Yves Scherrer

• Aulamo, Mikko; Virpioja, Sami; Scherrer, Yves & Tiedemann, Jörg (2026). The Challenge of Finding Robust and Efficient Strategies for Training Machine Translation Models with Noisy Data. In Shterionov, Dimitar; Vanmassenhove, Eva; Sisto, Mirella De; Blain, Fred; Pourmostafa, Javed; Sharami, Roshan; Lepp, Lisa; Manna, Chiara; Rescigno, Argentina Anna; Karakanta, Aliana; Terryn, Ayla Rigouts; Lardelli, Manuel; Resende, Natalia; Murgolo, Elena; Hackenbuchner, Janica; Zaretskaya, Anna; Esplà-Gomis, Miquel; Etchegoyhen, Thierry; Gromann, Dagmar; Bawden, Rachel; Haddow, Barry; Szoc, Sara; Forcada, Mikel & Moniz, Helena (Ed.), Proceedings of the 26th Annual Conference of the European Association for Machine Translation. European Association for Machine Translation (EAMT). ISSN 9789403901404. p. 173–187. doi: 10.26116/9789403901404. Full text in Research Archive Show summary

  Most machine translation datasets come with a certain level of noise, and strategies for handling such data need to be robust and efficient. Data selection and filtering are challenging and may depend on expensive language-specific tools that are not necessarily available, especially for low-resource languages. This paper looks at training strategies that combine cheap heuristic filters with curriculum learning to implement iterative procedures that robustly operate on raw noisy data without expensive prior preprocessing and data selection. The intuition is that we can cluster data into buckets with varying noise levels and use different sets of buckets at different stages of MT model training. We test various strategies and compare them to pre-filtering approaches for a diverse set of low-resource languages and conclude that curriculum learning can improve robustness but does not necessarily lead to improved translation performance. Overall, the experiments demonstrate the importance of proper experimental workflows, which cannot easily generalize from one language pair and scenario to another.

• Qian, Shenbin & Scherrer, Yves (2026). Why do Large Language Models Fail in Low-resource Translation? Unraveling the Token Dynamics of Large Language Models for Machine Translation. In Shterionov, Dimitar; Vanmassenhove, Eva; Sisto, Mirella De; Blain, Fred; Pourmostafa, Javed; Sharami, Roshan; Lepp, Lisa; Manna, Chiara; Rescigno, Argentina Anna; Karakanta, Aliana; Terryn, Ayla Rigouts; Lardelli, Manuel; Resende, Natalia; Murgolo, Elena; Hackenbuchner, Janica; Zaretskaya, Anna; Esplà-Gomis, Miquel; Etchegoyhen, Thierry; Gromann, Dagmar; Bawden, Rachel; Haddow, Barry; Szoc, Sara; Forcada, Mikel & Moniz, Helena (Ed.), Proceedings of the 26th Annual Conference of the European Association for Machine Translation. European Association for Machine Translation (EAMT). ISSN 9789403901404. p. 31–56. doi: 10.26116/9789403901404. Full text in Research Archive Show summary

  Large Language Models (LLMs) have recently demonstrated strong performance in machine translation (MT). However, most prior work focuses on improving or benchmarking translation quality, offering limited insight into when and why LLM-based translation fails. In this work, we systematically analyze failure modes of LLMs in MT by evaluating 15 models, including four reasoning LLMs, across 22 language pairs (LPs) with varying resource levels. We find that non-English-centric LPs consistently yield lower COMET scores than English-centric pairs. To investigate the underlying causes, we introduce Token Activation Rate (TAR), a metric that captures how effectively a model utilizes language-specific tokens in its vocabulary during generation. We validate TAR as a proxy for language representation using models with known language distributions in the training data, and show that lower TAR is strongly associated with poorer translation performance. Furthermore, reasoning LLMs tend to generate more tokens when translating into low-TAR languages, suggesting a compensatory mechanism, although its impact on translation quality varies across models. Overall, our findings emphasize the importance of token-level dynamics in understanding MT performance of LLMs.

