Anna Langedijk


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ChapGTP, ILLC’s Attempt at Raising a BabyLM: Improving Data Efficiency by Automatic Task Formation
Jaap Jumelet | Michael Hanna | Marianne de Heer Kloots | Anna Langedijk | Charlotte Pouw | Oskar van der Wal
Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning


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Meta-Learning for Fast Cross-Lingual Adaptation in Dependency Parsing
Anna Langedijk | Verna Dankers | Phillip Lippe | Sander Bos | Bryan Cardenas Guevara | Helen Yannakoudakis | Ekaterina Shutova
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Meta-learning, or learning to learn, is a technique that can help to overcome resource scarcity in cross-lingual NLP problems, by enabling fast adaptation to new tasks. We apply model-agnostic meta-learning (MAML) to the task of cross-lingual dependency parsing. We train our model on a diverse set of languages to learn a parameter initialization that can adapt quickly to new languages. We find that meta-learning with pre-training can significantly improve upon the performance of language transfer and standard supervised learning baselines for a variety of unseen, typologically diverse, and low-resource languages, in a few-shot learning setup.


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Generalising to German Plural Noun Classes, from the Perspective of a Recurrent Neural Network
Verna Dankers | Anna Langedijk | Kate McCurdy | Adina Williams | Dieuwke Hupkes
Proceedings of the 25th Conference on Computational Natural Language Learning

Inflectional morphology has since long been a useful testing ground for broader questions about generalisation in language and the viability of neural network models as cognitive models of language. Here, in line with that tradition, we explore how recurrent neural networks acquire the complex German plural system and reflect upon how their strategy compares to human generalisation and rule-based models of this system. We perform analyses including behavioural experiments, diagnostic classification, representation analysis and causal interventions, suggesting that the models rely on features that are also key predictors in rule-based models of German plurals. However, the models also display shortcut learning, which is crucial to overcome in search of more cognitively plausible generalisation behaviour.