Wietse de Vries


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Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages
Wietse de Vries | Martijn Wieling | Malvina Nissim
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Cross-lingual transfer learning with large multilingual pre-trained models can be an effective approach for low-resource languages with no labeled training data. Existing evaluations of zero-shot cross-lingual generalisability of large pre-trained models use datasets with English training data, and test data in a selection of target languages. We explore a more extensive transfer learning setup with 65 different source languages and 105 target languages for part-of-speech tagging. Through our analysis, we show that pre-training of both source and target language, as well as matching language families, writing systems, word order systems, and lexical-phonetic distance significantly impact cross-lingual performance. The findings described in this paper can be used as indicators of which factors are important for effective zero-shot cross-lingual transfer to zero- and low-resource languages.


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A Multilingual Approach to Identify and Classify Exceptional Measures against COVID-19
Georgios Tziafas | Eugenie de Saint-Phalle | Wietse de Vries | Clara Egger | Tommaso Caselli
Proceedings of the Natural Legal Language Processing Workshop 2021

The COVID-19 pandemic has witnessed the implementations of exceptional measures by governments across the world to counteract its impact. This work presents the initial results of an on-going project, EXCEPTIUS, aiming to automatically identify, classify and com- pare exceptional measures against COVID-19 across 32 countries in Europe. To this goal, we created a corpus of legal documents with sentence-level annotations of eight different classes of exceptional measures that are im- plemented across these countries. We evalu- ated multiple multi-label classifiers on a manu- ally annotated corpus at sentence level. The XLM-RoBERTa model achieves highest per- formance on this multilingual multi-label clas- sification task, with a macro-average F1 score of 59.8%.

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As Good as New. How to Successfully Recycle English GPT-2 to Make Models for Other Languages
Wietse de Vries | Malvina Nissim
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Adapting Monolingual Models: Data can be Scarce when Language Similarity is High
Wietse de Vries | Martijn Bartelds | Malvina Nissim | Martijn Wieling
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021


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What’s so special about BERT’s layers? A closer look at the NLP pipeline in monolingual and multilingual models
Wietse de Vries | Andreas van Cranenburgh | Malvina Nissim
Findings of the Association for Computational Linguistics: EMNLP 2020

Peeking into the inner workings of BERT has shown that its layers resemble the classical NLP pipeline, with progressively more complex tasks being concentrated in later layers. To investigate to what extent these results also hold for a language other than English, we probe a Dutch BERT-based model and the multilingual BERT model for Dutch NLP tasks. In addition, through a deeper analysis of part-of-speech tagging, we show that also within a given task, information is spread over different parts of the network and the pipeline might not be as neat as it seems. Each layer has different specialisations, so that it may be more useful to combine information from different layers, instead of selecting a single one based on the best overall performance.