• Milano, Emilia; Plum, Alistair; Scherrer, Yves & Purschke, Christoph (2026). Language Ideologies in a Multilingual Society: An LLM-based Analysis of Luxembourgish News Comments. In Stranisci, Marco Antonio; Falk, Neele; Labat, Sofie; Lo, Soda Marem; Velutharambath, Aswathy; Weber, Sabine; Damiano, Rossana; Frenda, Simona; Hoste, Véronique; Kleinberg, Bennett; Klinger, Roman; Patti, Viviana; Plaza-del-Arco, Flor Miriam; Sap, Maarten & Yimam, Seid Muhie (Ed.), Proceedings of the 1st Workshop on Social Context (SoCon) and the 2nd Workshop on Integrating NLP and Psychology to Study Social Interactions (NLPSI) @ LREC 2026. European Language Resources Association. ISSN 9782493814654. p. 114–131. doi: 10.63317/48kwvvr63gms. Full text in Research Archive Show summary

  Detecting language ideologies is a valuable yet complex task for understanding how identities are constructed through discourse. In Luxembourg’s multicultural and multilingual society, language ideologies reflect more than simple preferences: they carry deep cultural and social meanings, shaping identities and social belonging. Following recent developments in applying Natural Language Processing tools to linguistics and social science, this paper explores the potential of large language models to assist in the detection of language ideologies. We manually annotate a corpus of user comments in Luxembourgish with predefined ideological categories and then evaluate the performance of large language models under varying prompt conditions to assess their ability to replicate these human annotations. Since Luxembourgish is a small language and poorly represented in the LLMs’ training data, we also investigate whether machine-translating the data to high-resource languages increases performance on the ideology detection task. Our findings suggest that, while LLMs are not yet fully optimized for a multi-class ideological annotation task, they are practical tools to identify language ideological content.

• Othamar, Elisabeth & Scherrer, Yves (2026). Norwegian Sign Language: Overview of Resources and Experiments with Automatic SignWriting Transcription. In Efthimiou, Eleni; Fotinea, Stavroula-Evita; Hanke, Thomas; Hochgesang, Julie A.; Mesch, Johanna & Schulder, Marc (Ed.), Proceedings of the LREC 2026 12th Workshop on the Representation and Processing of Sign Languages: Language in Motion. European Language Resources Association. ISSN 9782493814821. p. 408–418. doi: 10.63317/3unezo3kea9w. Full text in Research Archive Show summary

  Norwegian Sign Language (NTS) remains an under-resourced sign language despite its official recognition in Norway since 2022. The limited availability of structured, reusable, and publicly accessible datasets continues to hinder both linguistic research and the development of sign language technologies such as recognition and translation systems. This paper presents an overview of existing datasets and potential data sources for NTS, categorizing them by accessibility, format, and suitability for computational research. We further discuss legal, ethical, and practical considerations related to data reuse, including copyright and privacy constraints. In addition, we report on a series of pilot experiments exploring alternative data acquisition strategies, including dictionary videos, SignWriting resources, and broadcast news material. These preliminary experiments explore whether automatic SignWriting transcription can serve as an intermediate representation for NTS, and examine its potential role in sign identification within continuous signing. The aim of this work is both to document ongoing efforts and to support future initiatives toward the sustainable development of NTS resources.

• Fedorova, Mariia; Arefyev, Nikolay; Buljan, Maja; Helcl, Jindřich; Oepen, Stephan & Rønningstad, Egil [Show all 7 contributors for this article] (2026). OpenLID-v3: Improving the Precision of Closely Related Language Identification – An Experience Report. In Scherrer, Yves; Aepli, Noëmi; Blaschke, Verena; Jauhiainen, Tommi; Ljubešić, Nikola; Nakov, Preslav; Tiedemann, Jörg & Zampieri, Marcos (Ed.), Proceedings of the 13th Workshop on NLP for Similar Languages, Varieties and Dialects. Association for Computational Linguistics (ACL). ISSN 9798891763722. p. 275–292. doi: 10.18653/v1/2026.vardial-1.23. Full text in Research Archive Show summary

