Veronika Laippala


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Toxicity Detection in Finnish Using Machine Translation
Anni Eskelinen | Laura Silvala | Filip Ginter | Sampo Pyysalo | Veronika Laippala
Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)

Due to the popularity of social media platforms and the sheer amount of user-generated content online, the automatic detection of toxic language has become crucial in the creation of a friendly and safe digital space. Previous work has been mostly focusing on English leaving many lower-resource languages behind. In this paper, we present novel resources for toxicity detection in Finnish by introducing two new datasets, a machine translated toxicity dataset for Finnish based on the widely used English Jigsaw dataset and a smaller test set of Suomi24 discussion forum comments originally written in Finnish and manually annotated following the definitions of the labels that were used to annotate the Jigsaw dataset. We show that machine translating the training data to Finnish provides better toxicity detection results than using the original English training data and zero-shot cross-lingual transfer with XLM-R, even with our newly annotated dataset from Suomi24.

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FinGPT: Large Generative Models for a Small Language
Risto Luukkonen | Ville Komulainen | Jouni Luoma | Anni Eskelinen | Jenna Kanerva | Hanna-Mari Kupari | Filip Ginter | Veronika Laippala | Niklas Muennighoff | Aleksandra Piktus | Thomas Wang | Nouamane Tazi | Teven Scao | Thomas Wolf | Osma Suominen | Samuli Sairanen | Mikko Merioksa | Jyrki Heinonen | Aija Vahtola | Samuel Antao | Sampo Pyysalo
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Large language models (LLMs) excel in many tasks in NLP and beyond, but most open models have very limited coverage of smaller languages and LLM work tends to focus on languages where nearly unlimited data is available for pretraining. In this work, we study the challenges of creating LLMs for Finnish, a language spoken by less than 0.1% of the world population. We compile an extensive dataset of Finnish combining web crawls, news, social media and eBooks. We pursue two approaches to pretrain models: 1) we train seven monolingual models from scratch (186M to 13B parameters) dubbed FinGPT, 2) we continue the pretraining of the multilingual BLOOM model on a mix of its original training data and Finnish, resulting in a 176 billion parameter model we call BLUUMI. For model evaluation, we introduce FIN-bench, a version of BIG-bench with Finnish tasks. We also assess other model qualities such as toxicity and bias. Our models and tools are openly available at


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Explaining Classes through Stable Word Attributions
Samuel Rönnqvist | Aki-Juhani Kyröläinen | Amanda Myntti | Filip Ginter | Veronika Laippala
Findings of the Association for Computational Linguistics: ACL 2022

Input saliency methods have recently become a popular tool for explaining predictions of deep learning models in NLP. Nevertheless, there has been little work investigating methods for aggregating prediction-level explanations to the class level, nor has a framework for evaluating such class explanations been established. We explore explanations based on XLM-R and the Integrated Gradients input attribution method, and propose 1) the Stable Attribution Class Explanation method (SACX) to extract keyword lists of classes in text classification tasks, and 2) a framework for the systematic evaluation of the keyword lists. We find that explanations of individual predictions are prone to noise, but that stable explanations can be effectively identified through repeated training and explanation. We evaluate on web register data and show that the class explanations are linguistically meaningful and distinguishing of the classes.

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Towards better structured and less noisy Web data: Oscar with Register annotations
Veronika Laippala | Anna Salmela | Samuel Rönnqvist | Alham Fikri Aji | Li-Hsin Chang | Asma Dhifallah | Larissa Goulart | Henna Kortelainen | Marc Pàmies | Deise Prina Dutra | Valtteri Skantsi | Lintang Sutawika | Sampo Pyysalo
Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022)

Web-crawled datasets are known to be noisy, as they feature a wide range of language use covering both user-generated and professionally edited content as well as noise originating from the crawling process. This article presents one solution to reduce this noise by using automatic register (genre) identification -whether the texts are, e.g., forum discussions, lyrical or how-to pages. We apply the multilingual register identification model by Rönnqvist et al. (2021) and label the widely used Oscar dataset. Additionally, we evaluate the model against eight new languages, showing that the performance is comparable to previous findings on a restricted set of languages. Finally, we present and apply a machine learning method for further cleaning text files originating from Web crawls from remains of boilerplate and other elements not belonging to the main text of the Web page. The register labeled and cleaned dataset covers 351 million documents in 14 languages and is available at


