Benchmarking the Simplification of Dutch Municipal Text

Daniel Vlantis, Iva Gornishka, Shuai Wang

Abstract
Text simplification (TS) makes written information more accessible to all people, especially those with cognitive or language impairments. Despite much progress in TS due to advances in NLP technology, the bottleneck issue of lack of data for low-resource languages persists. Dutch is one of these languages that lack a monolingual simplification corpus. In this paper, we use English as a pivot language for the simplification of Dutch medical and municipal text. We experiment with augmenting training data and corpus choice for this pivot-based approach. We compare the results to a baseline and an end-to-end LLM approach using the GPT 3.5 Turbo model. Our evaluation shows that, while we can substantially improve the results of the pivot pipeline, the zero-shot end-to-end GPT-based simplification performs better on all metrics. Our work shows how an existing pivot-based pipeline can be improved for simplifying Dutch medical text. Moreover, we provide baselines for the comparison in the domain of Dutch municipal text and make our corresponding evaluation dataset publicly available.
Anthology ID:
2024.lrec-main.199
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
2217–2226
Language:
URL:
https://aclanthology.org/2024.lrec-main.199
DOI:
Bibkey:
Cite (ACL):
Daniel Vlantis, Iva Gornishka, and Shuai Wang. 2024. Benchmarking the Simplification of Dutch Municipal Text. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 2217–2226, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Benchmarking the Simplification of Dutch Municipal Text (Vlantis et al., LREC-COLING 2024)
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https://preview.aclanthology.org/add_acl24_videos/2024.lrec-main.199.pdf
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