  Language identification (LID) is an essential step in building high-quality multilingual datasets from web data. Existing LID tools (such as OpenLID or GlotLID) often struggle to identify closely related languages and to distinguish valid natural language from noise, which contaminates language-specific subsets, especially for low-resource languages. In this work we extend the OpenLID classifier by adding more training data, merging problematic language variant clusters, and introducing a special label for marking noise. We call this extended system OpenLID-v3 and evaluate it against GlotLID on multiple benchmarks. During the development we focus on three groups of closely related languages (Bosnian, Croatian, and Serbian; Romance varieties of Northern Italy and Southern France; and Scandinavian languages) and contribute new evaluation datasets where existing ones are inadequate. We find that ensemble approaches improve precision but also substantially reduce coverage for low-resource languages.

• Intelligence, Network Regional Language and Artificial; Fischer, Hanna; Lameli, Alfred & Scherrer, Yves (2026). Regionale Sprache und Künstliche Intelligenz im Zeitalter der digitalen Transformation. Zeitschrift für Dialektologie und Linguistik. ISSN 0044-1449. doi: 10.25162/zdl-2026-0001. Full text in Research Archive Show summary

  For research on regional languages, the rapid development of AI technology in recent years offers significant innovative potential. At the same time, the discipline carries a particular responsibility when it comes to representing regional language in the digital sphere. The digital transformation confronts the field with three overarching development goals: (1) harnessing the potential of AI, (2) depicting cultural diversity, and (3) shaping digital transformation responsibly. This position paper defines these developmental goals and, through an assessment of the current situation, outlines the prerequisites and conditions currently in place for the use of AI-based methods in the field. The resulting challenges and desiderata lead to central guidelines that support these goals. Finally, concrete recommendations present measures for advancing research on regional languages.

• Miletić, Aleksandra; Mickus, Timothee & Scherrer, Yves (2026). Explainable linguistic variety classification: The case of Bosnian-Croatian-Montenegrin-Serbian. In Samardžić, Tanja (Eds.), PROCEEDINGS OF THE 1ST ReLDI CONFERENCE ON LANGUAGE SCIENCE AND TECHNOLOGY. ReLDI Centre, Belgrade. ISSN 9788690792207. p. 17–25. doi: 10.5281/zenodo.18488312. Full text in Research Archive Show summary

  This paper presents a preliminary evaluation and analysis of a method for explainable linguistic variety classification applied to Bosnian, Croatian, Montenegrin, and Serbian. Neural models have become the de-facto standard for most NLP tasks and applications, but the factors influencing their predictions remain to a large extent opaque. Using Twitter data, we aim to detect the linguistic features that contribute most to the classification decisions and conduct an extensive manual analysis of these features. The results show that the global model behaviour of neural classifiers aligns with existing knowledge about variation in these varieties, although the models struggle to identify features specific to a single variety, especially in the case of Bosnian and Montenegrin. Overall, the paper showcases the applicability of the examined explainability method in sociolinguistic contexts characterized by complex variation patterns, thereby demonstrating the interest of explainable machine learning methods for corpus-based variation research.

• Rowe, Jacqueline; Gibert, Ona De; Klimaszewski, Mateusz; Haley, Coleman; Birch, Alexandra & Scherrer, Yves (2025). EdinHelsOW WMT 2025 CreoleMT System Description: Improving Lusophone Creole Translation through Data Augmentation, Model Merging and LLM Post-editing. In Haddow, Barry; Kocmi, Tom; Koehn, Philipp & Monz, Christof (Ed.), Proceedings of the Tenth Conference on Machine Translation. Association for Computational Linguistics (ACL). ISSN 9798891763418. p. 1166–1182. doi: 10.18653/v1/2025.wmt-1.91. Full text in Research Archive Show summary

  In this work, we present our submissions to the unconstrained track of the System subtask of the WMT 2025 Creole Language Translation Shared Task. Of the 52 Creole languages included in the task, we focus on translation between English and seven Lusophone Creoles. Our approach leverages known strategies for low-resource machine translation, including back-translation and distillation of data, fine-tuning pre-trained multilingual models, and post-editing with large language models and lexicons. We also demonstrate that adding high-quality parallel Portuguese data in training, initialising Creole embeddings with Portuguese embedding weights, and strategically merging best checkpoints of different fine-tuned models all produce considerable gains in performance in certain translation directions. Our best models outperform the baselines on the Task test set for eight out of fourteen translation directions. When evaluated on a more diverse test set, they surpass the baselines in all but one direction.

