BERTie Bott's Every Flavor Labels: A Tasty Introduction to Semantic Role Labeling for Galician

In this paper, we leverage existing resources, such as WordNet and dependency parsing, to build the first Galician dataset for training semantic role labeling systems in an effort to expand available NLP resources. Additionally, we introduce Verbal Indexing, a new preprocessing method, which helps increase the performance when semantically parsing highly complex sentences. We use transfer learning to test both the resource and the Verbal Indexing method. Our results show that the effects of Verbal Indexing were amplified in scenarios where the model was both pre-trained and finetuned on datasets utilizing the method, but improvements are also noticeable when only used during fine-tuning. The best-performing Galician SRL model achieved an f1 score of 0.74, introducing a baseline for future Galician SRL systems. We also tested our method on Spanish where we achieved an f1 score of 0.83, outperforming the baseline set by the 2009 CoNLL Shared Task by 0.025, showing the merits of our Verbal Indexing method for pre-processing.