ymoslem/acl-abstracts huggingface dataset:
Towards a Derivational Semantics Resource for Latvian (2025)

In this paper we describe the implementation of the first structured resource of semantic derivational links for Latvian, basing it on the largest online dictionary T{\={e}}zaurs.lv and linking it to the Latvian WordNet. We separate two kinds of derivational links: semantic derivation links between senses and morphological derivation links between lexemes. The semantic links between senses are defined as a pair of semantic labels assigned to both ends of the link. The process of semantic linking involves revising the sense inventory of both the base word and the derivative, defining semantic labels for lexemes of four basic word classes {--} nouns, verbs, adjectives and adverbs, and adding the appropriate labels to the corresponding senses. We exemplify our findings with a detailed representation of sense relations between a base verb and its nominal derivatives.
Poro 34B and the Blessing of Multilinguality (2025)

The pretraining of state-of-the-art large language models now requires trillions of words of text, which is orders of magnitude more than available for the vast majority of languages. While including text in more than one language is an obvious way to acquire more pretraining data, multilinguality is often seen as a curse, and most model training efforts continue to focus near-exclusively on individual large languages. We believe that multilinguality can be a blessing: when the lack of training data is a constraint for effectively training larger models for a target language, augmenting the dataset with other languages can offer a way to improve over the capabilities of monolingual models for that language. In this study, we introduce Poro 34B, a 34 billion parameter model trained for 1 trillion tokens of Finnish, English, and programming languages, and demonstrate that a multilingual training approach can produce a model that substantially advances over the capabilities of existing models for Finnish and excels in translation, while also achieving competitive performance in its class for English and programming languages. We release the model parameters, scripts, and data under open licenses at https://huggingface.co/LumiOpen/Poro-34B.
Can summarization approximate simplification? A gold standard comparison (2025)

This study explores the overlap between text summarization and simplification outputs. While summarization evaluation methods are streamlined, simplification lacks cohesion, prompting the question: how closely can abstractive summarization resemble gold-standard simplification? We address this by applying two BART-based BRIO summarization methods to the Newsela corpus, comparing outputs with manually annotated simplifications and achieving a top ROUGE-L score of 0.654. This provides insight into where summarization and simplification outputs converge and differ.
A Comparative Study of PEFT Methods for Python Code Generation (2025)

Fine-tuning language models incurs high costs in training, inference and storage. Parameter-efficient fine-tuning (PEFT) methods have emerged as a more cost-effective alternative to full fine-tuning. However, limited work has compared different PEFT approaches for tasks like code generation. In this study, we examine the effect of various PEFT training methods on model performance in the task of Python code generation. We fine-tune four model families, ranging from 124M to 7B parameters, using three PEFT approaches alongside standard full fine-tuning. Our findings reveal that the effectiveness of each PEFT method varies with the model size and the corpus used.
A Collection of Question Answering Datasets for Norwegian (2025)

This paper introduces a new suite of question answering datasets for Norwegian; NorOpenBookQA, NorCommonSenseQA, NorTruthfulQA, and NRK-Quiz-QA. The data covers a wide range of skills and knowledge domains, including world knowledge, commonsense reasoning, truthfulness, and knowledge about Norway. Covering both of the written standards of Norwegian {--} Bokm{\r{a}}l and Nynorsk {--} our datasets comprise over 10k question-answer pairs, created by native speakers. We detail our dataset creation approach and present the results of evaluating 11 language models (LMs) in zero- and few-shot regimes. Most LMs perform better in Bokm{\r{a}}l than Nynorsk, struggle most with commonsense reasoning, and are often untruthful in generating answers to questions. All our datasets and annotation materials are publicly available.
Incorporating Target Fuzzy Matches into Neural Fuzzy Repair (2025)

Neural fuzzy repair (NFR) is a simple implementation of retrieval-augmented translation (RAT), based on data augmentation. In NFR, a translation database is searched for translation examples where the source sentence is similar to the sentence being translated, and the target side of the example is concatenated with the source sentences. We experiment with introducing retrieval that is based on target similarity to NFR during training. The results of our experiments confirm that including target similarity matches during training supplements source similarity matches and leads to better translations at translation time.
Constructions and Strategies in Universal Dependencies (2025)

Is the framework of Universal Dependencies (UD) compatible with findings from linguistic typology? One way to find out is to investigate whether UD can adequately represent constructions of the world`s languages, as described in William Croft`s recent book Morphosyntax. This paper discusses how such an investigation could be carried out and why it would be useful.
Finnish SQuAD: A Simple Approach to Machine Translation of Span Annotations (2025)

