CSSL: Contrastive Self-Supervised Learning for Dependency Parsing on Relatively Free Word Ordered and Morphologically Rich Low Resource Languages

Neural dependency parsing has achieved remarkable performance for Low-Resource Morphologically-Rich languages. It has also been well-studied that Morphologically-Rich languages exhibit relatively free-word-order. This prompts a fundamental investigation: Is there a way to enhance dependency parsing performance, making the model robust to word order variations utilizing the relatively freeword-order nature of Morphologically-Rich languages? In this work, we examine the robustness of graph-based parsing architectures on 7 relatively free-word-order languages. We focus on scrutinizing essential modifications such as data augmentation and the removal of position encoding required to adapt these architectures accordingly. To this end, we propose a contrastive self-supervised learning method to make the model robust to word order variations. Furthermore, our proposed modification demonstrates a substantial average gain of 3.03/2.95 points in 7 relatively free-word-order languages, as measured by the UAS/LAS Score metric when compared to the best performing baseline. 1