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Multilingual and Zero-Shot is Closing in on Monolingual Web Register Classification
Samuel Rönnqvist | Valtteri Skantsi | Miika Oinonen | Veronika Laippala
Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)

This article studies register classification of documents from the unrestricted web, such as news articles or opinion blogs, in a multilingual setting, exploring both the benefit of training on multiple languages and the capabilities for zero-shot cross-lingual transfer. While the wide range of linguistic variation found on the web poses challenges for register classification, recent studies have shown that good levels of cross-lingual transfer from the extensive English CORE corpus to other languages can be achieved. In this study, we show that training on multiple languages 1) benefits languages with limited amounts of register-annotated data, 2) on average achieves performance on par with monolingual models, and 3) greatly improves upon previous zero-shot results in Finnish, French and Swedish. The best results are achieved with the multilingual XLM-R model. As data, we use the CORE corpus series featuring register annotated data from the unrestricted web.

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Beyond the English Web: Zero-Shot Cross-Lingual and Lightweight Monolingual Classification of Registers
Liina Repo | Valtteri Skantsi | Samuel Rönnqvist | Saara Hellström | Miika Oinonen | Anna Salmela | Douglas Biber | Jesse Egbert | Sampo Pyysalo | Veronika Laippala
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop

We explore cross-lingual transfer of register classification for web documents. Registers, that is, text varieties such as blogs or news are one of the primary predictors of linguistic variation and thus affect the automatic processing of language. We introduce two new register-annotated corpora, FreCORE and SweCORE, for French and Swedish. We demonstrate that deep pre-trained language models perform strongly in these languages and outperform previous state-of-the-art in English and Finnish. Specifically, we show 1) that zero-shot cross-lingual transfer from the large English CORE corpus can match or surpass previously published monolingual models, and 2) that lightweight monolingual classification requiring very little training data can reach or surpass our zero-shot performance. We further analyse classification results finding that certain registers continue to pose challenges in particular for cross-lingual transfer.


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A Broad-coverage Corpus for Finnish Named Entity Recognition
Jouni Luoma | Miika Oinonen | Maria Pyykönen | Veronika Laippala | Sampo Pyysalo
Proceedings of the Twelfth Language Resources and Evaluation Conference

We present a new manually annotated corpus for broad-coverage named entity recognition for Finnish. Building on the original Universal Dependencies Finnish corpus of 754 documents (200,000 tokens) representing ten different genres of text, we introduce annotation marking person, organization, location, product and event names as well as dates. The new annotation identifies in total over 10,000 mentions. An evaluation of inter-annotator agreement indicates that the quality and consistency of annotation are high, at 94.5% F-score for exact match. A comprehensive evaluation using state-of-the-art machine learning methods demonstrates that the new resource maintains compatibility with a previously released single-domain corpus for Finnish NER and makes it possible to recognize named entity mentions in texts drawn from most domains at precision and recall approaching or exceeding 90%. Remaining challenges such as the identification of names in blog posts and transcribed speech are also identified. The newly introduced Turku NER corpus and related resources introduced in this work are released under open licenses via .

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From Web Crawl to Clean Register-Annotated Corpora
Veronika Laippala | Samuel Rönnqvist | Saara Hellström | Juhani Luotolahti | Liina Repo | Anna Salmela | Valtteri Skantsi | Sampo Pyysalo
Proceedings of the 12th Web as Corpus Workshop

The web presents unprecedented opportunities for large-scale collection of text in many languages. However, two critical steps in the development of web corpora remain challenging: the identification of clean text from source HTML and the assignment of genre or register information to the documents. In this paper, we evaluate a multilingual approach to this end. Our starting points are the Swedish and French Common Crawl datasets gathered for the 2017 CoNLL shared task, particularly the URLs. We 1) fetch HTML pages based on the URLs and run boilerplate removal, 2) train a classifier to further clean out undesired text fragments, and 3) annotate text registers. We compare boilerplate removal against the CoNLL texts, and find an improvement. For the further cleaning of undesired material, the best results are achieved using Multilingual BERT with monolingual fine-tuning. However, our results are promising also in a cross-lingual setting, without fine-tuning on the target language. Finally, the register annotations show that most of the documents belong to a relatively small set of registers, which are relatively similar in the two languages. A number of additional flags in the annotation are, however, necessary to reflect the wide range of linguistic variation associated with the documents.