• Mæhlum, Petter; Evensen, Anders Næss & Scherrer, Yves (2025). Improved Norwegian Bokmål Translations for FLORES. In Haddow, Barry; Kocmi, Tom; Koehn, Philipp & Monz, Christof (Ed.), Proceedings of the Tenth Conference on Machine Translation. Association for Computational Linguistics (ACL). ISSN 9798891763418. p. 1124–1132. doi: 10.18653/v1/2025.wmt-1.86. Full text in Research Archive Show summary

  FLORES+ is a collection of parallel datasets obtained by translation from originally English source texts. FLORES+ contains Norwegian translations for the two official written variants of Norwegian: Norwegian Bokmål and Norwegian Nynorsk. However, the earliest Bokmål version contained non-native-like mistakes, and even after a later revision, the dataset contained grammatical and lexical errors. This paper aims at correcting unambiguous mistakes, and thus creating a new version of the Bokmål dataset. At the same time, we provide a translation into Radical Bokmål, a sub-variety of Norwegian which is closer to Nynorsk in some aspects, while still being within the official norms for Bokmål. We discuss existing errors and differences in the various translations and the corrections that we provide.

• Fedorova, Mariia; Kutuzov, Andrey; Periti, Francesco & Scherrer, Yves (2025). Explaining novel senses using definition generation with open language models. In Christodoulopoulos, Christos; Chakraborty, Tanmoy; Rose, Carolyn & Peng, Violet (Ed.), Findings of the Association for Computational Linguistics: EMNLP 2025. Association for Computational Linguistics (ACL). ISSN 9798891763357. p. 22294–22302. doi: 10.18653/v1/2025.findings-emnlp.1214. Full text in Research Archive Show summary

  We apply definition generators based on open-weights large language models to the task of creating explanations of novel senses, taking target word usages as an input. To this end, we employ the datasets from the AXOLOTL’24 shared task on explainable semantic change modeling, which features Finnish, Russian and German languages. We fine-tune and provide publicly the open-source models performing higher than the best submissions of the aforementioned shared task, which employed closed proprietary LLMs. In addition, we find that encoder-decoder definition generators perform on par with their decoder-only counterparts.

• Scherrer, Yves; Goot, Rob van der & Mæhlum, Petter (2025). Findings of the VarDial Evaluation Campaign 2025: The NorSID Shared Task on Norwegian Slot, Intent and Dialect Identification. In Scherrer, Yves; Jauhiainen, Tommi; Ljubešić, Nikola; Nakov, Preslav; Tiedemann, Jörg & Zampieri, Marcos (Ed.), Proceedings of the 12th Workshop on NLP for Similar Languages, Varieties and Dialects. Association for Computational Linguistics (ACL). ISSN 9798891762084. p. 1–8. Full text in Research Archive
• Fedorova, Mariia; Frydenberg, Jonas Sebulon; Handford, Victoria; Langø, Victoria Ovedie Chruickshank; Willoch, Solveig Helene & Midtgaard, Marthe Løken [Show all 9 contributors for this article] (2025). Multi-label Scandinavian Language Identification (SLIDE). In Holdt, Špela Arhar; Ilinykh, Nikolai; Scalvini, Barbara; Bruton, Micaella; Debess, Iben Nyholm & Tudor, Crina Madalina (Ed.), Proceedings of the Third Workshop on Resources and Representations for Under-Resourced Languages and Domains (RESOURCEFUL-2025). Tartu Ülikool Kirjastus. ISSN 9789908531212. p. 179–189. Full text in Research Archive Show summary

  Identifying closely related languages at sentence level is difficult, in particular because it is often impossible to assign a sentence to a single language. In this paper, we focus on multi-label sentence-level Scandinavian language identification (LID) for Danish, Norwegian Bokmål, Norwegian Nynorsk, and Swedish. We present the Scandinavian Language Identification and Evaluation, SLIDE, a manually curated multi-label evaluation dataset and a suite of LID models with varying speed–accuracy tradeoffs. We demonstrate that the ability to identify multiple languages simultaneously is necessary for any accurate LID method, and present a novel approach to training such multi-label LID models.

