Transfer learning for dependency parsing of V edic S anskrit (2025)

Abstract: This paper focuses on data-driven dependency parsing for Vedic Sanskrit. We propose and evaluate a transfer learning approach that benefits from syntactic analysis of typologically related languages, including Ancient Greek and Latin, and a descendant language - Classical Sanskrit. Experiments on the Vedic TreeBank demonstrate the effectiveness of cross-lingual transfer, demonstrating improvements from the biaffine baseline as well as outperforming the current state of the art benchmark, the deep contextualised self-training algorithm, across a wide range of experimental setups.

## introduction there is a pressing need for high-quality linguistic analysis in the study of ancient languages; this work is critically hindered by a scarcity of annotated data @xcite . this challenge is particularly acute for vedic sanskrit, a low-resource language whose free word order and rich morphology create complex, non-projective dependency structures that make automatic parsing a formidable task @xcite . this combination of structural complexity and data scarcity establishes vedic sanskrit as a critical test case for the robustness and scalability of modern datadriven parsing methods. vedic sanskrit exemplifies the need for methods that can operate effectively in low-resource settings. two dominant paradigms address this directly: transfer learning and self-learning @xcite . the former involves transferring a model trained on a high-resource language, often syntactically related to the target language, using the model's predictions to create a large corpus of 'silver-standard' data. the latter, exemplified by deep contextualized self-training (dcst) @xcite , utilises a semi-supervised loop where a model is iteratively retrained on its most confident predictions over a large unlabelled corpus. the application of these techniques to vedic sanskrit is compelling; transfer learning could exploit structural similarities with other ancient indo-european languages, while self-training could leverage the unannotated vedic corpus itself to refine a parser's accuracy. in this paper, we make the following primary contributions. first, we establish a new state-ofthe-art for vedic sanskrit dependency parsing by proposing a cross-lingual transfer learning framework that achieves a labelled attachment score (las) of 82.5%. this result outperforms the previous state-of-the-art, the deep contextualized self-training (dcst) method, by 2.3 points. second, we demonstrate the remarkable data efficiency of this framework; in a rigorous few-shot setting using only 80 annotated sentences, our model achieves a las of 17.33%, more than doubling the performance of a randomly initialised baseline. finally, our direct empirical comparison reveals that our transfer learning approach is a more effective and robust strategy than the complex self-training paradigm for this task. ## related work our research evaluates competing strategies for low-resource neural dependency parsing by enhancing the foundational deep biaffine attention parser @xcite , a powerful architecture well-suited to the non-projective structures found in free-word-order languages. specifically, we adapt the modern trend of replacing traditional lstm encoders with the more powerful transformer architecture @xcite , a combination that has been successfully demonstrated @xcite . applying this enhanced parser to vedic sanskrit, we use the vedic treebank @xcite to conduct two primary investigations: we first measure the impact of the trans-former encoder, and then we empirically compare the state-of-the-art dcst paradigm @xcite against our proposed cross-lingual transfer learning framework. the primary challenge for parsing vedic sanskrit is data scarcity. we address this by comparing two paradigms. the first is self-training, where a model learns from its own predictions on unlabelled data. the state-of-the-art for vedic sanskrit is an advanced implementation of this called dcst, pioneered by @xcite and applied to vedic sanskrit by @xcite , which serves as our primary baseline. the second, competing paradigm is cross-lingual transfer learning, where syntactic knowledge is leveraged from related, higher-resource languages like ancient greek and latin @xcite . we explore this through full fine-tuning and a particularly data-efficient few-shot learning approach @xcite , which is crucial for extremely low-resource settings. this targeted transfer complements the broader trend of building large monolingual foundation models like sanskritt5 @xcite . while both self-training and cross-lingual transfer are established techniques, a direct empirical comparison of their efficacy for a morphologically rich and free-word-order language like vedic sanskrit has been absent. this project fills that critical gap. we augment the standard biaffine parser with a more powerful transformer encoder and use it to systematically evaluate its performance within both the complex dcst framework and a simpler, direct transfer learning framework. by testing this rigorously in full-resource and few-shot learning scenarios, our comparative framework isolates the impact of architectural choices and training paradigms, ultimately demonstrating that a simpler transferbased method is more effective than the current state-of-the-art. ## methodology we formulate dependency parsing as the task of finding the maximum-weight spanning tree in a graph of all possible head-dependent relations, following @xcite . our methodology systematically evaluates architectural enhancements and compares learning paradigms to improve upon the state-of-the-art for this graph-based approach on vedic sanskrit. a summary of our experimental framework is shown in figure 1 . ## experiments and analysis we conducted a series of experiments to evaluate our proposed approach. we first establish the architectural advantage of using a transformer encoder and then compare our cross-lingual transfer learning paradigm with the state-of-the-art self-training method (dcst). finally, we demonstrate the data efficiency of our approach in a rigorous few-shot setting. all experiments are performed on the vedic treebank @xcite and evaluated using mean uas and las with 5-fold cross-validation. first, we evaluated the impact of our architectural choice, the results can be found in ## discussion our experiments consistently demonstrate that a transformer-based parser augmented with crosslingual transfer learning is a superior approach for vedic sanskrit dependency parsing compared to the previous state-of-the-art. the key insight from our analysis is that pre-training on high-quality, annotated data from typologically related languages provides a more effective and robust inductive bias than the semi-supervised, pseudo-labelling approach of dcst. the model learns a strong representation of syntactic structure that requires only minimal, targeted fine-tuning. a particularly noteworthy finding is the strong performance of ancient greek as a source language, despite its different script not being explicitly handled by our tokeniser. this suggests that the model is capturing deep, abstract structural similarities between the languages, rather than relying on surface-level lexical overlap. this highlights the robustness of transfer learning for morphologically rich, low-resource languages. the success of our few-shot learning experiments further underscores this point. by freezing the encoder, we showed that the core syntactic knowledge can be effectively transferred, while the fine-tuning process specialises the final layers for the target language's label set. this provides a practical and highly data-efficient roadmap for developing parsing tools for other ancient or lowresource languages where annotated data is scarce. ## conclusion we establish a new state-of-the-art dependency parser for vedic sanskrit by demonstrating that a modern transformer-based architecture significantly outperforms a traditional bilstm baseline. our central contribution, however, is showing that a straightforward cross-lingual transfer learning framework is more effective and data-efficient than the existing, more complex self-training paradigm. we find that pre-training on typologically related ancient languages provides a powerful inductive bias that substantially improves parsing accuracy, even in rigorous few-shot settings. this work delivers a new benchmark for vedic sanskrit and also validates a robust methodology for tackling parsing challenges in resource-scarce linguistic contexts.