Abstract
The Dutch Ministry of Social Affairs and Employment has to regularly explore the content of labour agreements. Studies on topics such as diversity and work flexibility are conducted on the regular basis by means of specialised questionnaires. We show that a relatively small domain-specific dataset allows to train the state-of-the-art extractive question answering (QA) system to answer these questions automatically. This paper introduces the new dataset, Dutch SQuAD, obtained by machine translating the original SQuAD v2.0 dataset from English to Dutch (made publicly available on https://gitlab.com/niels.rouws/dutch-squad-v2.0). Our results demonstrate that it allows us to improve domain adaptation for QA models by pre-training these models first on this general domain machine-translated dataset. In our experiments, we compare fine-tuning the pre-trained Dutch versus multilingual language models: BERTje, RobBERT, and mBERT. Our results demonstrate that domain adaptation of the QA models that were first trained on a general-domain machine-translated QA dataset to the Dutch labour agreement dataset outperforms the models that were directly fine-tuned on the in-domain documents. We also compare several ensemble learning techniques and show how they allow to achieve additional performance gain on this task. A new approach of string-based voting is introduced and we showed that it performs on par with a previously proposed approach.
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Rouws, N.J., Vakulenko, S., Katrenko, S. (2022). Dutch SQuAD and Ensemble Learning for Question Answering from Labour Agreements. In: Leiva, L.A., Pruski, C., Markovich, R., Najjar, A., Schommer, C. (eds) Artificial Intelligence and Machine Learning. BNAIC/Benelearn 2021. Communications in Computer and Information Science, vol 1530. Springer, Cham. https://doi.org/10.1007/978-3-030-93842-0_9
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