Jonathan David Mutal


2023

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Improving Standard German Captioning of Spoken Swiss German: Evaluating Multilingual Pre-trained Models
Jonathan David Mutal | Pierrette Bouillon | Johanna Gerlach | Marianne Starlander
Proceedings of Machine Translation Summit XIX, Vol. 2: Users Track

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.

2022

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Standard German Subtitling of Swiss German TV content: the PASSAGE Project
Jonathan David Mutal | Pierrette Bouillon | Johanna Gerlach | Veronika Haberkorn
Proceedings of the Thirteenth Language Resources and Evaluation Conference

In Switzerland, two thirds of the population speak Swiss German, a primarily spoken language with no standardised written form. It is widely used on Swiss TV, for example in news reports, interviews or talk shows, and subtitles are required for people who cannot understand this spoken language. This paper focuses on the task of automatic Standard German subtitling of spoken Swiss German, and more specifically on the translation of a normalised Swiss German speech recognition result into Standard German suitable for subtitles. Our contribution consists of a comparison of different statistical and deep learning MT systems for this task and an aligned corpus of normalised Swiss German and Standard German subtitles. Results of two evaluations, automatic and human, show that the systems succeed in improving the content, but are currently not capable of producing entirely correct Standard German.