Towards accurate dependency parsing for Galician with limited resources

Resumen

Automatic syntactic parsing is a fundamental aspect within NLP. However, effective parsing tools necessitate extensive and high-quality annotated treebanks for satisfactory performance. Consequently, the parsing quality for low-resource languages such as Galician remains inadequate. In this context, the present study explores several approaches to improve the automatic syntactic analysis of Galician using the UD framework. Through experimental endeavors, we analyze the quality of the model incrementing the size of the initial training corpus by adding data from Galician PUD treebank. Additionally, we explore the benefits of incorporating contextualized vector representations by comparing the use of various BERT models. Lastly, we assess the impact of integrating cross-lingual training data from similar varieties, analyzing the models’ performance across used treebanks. Our findings underscore (1) the positive correlation between augmented training data and enhanced model performance across used treebanks; (2) superior performance of monolingual BERT models compared to their multilingual analogues; (3) improvement of overall model performance across utilized treebanks by incorporation of cross-lingual data.