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This paper introduces grounded glossary generation for Classical Sanskrit, a task involving recovering Sanskrit phrases and producing translation-grounded meanings from sloka-translation pairs. It constructs a benchmark from Hindu texts and evaluates various AI models, finding that instruction fine-tuning improves performance, with morphological modeling identified as a key challenge.
A two-dialect finite-state morphological analyzer for the Dungan language is presented, with a multi-genre evaluation measuring inflection, ambiguity, and lexical coverage.
Presents an open, offline word-level digital reader of the Prasthānatrayī with Śaṅkara's Bhāṣya, featuring clickable word analysis, concordance, and a hybrid pipeline using rule-based and LLM-assisted methods.
This paper presents QuechuaTok, a benchmark for evaluating tokenization strategies for Southern Quechua, and introduces Morphological Boundary Accuracy (MorphAcc) as a necessary metric. It shows that BPE achieves low fertility but poor morphological accuracy, while a morphology-aware PRPE tokenizer achieves 83% MorphAcc, demonstrating that fertility rate alone is insufficient for agglutinative languages.
This paper presents a deep learning-based chatbot system for answering frequently asked questions in the Amharic language at universities, achieving 91.55% accuracy using neural networks with TensorFlow and Keras. The system addresses Amharic-specific linguistic challenges including morphological variation and lexical gaps, and was deployed on Facebook Messenger via Heroku.