Natural language processing for Greek and Latin, inflectional languages with small corpora, requires special techniques. For morphological tagging, transformer models show promising potential, but the best approach to use these models is unclear. For both languages, this paper examines the impact of using morphological lexica, training different model types (a single model with a combined feature tag, multiple models for separate features, and a multi-task model for all features), and adding linguistic constraints. We find that, although simply fine-tuning transformers to predict a monolithic tag may already yield decent results, each of these adaptations can further improve tagging accuracy.

This report describes the KU Leuven / Brepols-CTLO submission to EvaLatin 2024. We present the results of two runs, both of which try to implement a span extraction approach. The first run implements span-span prediction, rooted in Machine Reading Comprehension, while making use of LaBERTa, a RoBERTa model pretrained on Latin texts. The first run produces meaningful results. The second, more experimental run operates on the token-level with a span-extraction approach based on the Question Answering task. This model finetuned a DeBERTa model, pretrained on Latin texts. The finetuning was set up in the form of a Multitask Model, with classification heads for each token’s part-of-speech tag and dependency relation label, while a question answering head handled the dependency head predictions. Due to the shared loss function, this paper tried to capture the link between part-of-speech tag, dependency relation and dependency heads, that follows the human intuition. The second run did not perform well.

This paper seeks to leverage translations of Ancient Greek texts to enhance the performance of automatic word sense disambiguation (WSD). Satisfactory WSD in Ancient Greek is achievable, provided that the system can rely on annotated data. This study, acknowledging the challenges of manually assigning meanings to every Greek lemma, explores the strategies to derive WSD data from parallel texts using sentence and word alignment. Our results suggest that, assuming the condition of high word frequency is met, this technique permits us to automatically produce a significant volume of annotated data, although there are still significant obstacles when trying to automate this process.

This report describes the KU Leuven / Brepols-CTLO submission to EvaLatin 2022. We present the results of our current small Latin ELECTRA model, which will be expanded to a larger model in the future. For the lemmatization task, we combine a neural token-tagging approach with the in-house rule-based lemma lists from Brepols’ ReFlex software. The results are decent, but suffer from inconsistencies between Brepols’ and EvaLatin’s definitions of a lemma. For POS-tagging, the results come up just short from the first place in this competition, mainly struggling with proper nouns. For morphological tagging, there is much more room for improvement. Here, the constraints added to our Multiclass Multilabel model were often not tight enough, causing missing morphological features. We will further investigate why the combination of the different morphological features, which perform fine on their own, leads to issues.