We introduce grounded glossary generation, a structured task requiring models to recover semantically meaningful Sanskrit phrases and produce translation-grounded meanings from a sloka-translation pair, formalizing the traditional patha commentary practice as an evaluable NLP objective. We construct a benchmark of 31,316 sloka-translation-glossary triples from the Valmiki Ramayana and Srimad Bhagavatam, paired with two metrics: Jaccard for phrase recovery and Meaning Faithfulness for semantic consistency. Across zero-shot, few-shot, and instruction fine-tuned variants of Gemma-3n-E4B, Gemma-3-12B, Phi-4, and Qwen3.5-9B, instruction fine-tuning substantially outperforms prompting, while explicit segmentation yields gains. Error analysis identifies over-segmentation of sandhi and samasa compounds as the dominant failure mode, pointing to morphological modeling as the key bottleneck for faithful Sanskrit lexical decomposition.
TF-IDF provides a strong no-training lexical retrieval baseline, alongside representation analyses and generation controls for the learned models, and one narrow result remains: the decoder-only model prefers authentic word order in 107 of 112 minimal-pair comparisons, but does not reproduce held-out commentary content...
The proposed Semantic-Syntax Prealignment (SSPA), an innovative corpus generation framework, exhibits remarkable cross-domain adaptability and stylistic consistency, offering a robust, versatile solution for low-resource Tibetan professional domain MT.
This work argues that one meaningful and currently unmeasured axis is referential reach, the distance a segment must look back into its document to resolve the entities and pronouns it contains, and formalizes this as discourse dependency (DDP), a metric-free, source-side measure computed from named entity re-mentions...
Ahrii Kim, Chanjun Park, Seong-heum Kim· 0 citations
An end-to-end sequence-to-sequence formulation and fine-tune mT5-small and mBART-50 on a synthetic corpus of up to 657,720 noisy-clean Tamil sentence pairs spanning ten error categories, showing that a Tamil-adapted instruction model does not transfer to specialised sentence-level correction without task-specific super...
A. Karthikeyan, S. JayaNirmala, Sangeetha Sivanesan et al.· 0 citations
The exponential growth of scientific literature has intensified the demand for automated summarization systems capable of producing abstracts that are both linguistically fluent and factually reliable. Existing approaches face a fundamental trade-off: encoder-decoder models such as BART and T5 maintain strong factual g...
Geoffrey Antonio Arifin, Andrew Widyanata, Henry Lucky et al.· International Conference on...· 0 citations
VIVID is presented, the first systematic benchmark for evaluating culturally grounded figurative language understanding in Vietnamese, and an evaluation framework combining generative and discriminative tasks, proposing an LLM-as-a-Judge approach with aspect-based prompting validated against human judgment.
Tu Tran Do, N. Nguyen, Tung Khanh Tran et al.· International Conference on...· 0 citations
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