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Toward Multilingual Identification of Online Registers
Veronika Laippala | Roosa Kyllönen | Jesse Egbert | Douglas Biber | Sampo Pyysalo
Proceedings of the 22nd Nordic Conference on Computational Linguistics

We consider cross- and multilingual text classification approaches to the identification of online registers (genres), i.e. text varieties with specific situational characteristics. Register is the most important predictor of linguistic variation, and register information could improve the potential of online data for many applications. We introduce the first manually annotated non-English corpus of online registers featuring the full range of linguistic variation found online. The data set consists of 2,237 Finnish documents and follows the register taxonomy developed for the Corpus of Online Registers of English (CORE). Using CORE and the newly introduced corpus, we demonstrate the feasibility of cross-lingual register identification using a simple approach based on convolutional neural networks and multilingual word embeddings. We further find that register identification results can be improved through multilingual training even when a substantial number of annotations is available in the target language.


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Creating register sub-corpora for the Finnish Internet Parsebank
Veronika Laippala | Juhani Luotolahti | Aki-Juhani Kyröläinen | Tapio Salakoski | Filip Ginter
Proceedings of the 21st Nordic Conference on Computational Linguistics


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Towards the Classification of the Finnish Internet Parsebank: Detecting Translations and Informality
Veronika Laippala | Jenna Kanerva | Anna Missilä | Sampo Pyysalo | Tapio Salakoski | Filip Ginter
Proceedings of the 20th Nordic Conference of Computational Linguistics (NODALIDA 2015)

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Universal Dependencies for Finnish
Sampo Pyysalo | Jenna Kanerva | Anna Missilä | Veronika Laippala | Filip Ginter
Proceedings of the 20th Nordic Conference of Computational Linguistics (NODALIDA 2015)

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Towards Universal Web Parsebanks
Juhani Luotolahti | Jenna Kanerva | Veronika Laippala | Sampo Pyysalo | Filip Ginter
Proceedings of the Third International Conference on Dependency Linguistics (Depling 2015)


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Towards a Dependency-Based PropBank of General Finnish
Katri Haverinen | Veronika Laippala | Samuel Kohonen | Anna Missilä | Jenna Nyblom | Stina Ojala | Timo Viljanen | Tapio Salakoski | Filip Ginter
Proceedings of the 19th Nordic Conference of Computational Linguistics (NODALIDA 2013)

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Building a Large Automatically Parsed Corpus of Finnish
Filip Ginter | Jenna Nyblom | Veronika Laippala | Samuel Kohonen | Katri Haverinen | Simo Vihjanen | Tapio Salakoski
Proceedings of the 19th Nordic Conference of Computational Linguistics (NODALIDA 2013)


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Dependency-Based PropBanking of Clinical Finnish
Katri Haverinen | Filip Ginter | Timo Viljanen | Veronika Laippala | Tapio Salakoski
Proceedings of the Fourth Linguistic Annotation Workshop


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Parsing Clinical Finnish: Experiments with Rule-Based and Statistical Dependency Parsers
Katri Haverinen | Filip Ginter | Veronika Laippala | Tapio Salakoski
Proceedings of the 17th Nordic Conference of Computational Linguistics (NODALIDA 2009)


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On the unification of syntactic annotations under the Stanford dependency scheme: A case study on BioInfer and GENIA
Sampo Pyysalo | Filip Ginter | Veronika Laippala | Katri Haverinen | Juho Heimonen | Tapio Salakoski
Biological, translational, and clinical language processing