommaso Fornaciari, Alexandra Uma, Silviu Paun, Barbara Plank, Dirk Hovy, and Massimo
Poesio. Beyond Black & White: Leveraging Annotator Disagreement via Soft-Label Multi-
Task Learning. In Proceedings of the 2021 Conference of the North American Chapter
of the Association for Computational Linguistics: Human Language Technologies, pages
2591–2597, 2021.
[2] Max Müller-Eberstein, Rob van der Goot, and Barbara Plank. Genre as Weak Supervision
for Cross-lingual Dependency Parsing. In Proceedings of the 2021 Conference on Empirical
Methods in Natural Language Processing, pages 4786–4802, 2021.
[3] Barbara Plank. What to do about non-standard (or non-canonical) language in NLP. In Pro-
ceedings of KONVENS 2016, Ruhr-University Bochum. Bochumer Linguistische Arbeits-
berichte, 2016.
[4] Barbara Plank, Dirk Hovy, and Anders Søgaard. Learning part-of-speech taggers with inter-
annotator agreement loss. In Proceedings of the 14th Conference of the European Chapter
of the Association for Computational Linguistics, pages 742–751, 2014.
[5] Barbara Plank, Kristian Nørgaard Jensen, and Rob van der Goot. DaN+: Danish nested
named entities and lexical normalization. In Proceedings of the 28th International Confer-
ence on Computational Linguistics, pages 6649–6662, 2020.
[6] Rob van der Goot, Ibrahim Sharaf, Aizhan Imankulova, Ahmet Üstün, Marija Stepanović,
Alan Ramponi, Siti Oryza Khairunnisa, Mamoru Komachi, and Barbara Plank. From
Masked Language Modeling to Translation: Non-English Auxiliary Tasks Improve Zero-
shot Spoken Language Understanding. In Proceedings of the 2021 Conference of the North
American Chapter of the Association for Computational Linguistics: Human Language
Technologies, pages 2479–2497, 2021.