We apply a simple method to machine translate datasets with span-level annotation using the DeepL MT service and its ability to translate formatted documents. Using this method, we produce a Finnish version of the SQuAD2.0 question answering dataset and train QA retriever models on this new dataset. We evaluate the quality of the dataset and more generally the MT method through direct evaluation, indirect comparison to other similar datasets, a backtranslation experiment, as well as through the performance of downstream trained QA models. In all these evaluations, we find that the method of transfer is not only simple to use but produces consistently better translated data. Given its good performance on the SQuAD dataset, it is likely the method can be used to translate other similar span-annotated datasets for other tasks and languages as well. All code and data is available under an open license: data at HuggingFace TurkuNLP/squad{\_}v2{\_}fi, code on GitHub TurkuNLP/squad2-fi, and model at HuggingFace TurkuNLP/bert-base-finnish-cased-squad2.
How to Tune a Multilingual Encoder Model for Germanic Languages: A Study of PEFT, Full Fine-Tuning, and Language Adapters (2025)

This paper investigates the optimal use of the multilingual encoder model mDeBERTa for tasks in three Germanic languages {--} German, Swedish, and Icelandic {--} representing varying levels of presence and likely data quality in mDeBERTas pre-training data. We compare full fine-tuning with the parameter-efficient fine-tuning (PEFT) methods LoRA and Pfeiffer bottleneck adapters, finding that PEFT is more effective for the higher-resource language, German. However, results for Swedish and Icelandic are less consistent. We also observe differences between tasks: While PEFT tends to work better for question answering, full fine-tuning is preferable for named entity recognition. Inspired by previous research on modular approaches that combine task and language adapters, we evaluate the impact of adding PEFT modules trained on unstructured text, finding that this approach is not beneficial.
Match \textquoteleftem: Multi-Tiered Alignment for Error Analysis in ASR (2025)

We introduce {\textquotedblleft}Match {\textquoteleft}em{\textquotedblright}: a new framework for aligning output from automatic speech recognition (ASR) with reference transcriptions. This allows a more detailed analysis of errors produced by end-to-end ASR systems compared to word error rate (WER). Match {\textquoteleft}em performs the alignment on both the word and character level; each relying on information from the other to provide the most meaningful global alignment. At the character level, we define a speech production motivated character similarity metric. At the word level, we rely on character similarities to define word similarity and, additionally, we reconcile compounding (insertion or deletion of spaces). We evaluated Match {\textquoteleft}em on transcripts of three European languages produced by wav2vec2 and Whisper. We show that Match {\textquoteleft}em results in more similar word substitution pairs and that compound reconciling can capture a broad range of spacing errors. We believe Match {\textquoteleft}em to be a valuable tool for ASR error analysis across many languages.
Adding Metadata to Existing Parliamentary Speech Corpus (2025)

Parliamentary proceedings are convenient data sources for creating corpora for speech technology. Given its public nature, there is an abundance of extra information about the speakers that can be legally and ethically harvested to enrich this kind of corpora. This paper describes the methods we have used to add speaker metadata to the Stortinget Speech Corpus (SSC) containing over 5,000 hours of Norwegian speech with non-verbatim transcripts but without speaker metadata. The additional metadata for each speech segment includes speaker ID, gender, date of birth, municipality of birth, and counties represented. We also infer speaker dialect from their municipality of birth using a manually designed mapping between municipalities and Norwegian dialects. We provide observations on the SSC data and give suggestions for how it may be used for tasks other than speech recognition. Finally, we demonstrate the utility of this new metadata through a dialect identification task. The described methods can be adapted to add metadata information to parliamentary corpora in other languages.
Paragraph-Level Machine Translation for Low-Resource Finno-Ugric Languages (2025)

We develop paragraph-level machine translation for four low-resource Finno-Ugric languages: Proper Karelian, Livvi, Ludian, and Veps. The approach is based on sentence-level pre-trained translation models, which are fine-tuned with paragraph-parallel data. This allows the resulting model to develop a native ability to handle discource-level phenomena correctly, in particular translating from grammatically gender-neutral input in Finno-Ugric languages. We collect monolingual and parallel paragraph-level corpora for these languages. Our experiments show that paragraph-level translation models can translate sentences no worse than sentence-level systems, while handling discourse-level phenomena better. For evaluation, we manually translate part of FLORES-200 into these four languages. All our results, data, and models are released openly.
Evaluating LLM-Generated Explanations of Metaphors -- A Culture-Sensitive Study of Danish (2025)