• Midtgaard, Marthe Løken; Mæhlum, Petter & Scherrer, Yves (2025). LTG at VarDial 2025 NorSID: More and Better Training Data for Slot and Intent Detection. In Scherrer, Yves; Jauhiainen, Tommi; Ljubešić, Nikola; Nakov, Preslav; Tiedemann, Jörg & Zampieri, Marcos (Ed.), Proceedings of the 12th Workshop on NLP for Similar Languages, Varieties and Dialects. Association for Computational Linguistics (ACL). ISSN 9798891762084. p. 200–208. Full text in Research Archive Show summary

  This paper describes the LTG submission to the VarDial 2025 shared task, where we participate in the Norwegian slot and intent detection subtasks. The shared task focuses on Norwegian dialects, which present challenges due to their low-resource nature and variation. We test a variety of neural models and training data configurations, with the focus on improving and extending the available Norwegian training data. This includes automatically re-aligning slot spans in Norwegian Bokmål, as well as re-translating the original English training data into both Bokmål and Nynorsk. % to address dialectal diversity. We also re-annotate an external Norwegian dataset to augment the training data. Our best models achieve first place in both subtasks, achieving an span F1 score of 0.893 for slot filling and an accuracy of 0.980 for intent detection. Our results indicate that while translation quality is less critical, improving the slot labels has a notable impact on slot performance. Moreover, adding more standard Norwegian data improves performance, but incorporating even small amounts of dialectal data leads to greater gains.

• Rabus, Achim & Scherrer, Yves (2025). Dialects, Topic Models, and Border Effects: The Rusyn Case, Proceedings of the 10th Workshop on Slavic Natural Language Processing (Slavic NLP 2025). Association for Computational Linguistics (ACL). ISSN 9781959429579. p. 38–43. doi: 10.18653/v1/2025.bsnlp-1.5. Full text in Research Archive Show summary

  In this contribution, we present, discuss, and apply a data-driven approach for analyzing varieties of the Slavic minority language Carpathian Rusyn spoken in different countries in the Carpathian region. Using topic modeling, a method originally developed for text mining, we show that the Rusyn varieties are subject to border effects, i.e., vertical convergence and horizontal divergence, due to language contacts with their respective umbrella languages Polish, Slovak and Standard Ukrainian. Additionally, we show that the method is suitable for uncovering fieldworker isoglosses, i.e., different transcription principles in an otherwise homogeneous dataset.

• Scherrer, Yves & Kuparinen, Olli (2025). Interactive maps for corpus-based dialectology. In Johansson, Richard & Stymne, Sara (Ed.), Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025). University of Tartu Library. ISSN 9789908531090. p. 634–638. doi: https:/aclanthology.org/2025.nodalida-1.64/. Full text in Research Archive Show summary

  Traditional data collection methods in dialectology rely on structured surveys, whose results can be easily presented on printed or digital maps. But in recent years, corpora of transcribed dialect speech have become a precious alternative data source for data-driven linguistic analysis. For example, topic models can be advantageously used to discover both general dialectal variation patterns and specific linguistic features that are most characteristic for certain dialects. Multilingual (or rather, multilectal) language modeling tasks can also be used to learn speaker-specific embeddings. In connection with this paper, we introduce a website that presents the results of two recent studies in the form of interactive maps, allowing visitors to explore the effects of various parameter settings. The website covers two tasks (topic models and speaker embeddings) and three language areas (Finland, Norway, and German-speaking Switzerland). It is available at https://www.corcodial.net/ .

