Although machine translation is often considered solved for high-resource languages, it remains challenging for texts involving poetic language, philosophical concepts, and layered metaphors, characteristics that are central to Sanskrit literature. Additionally, Sanskrit’s rich morphology, sandhi, and compounding, combined with its multi-millennial and multi-domain textual tradition, highlight the need for large, diverse parallel resources, which are currently scarce. We introduce Mitrasaṃgraha, a large-scale Sanskrit-English machine translation dataset containing 391,548 aligned sentence pairs, over 4 times larger than the previously available Itihāsa corpus (Aralikatte et al., 2021). The dataset spans more than 3 millennia of Sanskrit literature across multiple domains and includes temporally annotated data for studying domain and period effects on translation performance. We release manually corrected development (5,587) and test (5,552) sets. Benchmarks show that fine-tuning models such as NLLB and Gemma substantially improves translation quality, and retrieval-augmented prompting further enhances performance. In a controlled cross-dataset comparison, training on Mitrasaṃgraha outperforms both Itihāsa and web-mined bitext for two model families. Despite these gains, our results reveal persistent challenges in translating complex compounds, philosophical terminology, and metaphorical language in Sanskrit, highlighting the need for further research in this area.

This paper introduces the Vedic Compound Dataset (VCD), the first resource providing annotated compounds from Vedic Sanskrit, a South Asian Indo-European language used from ca. 1500 to 500 BCE. The VCD aims at facilitating the study of language change in early Indo-Iranian and offers comparative material for quantitative cross-linguistic research on compounds. The process of annotating Vedic compounds is complex as they contain five of the six basic types of compounds defined by Scalise & Bisetto (2005), which are, however, not consistently marked in morphosyntax, making their automatic classification a significant challenge. The paper details the process of collecting and preprocessing the relevant data, with a particular focus on the question of how to distinguish exocentric from endocentric usage. It further discusses experiments with a simple ML classifier that uses compound internal syntactic relations, outlines the composition of the dataset, and sketches directions for future research.

The rhetoric strategy of hedging serves to attenuate speech acts and their semantic content, as in English ‘kind of’ or ‘somehow’. While hedging has recently met with increasing interest in linguistic research, most studies deal with modern languages, preferably English, and take a synchronic approach. This paper complements this research by tracing the diachronic syntactic flexibilization of the Vedic Sanskrit particle iva from a marker of comparison (‘like’) to a full-fledged adaptor. We discuss the outcomes of a diachronic Bayesian framework applied to iva constructions in a Universal Dependencies treebank, and supplement these results with a qualitative discussion of relevant text passages.

Corpus-based studies of diachronic syntactic changes are typically guided by the results of previous qualitative research. When such results are missing or, as is the case for Vedic Sanskrit, are restricted to small parts of a transmitted corpus, an exploratory framework that detects such changes in a data-driven fashion can substantially support the research process. In this paper, we introduce a customized version of the infinite relational model that groups syntactic constituents based on their structural similarities and their diachronic distributions. We propose a simple way to control for register and intellectual affiliation, and discuss our findings for four syntactic structures in Vedic texts.