In this study, we examine how well Danish culture-specific metaphors are explained by two of the best performing language models for Danish, namely ChatGPT and Llama. For comparison, the explana- tions are measured against how well cross- lingual (or `universal') metaphors are ex- plained by the models; referring here to metaphors that exist in Danish as well as across cultures and languages and in par- ticular in English. To perform our study, we compile a pilot dataset of 150 Danish metaphors and idioms divided tentatively by culture specificity. We prompt the two models and perform a careful qualitative evaluation of the explanations against a four-graded scale. Our studies show that both models are heavily biased towards English since they have much more suc- cess in explaining the metaphors that also exist in English than the culture-specific ones, relying presumably on erroneous transfer from English when dealing with the latter. In particular, the sentiment of the culture-specific metaphors seems to be often `lost in translation'. We further claim that this strong colouring towards English poses a serious problem in the era of LLMs with regards to developing and maintaining cultural and linguistic diver- sity in other languages.
Tokenization on Trial: The Case of Kalaallisut--Danish Legal Machine Translation (2025)

The strengths of subword tokenization have been widely demonstrated when applied to higher-resourced, morphologically simple languages. However, it is not self-evident that these results transfer to lower-resourced, morphologically complex languages. In this work, we investigate the influence of different subword segmentation techniques on machine translation between Danish and Kalaallisut, the official language of Greenland. We present the first semi-manually aligned parallel corpus for this language pair, and use it to compare subwords from unsupervised tokenizers and morphological segmenters. We find that Unigram-based segmentation both preserves morphological boundaries and handles out-of-vocabulary words adequately, but that this does not directly correspond to superior translation quality. We hope that our findings lay further groundwork for future efforts in neural machine translation for Kalaallisut.
The Roles of English in Evaluating Multilingual Language Models (2025)

Multilingual natural language processing is getting increased attention, with numerous models, benchmarks, and methods being released for many languages. English is often used in multilingual evaluation to prompt language models (LMs), mainly to overcome the lack of instruction tuning data in other languages. In this position paper, we lay out two roles of English in multilingual LM evaluations: as an interface and as a natural language. We argue that these roles have different goals: task performance versus language understanding. This discrepancy is highlighted with examples from datasets and evaluation setups. Numerous works explicitly use English as an interface to boost task performance. We recommend to move away from these imprecise methods and instead focus on language understanding.
Revisiting Projection-based Data Transfer for Cross-Lingual Named Entity Recognition in Low-Resource Languages (2025)

Cross-lingual Named Entity Recognition (NER) leverages knowledge transfer between languages to identify and classify named entities, making it particularly useful for low-resource languages. We show that the data-based cross-lingual transfer method is an effective technique for cross-lingual NER and can outperform multi-lingual language models for low-resource languages. This paper introduces two key enhancements to the annotation projection step in cross-lingual NER for low-resource languages. First, we explore refining word alignments using back-translation to improve accuracy. Second, we present a novel formalized projection approach of matching source entities with extracted target candidates. Through extensive experiments on two datasets spanning 57 languages, we demonstrated that our approach surpasses existing projection-based methods in low-resource settings. These findings highlight the robustness of projection-based data transfer as an alternative to model-based methods for cross-lingual named entity recognition in low-resource languages.
Empathy vs Neutrality: Designing and Evaluating a Natural Chatbot for the Healthcare Domain (2025)

As lifestyle-related diseases rise due to unhealthy habits such as smoking, poor diet, lack of exercise, and alcohol consumption, the role of Conversational AI in healthcare is increasingly significant. This study provides an empirical study on the design and evaluation of a natural and intuitive healthcare chatbot, specifically focusing on the impact of empathetic responses on user experience regarding lifestyle changes. Findings reveal a strong preference for the empathetic chatbot, with results showing statistical significance (p {\ensuremath{<}}0.001), highlighting the importance of empathy in enhancing user interaction with healthcare chatbots.
Assessed and Annotated Vowel Lengths in Spoken Icelandic Sentences for L1 and L2 Speakers: A Resource for Pronunciation Training (2025)

We introduce a dataset of time-aligned phonetic transcriptions focusing on vowel length (quantity) in Icelandic. Ultimately, this aims to support computer assisted pronunciation training (CAPT) software, to automatically assess length and possible errors in Icelandic learners' pronunciations. The dataset contains a range of long and short vowel targets, including the first acoustic description of quantity in non-native Icelandic. Evaluations assess how manual annotations and automatic forced alignment characterise quantity contrasts. Initial analyses also imply partial acquisition of phonologically conditioned quantity alternations by non-native speakers.
The BRAGE Benchmark: Evaluating Zero-shot Learning Capabilities of Large Language Models for Norwegian Customer Service Dialogues (2025)