• Hopton, Zachary; Scherrer, Yves & Samardžić, Tanja (2025). Functional Lexicon in Subword Tokenization, Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics (ACL). ISSN 9798891761896. p. 7839–7854. doi: 10.18653/v1/2025.naacl-long.398. Full text in Research Archive Show summary

  The distinction between function and content units of the lexicon has been somewhat neglected in recent NLP work, but it could still be useful when working with low-resource languages, and, in particular, to improve cross-lingual transfer. In this paper, we investigate to what extent BPE subword tokenization can be used to identify units of the functional lexicon in a language without any annotated data. We analyze subword tokens in terms of their productivity and attempt to find thresholds that best distinguish function from content tokens. On a sample of seven diverse languages, we find that the best results are obtained with 50 BPE merges. We also show that this subword tokenization setting can be beneficial for the interlinear glossing task.

• Gibert, Ona de; Nieminen, Tommi; Scherrer, Yves & Tiedemann, Jörg (2025). OpusDistillery: A Configurable End-to-End Pipeline for Systematic Multilingual Distillation of Open NMT Models. In Johansson, Richard & Stymne, Sara (Ed.), Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025). University of Tartu Library. ISSN 9789908531090. p. 201–208. doi: https:/aclanthology.org/2025.nodalida-1.20/. Full text in Research Archive Show summary

  In this work, we introduce OpusDistillery, a novel framework to streamline the Knowledge Distillation (KD) process of multilingual NMT models. OpusDistillery’s main features are the integration of openly available teacher models from OPUS-MT and Hugging Face, comprehensive multilingual support and robust GPU utilization tracking. We describe the tool in detail and discuss the individual contributions of its pipeline components, demonstrating its flexibility for different use cases. OpusDistillery is open-source and released under a permissive license, aiming to facilitate further research and development in the field of multilingual KD for any sequence-to-sequence task. Our code is available at https://github.com/Helsinki-NLP/OpusDistillery.

• Fedorova, Mariia; Kutuzov, Andrei & Scherrer, Yves (2024). Definition generation for lexical semantic change detection, Findings of the Association for Computational Linguistics ACL 2024. Association for Computational Linguistics (ACL). ISSN 9798891760998. p. 5712–5724. Full text in Research Archive Show summary

  We use contextualized word definitions generated by large language models as semantic representations in the task of diachronic lexical semantic change detection (LSCD). In short, generated definitions are used as ‘senses’, and the change score of a target word is retrieved by comparing their distributions in two time periods under comparison. On the material of five datasets and three languages, we show that generated definitions are indeed specific and general enough to convey a signal sufficient to rank sets of words by the degree of their semantic change over time. Our approach is on par with or outperforms prior non-supervised sense-based LSCD methods. At the same time, it preserves interpretability and allows to inspect the reasons behind a specific shift in terms of discrete definitions-as-senses. This is another step in the direction of explainable semantic change modeling.

