Improving Standard German Captioning of Spoken Swiss German: Evaluating Multilingual Pre-trained Models
Jonathan David Mutal, Pierrette Bouillon, Johanna Gerlach, Marianne Starlander
Abstract
Multilingual pre-trained language models are often the best alternative in low-resource settings. In the context of a cascade architecture for automatic Standard German captioning of spoken Swiss German, we evaluate different models on the task of transforming normalised Swiss German ASR output into Standard German. Instead of training a large model from scratch, we fine-tuned publicly available pre-trained models, which reduces the cost of training high-quality neural machine translation models. Results show that pre-trained multilingual models achieve the highest scores, and that a higher number of languages included in pre-training improves the performance. We also observed that the type of source and target included in fine-tuning data impacts the results.- Anthology ID:
- 2023.mtsummit-users.6
- Volume:
- Proceedings of Machine Translation Summit XIX, Vol. 2: Users Track
- Month:
- September
- Year:
- 2023
- Address:
- Macau SAR, China
- Editors:
- Masaru Yamada, Felix do Carmo
- Venue:
- MTSummit
- SIG:
- Publisher:
- Asia-Pacific Association for Machine Translation
- Note:
- Pages:
- 65–76
- Language:
- URL:
- https://aclanthology.org/2023.mtsummit-users.6
- DOI:
- Cite (ACL):
- Jonathan David Mutal, Pierrette Bouillon, Johanna Gerlach, and Marianne Starlander. 2023. Improving Standard German Captioning of Spoken Swiss German: Evaluating Multilingual Pre-trained Models. In Proceedings of Machine Translation Summit XIX, Vol. 2: Users Track, pages 65–76, Macau SAR, China. Asia-Pacific Association for Machine Translation.
- Cite (Informal):
- Improving Standard German Captioning of Spoken Swiss German: Evaluating Multilingual Pre-trained Models (Mutal et al., MTSummit 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2023.mtsummit-users.6.pdf