This study explores the capabilities of open-weight Large Language Models in a zero-shot learning setting, testing their ability to classify the content of customer service dialogues in Norwegian from a single instruction, named the BRAGE benchmark. By comparing results against widely used downstream tasks such as question-answering and named entity recognition, we find that (1) specific instruction models greatly exceed base models on the benchmark, (2) both English and multilingual instruction models outperform the tested Norwegian models of similar sizes, and (3) the difference between base and instruction models is less pronounced than in other generative tasks, suggesting that BRAGE is a challenging benchmark, requiring precise and generalizable instruction-tuning.
Mixed Feelings: Cross-Domain Sentiment Classification of Patient Feedback (2025)

Sentiment analysis of patient feedback from the public health domain can aid decision makers in evaluating the provided services. The current paper focuses on free-text comments in patient surveys about general practitioners and psychiatric healthcare, annotated with four sentence-level polarity classes - positive, negative, mixed and neutral - while also attempting to alleviate data scarcity by leveraging general-domain sources in the form of reviews. For several different architectures, we compare in-domain and out-of-domain effects, as well as the effects of training joint multi-domain models.
The Impact of Copyrighted Material on Large Language Models: A Norwegian Perspective (2025)

The use of copyrighted materials in training language models raises critical legal and ethical questions. This paper presents a framework for and the results of empirically assessing the impact of publisher-controlled copyrighted corpora on the performance of generative large language models (LLMs) for Norwegian. When evaluated on a diverse set of tasks, we found that adding both books and newspapers to the data mixture of LLMs tend to improve their performance, while the addition of fiction works seems to be detrimental. Our experiments could inform the creation of a compensation scheme for authors whose works contribute to AI development.
Encoder vs Decoder: Comparative Analysis of Encoder and Decoder Language Models on Multilingual NLU Tasks (2025)

This paper explores the performance of encoder and decoder language models on multilingual Natural Language Understanding (NLU) tasks, with a broad focus on Germanic languages. Building upon the ScandEval benchmark, initially restricted to evaluating encoder models, we extend the evaluation framework to include decoder models. We introduce a method for evaluating decoder models on NLU tasks and apply it to the languages Danish, Swedish, Norwegian, Icelandic, Faroese, German, Dutch, and English. Through a series of experiments and analyses, we also address research questions regarding the comparative performance of encoder and decoder models, the impact of NLU task types, and the variation across language resources. Our findings reveal that encoder models can achieve significantly better NLU performance than decoder models despite having orders of magnitude fewer parameters. Additionally, we investigate the correlation between decoders and task performance via a UMAP analysis, shedding light on the unique capabilities of decoder and encoder models. This study contributes to a deeper understanding of language model paradigms in NLU tasks and provides valuable insights for model selection and evaluation in multilingual settings.
Small Languages, Big Models: A Study of Continual Training on Languages of Norway (2025)

Training large language models requires vast amounts of data, posing a challenge for less widely spoken languages like Norwegian and even more so for truly low-resource languages like Northern S{\'a}mi. To address this issue, we present a novel three-stage continual training approach that substantially improves the downstream performance together with the inference efficiency for the target languages. Based on our findings, we train, evaluate, and openly release a new generative language model for Norwegian Bokm{\r{a}}l, Nynorsk, and Northern S{\'a}mi with 11.4 billion parameters: NorMistral-11B.
Rethinking Low-Resource MT: The Surprising Effectiveness of Fine-Tuned Multilingual Models in the LLM Age (2025)

This study challenges the current paradigm shift in machine translation, where large language models (LLMs) are gaining prominence over traditional neural machine translation models, with a focus on English-to-Faroese translation. We compare the performance of various models, including fine-tuned multilingual models, LLMs (GPT-SW3, Llama 3.1), and closed-source models (Claude 3.5, GPT-4). Our findings show that a fine-tuned NLLB model outperforms most LLMs, including some larger models, in both automatic and human evaluations. We also demonstrate the effectiveness of using LLM-generated synthetic data for fine-tuning. While closed-source models like Claude 3.5 perform best overall, the competitive performance of smaller, fine-tuned models suggests a more nuanced approach to low-resource machine translation. Our results highlight the potential of specialized multilingual models and the importance of language-specific knowledge. We discuss implications for resource allocation in low-resource settings and suggest future directions for improving low-resource machine translation, including targeted data creation and more comprehensive evaluation methodologies.
Prompt Engineering Enhances Faroese MT, but Only Humans Can Tell (2025)

This study evaluates GPT-4`s English-to-Faroese translation capabilities, comparing it with multilingual models on FLORES-200 and Sprotin datasets. We propose a prompt optimization strategy using Semantic Textual Similarity (STS) to improve translation quality. Human evaluation confirms the effectiveness of STS-based few-shot example selection, though automated metrics fail to capture these improvements. Our findings advance LLM applications for low-resource language translation while highlighting the need for better evaluation methods in this context.
Interactive maps for corpus-based dialectology (2025)

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/ .