Subtitle translation poses challenges dis tinct from general-purpose machine trans lation model trained on web corpus. The outputs must fit strict display constraints as the text is predominantly spoken dia logue, local slangs, proper names, cultural nuances. In the "WMT26 Chinese → En glish task" all test videos belong to the historical costume drama genre, where pe riod vocabulary cannot be resolved from a single line in isolation. We address these challenges with a two-part system. We fine-tune Hy-MT2-1.8B (Zheng et al., 2026) on 500,000 Chinese-English subtitle pairs from the TVsub corpus (Wang et al., 2018) using 4-bit QLoRA (Dettmers et al., 2023), and at test time we inject show-level metadata (title, episode name, summary) into a structured system prompt to ground character names and period terms. On an in-domain 200-pair development split, QLoRA fine-tuning raises BLEU from 6.93 to 31.29 (+24.4 points) which demon strates the value of domain adaptation over the base multilingual model. Across four prompt configurations evaluated via LLM-as-judge on 20 sampled test trans lations the metadata-augmented, previous context-free variant (structured_noctx) wins 14 of 20 judged dialogues and is sent for submission. The fine-tuned model weights are released under Apache 2.0 at ShivRamSaud/hy-mt2-1.8b-wmt26.

The Nepali language has a rich and complex morphology. Existing lemmatization research focuses on traditional rule-based or TRIE-based approaches. These methods often fail when encountering out-of-vocabulary or misspelled words. This paper investigates neural lemmatization for the under-resourced Nepali language using multilingual transformer models. We formulate lemmatization as a text-to-text generation problem and evaluate its impacts on downstream tasks by finetuning mBART-large-50, mT5-base, and mT5-small. The models were trained on a combination of publicly available and human-annotated word-lemma pair (8,000 instances) dataset. The performance is evaluated using Character Error Rate (CER), accuracy, character-level Bilingual Evaluation Understudy (BLEU), and morphological coverage. The mT5-base model achieved the highest overall performance. The model achieved 96.1% accuracy and a 1.1% CER using a learning rate of 5 × 10−4. However, it showed slightly weaker performance in handling complex morphological variations. The mBART-large-50 model followed closely with 96.0% accuracy and 0.970 morphological coverage. To assess the efficacy of these models, we applied lemmatization to downstream tasks. In Hindi-Nepali cross-lingual alignment, performance improved significantly from 12.86% to 41.61% using mBART model. In information retrieval, the Mean Average Precision (MAP)@1 using binary index increased from 0.71 to 0.90 using mBART model. These results demonstrate that multilingual transformers effectively learn morphological transformations for low-resource languages through text-to-text generation.

This paper presents a comparative analysis of FastText and BERT-based approaches for Natural Language Understanding (NLU) tasks in Devanagari script languages. We evaluate these models on three critical tasks: language identification, hate speech detection, and target identification across five languages: Nepali, Marathi, Sanskrit, Bhojpuri, and Hindi. Our experiments, although with raw tweet dataset but extracting only devanagari script, demonstrate that while both models achieve exceptional performance in language identification (F1 scores > 0.99), they show varying effectiveness in hate speech detection and target identification tasks. FastText with augmented data outperforms BERT in hate speech detection (F1 score: 0.8552 vs 0.5763), while BERT shows superior performance in target identification (F1 score: 0.5785 vs 0.4898). These findings contribute to the growing body of research on NLU for low-resource languages and provide insights into model selection for specific tasks in Devanagari script processing.