Yves Scherrer

• Tiedemann, Jörg; Aulamo, Mikko; Bakshandaeva, Daria; Boggia, Michele; Grönroos, Stig-Arne & Nieminen, Tommi [Show all 10 contributors for this article] (2023). Democratizing neural machine translation with OPUS-MT. Language Resources and Evaluation. ISSN 1574-020X. p. 67–79. doi: 10.1007/s10579-023-09704-w. Full text in Research Archive
• Kuparinen, Olli; Miletic, Aleksandra & Scherrer, Yves (2023). Dialect-to-Standard Normalization: A Large-Scale Multilingual Evaluation. In Bouamor, Houda; Pino, Juan Miguel Rey & Bali, Kalika (Ed.), Findings of the Association for Computational Linguistics: EMNLP 2023. Association for Computational Linguistics. ISSN 979-8-89176-061-5. p. 13814–13828. Full text in Research Archive Show summary

  Text normalization methods have been commonly applied to historical language or user-generated content, but less often to dialectal transcriptions. In this paper, we introduce dialect-to-standard normalization – i.e., mapping phonetic transcriptions from different dialects to the orthographic norm of the standard variety – as a distinct sentence-level character transduction task and provide a large-scale analysis of dialect-to-standard normalization methods. To this end, we compile a multilingual dataset covering four languages: Finnish, Norwegian, Swiss German and Slovene. For the two biggest corpora, we provide three different data splits corresponding to different use cases for automatic normalization. We evaluate the most successful sequence-to-sequence model architectures proposed for text normalization tasks using different tokenization approaches and context sizes. We find that a character-level Transformer trained on sliding windows of three words works best for Finnish, Swiss German and Slovene, whereas the pre-trained byT5 model using full sentences obtains the best results for Norwegian. Finally, we perform an error analysis to evaluate the effect of different data splits on model performance.

• Siewert, Janine; Wieling, Martijn & Scherrer, Yves (2023). Changing usage of Low Saxon auxiliary and modal verbs. In Tahmasebi, Nina; Montariol, Syrielle; Dubossarsky, Haim; Kutuzov, Andrei; Hengchen, Simon; Alfter, David; Periti, Francesco & Cassotti, Pierluigi (Ed.), Proceedings of the 4th Workshop on Computational Approaches to Historical Language Change. Association for Computational Linguistics. ISSN 979-8-89176-043-1. p. 112–118. Full text in Research Archive Show summary

  We investigate the usage of auxiliary and modal verbs in Low Saxon dialects from both Germany and the Netherlands based on word vectors, and compare developments in the modern language to Middle Low Saxon. Although most of these function words have not been affected by lexical replacement, changes in usage that likely at least partly result from contact with the state languages can still be observed.

• Scherrer, Yves; Miletić, Aleksandra & Kuparinen, Olli (2023). The Helsinki-NLP Submissions at NADI 2023 Shared Task: Walking the Baseline. In Sawaf, Hassan; El-Beltagy, Samhaa; Zaghouani, Wajdi; Magdy, Walid; Abdelali, Ahmed; Tomeh, Nadi; Abu Farha, Ibrahim; Habash, Nizar; Khalifa, Salam; Keleg, Amr; Haddad, Hatem; Zitouni, Imed; Mrini, Khalil & Almatham, Rawan (Ed.), Proceedings of ArabicNLP 2023. Association for Computational Linguistics. ISSN 978-1-959429-27-2. p. 670–677. Full text in Research Archive Show summary

  The Helsinki-NLP team participated in the NADI 2023 shared tasks on Arabic dialect translation with seven submissions. We used statistical (SMT) and neural machine translation (NMT) methods and explored character- and subword-based data preprocessing. Our submissions placed second in both tracks. In the open track, our winning submission is a character-level SMT system with additional Modern Standard Arabic language models. In the closed track, our best BLEU scores were obtained with the leave-as-is baseline, a simple copy of the input, and narrowly followed by SMT systems. In both tracks, fine-tuning existing multilingual models such as AraT5 or ByT5 did not yield superior performance compared to SMT.

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