KU Leuven / Brepols-CTLO at EvaLatin 2024: Span Extraction Approaches for Latin Dependency Parsing

AbstractThis 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.