• Attieh, Joseph; Hopton, Zachary; Scherrer, Yves & Samardžić, Tanja (2024). System Description of the NordicsAlps Submission to the AmericasNLP 2024 Machine Translation Shared Task. In Mager, Manuel; Ebrahimi, Abteen; Rijhwani, Shruti; Oncevay, Arturo; Chiruzzo, Luis; Pugh, Robert & Wense, Katharina von der (Ed.), Proceedings of the 4th Workshop on Natural Language Processing for Indigenous Languages of the Americas (AmericasNLP 2024). Association for Computational Linguistics (ACL). ISSN 9798891761087. p. 150–158. doi: 10.18653/v1/2024.americasnlp-1.18. Full text in Research Archive
• Kuparinen, Olli & Scherrer, Yves (2024). Corpus-based dialectometry with topic models. Journal of Linguistic Geography. 12(1), p. 1–12. doi: 10.1017/jlg.2024.6. Full text in Research Archive
• Mæhlum, Petter & Scherrer, Yves (2024). NoMusic - The Norwegian Multi-Dialectal Slot and Intent Detection Corpus. In Scherrer, Yves; Jauhiainen, Tommi; Ljubešić, Nikola; Zampieri, Marcos; Nakov, Preslav & Tiedemann, Jörg (Ed.), Proceedings of the Eleventh Workshop on NLP for Similar Languages, Varieties, and Dialects (VarDial 2024). Association for Computational Linguistics (ACL). ISSN 9798891761049. doi: 10.18653/v1/2024.vardial-1.9. Full text in Research Archive
• Gibert, Ona de; Aulamo, Mikko; Scherrer, Yves & Tiedemann, Jörg (2024). Hybrid Distillation from RBMT and NMT: Helsinki-NLP’s Submission to the Shared Task on Translation into Low-Resource Languages of Spain. In Haddow, Barry; Kocmi, Tom; Koehn, Philipp & Monz, Christof (Ed.), Proceedings of the Ninth Conference on Machine Translation. Association for Computational Linguistics (ACL). ISSN 9798891761797. p. 908–917. doi: 10.18653/v1/2024.wmt-1.88. Full text in Research Archive
• Chifu, Adrian-Gabriel; Glavaš, Goran; Ionescu, Radu Tudor; Ljubešic, Nikola; Miletić, Aleksandra & Miletić, Filip [Show all 8 contributors for this article] (2024). VarDial Evaluation Campaign 2024: Commonsense Reasoning in Dialects and Multi-Label Similar Language Identification. In Scherrer, Yves; Jauhiainen, Tommi; Ljubešić, Nikola; Zampieri, Marcos; Nakov, Preslav & Tiedemann, Jörg (Ed.), Proceedings of the Eleventh Workshop on NLP for Similar Languages, Varieties, and Dialects (VarDial 2024). Association for Computational Linguistics (ACL). ISSN 9798891761049. doi: 10.18653/v1/2024.vardial-1.1. 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 (ACL). ISSN 9798891760615. p. 13814–13828. doi: 10.18653/v1/2023.findings-emnlp.923. 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 (ACL). ISSN 9798891760431. p. 112–118. doi: 10.18653/v1/2023.lchange-1.12. 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; Farha, Ibrahim Abu; Habash, Nizar; Khalifa, Salam; Keleg, Amr; Haddad, Hatem; Zitouni, Imed; Mrini, Khalil & Almatham, Rawan (Ed.), Proceedings of ArabicNLP 2023. Association for Computational Linguistics (ACL). ISSN 9781959429272. p. 670–677. doi: 10.18653/v1/2023.arabicnlp-1.73. 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.

• 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 Show summary

  Abstract This paper presents the OPUS ecosystem with a focus on the development of open machine translation models and tools, and their integration into end-user applications, development platforms and professional workflows. We discuss our ongoing mission of increasing language coverage and translation quality, and also describe work on the development of modular translation models and speed-optimized compact solutions for real-time translation on regular desktops and small devices.

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• Scherrer, Yves; Aepli, Noëmi; Blaschke, Verena; Jauhiainen, Tommi; Ljubešić, Nikola & Nakov, Preslav [Show all 8 contributors for this article] (2026). Proceedings of the 13th Workshop on NLP for Similar Languages, Varieties and Dialects. Association for Computational Linguistics (ACL). ISBN 9798891763722. 404 p. doi: 10.18653/v1/2026.vardial-1. Full text in Research Archive
• Scherrer, Yves; Jauhiainen, Tommi; Ljubešić, Nikola; Nakov, Preslav; Tiedemann, Jörg & Zampieri, Marcos (2025). Proceedings of the 12th Workshop on NLP for Similar Languages, Varieties and Dialects. Association for Computational Linguistics (ACL). ISBN 9798891762084. 223 p. Full text in Research Archive
• Scherrer, Yves; Jauhiainen, Tommi; Ljubešić, Nikola; Zampieri, Marcos; Nakov, Preslav & Tiedemann, Jörg (2024). Proceedings of the Eleventh Workshop on NLP for Similar Languages, Varieties, and Dialects (VarDial 2024). Association for Computational Linguistics (ACL). ISBN 9798891761049. 252 p. Full text in Research Archive

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• Mæhlum, Petter & Scherrer, Yves (2024). NoMusic – Et parallellkorpus for skriftlig dialekt. Full text in Research Archive
• Scherrer, Yves (2024). NoMusic: Norwegian Multi-Parallel Slot and Intent Detection Corpus. Full text in Research Archive

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