The paper presents a cross‑lingual abstractive summarization system for Marathi, leveraging the Marathi MahaSum corpus and the smaller PMIndiaSum dataset to fine‑tune the IndicTrans2 neural machine translation model. Evaluation on Marathi‑to‑English summarization using ROUGE, chrF, and chrF++ metrics shows that this approach outperforms prior IndicBART and mBART‑50 baselines. The authors also discuss extending the method to English‑to‑Marathi summarization.
Text summarization is an important task for advancing natural language processing (NLP) research and applications in Indic languages. Despite progress in extractive and abstractive summarization, both...
arXiv:2606. 24825v1 Announce Type: cross Abstract: Part-of-Speech (POS) tagging is a foundational NLP task underpinning machine translation, information extraction, and syntactic parsing.
COILD is an Indic‑centric parallel corpus that contains over 1.16 million human‑translated and verified sentence pairs across 20 Indian language pairs from four language families. The corpus is sourced from original Indian language materials in eight domains, and a 2,000‑sentence domain‑centric benchmark is provided for consistent multilingual evaluation. Experiments with IndicTrans2‑Distilled and NLLB‑200 show consistent improvements in automatic metrics and human judgments, underscoring the value of high‑quality Indic‑centric data.
arXiv:2606. 08272v1 Announce Type: cross Abstract: AgriGov is a curated, trilingual (English-Hindi-Marathi) dataset designed to address the scarcity of domain-grounded multilingual resources for agricultural policies and farmer welfare schemes.
The study introduces a zero‑shot benchmark for eight large language models on English and Bengali abstractive summarization, using both lexical/semantic metrics and reference‑free factuality checks. Results show significant cross‑lingual differences: GPT‑5.6 Luna excels in English but not in Bengali, where LLaMA 3.3 70B tops ROUGE and BLEU, while Gemini 2.5 Flash achieves the best Bengali factuality score. The evaluation also reveals that different metrics rank models differently, underscoring the need for multi‑dimensional assessment in low‑resource languages.
The study replicates a distributional‑semantics extractive summarisation method for Hindi, adapting all language‑specific components to Devanagari. Evaluated on the Hindi portions of XL‑Sum and FIRE ILSUM 2.0 with a Devanagari‑aware ROUGE scorer, the replicated system performs significantly worse than a simple three‑sentence lead baseline. Feature ablation shows that sentence position alone reproduces the lead baseline, while other features only steer extraction toward long, entity‑dense body sentences, and TextRank performs identically. "whyItMatters":"The results indicate that current Hindi summarisation benchmarks cannot reward non‑lead content selection, highlighting the need for purpose‑built evaluation resources."
The paper introduces SHAKTI, a large-scale Hindi benchmark for detecting AI-generated text, covering multiple genres, LLM families, prompting strategies, and adversarial transformations. It evaluates the benchmark with human annotation and examines linguistic, grammatical, and sentiment differences between human and AI texts. Building on these insights, the authors propose TRIDENT, a round‑trip translation‑based detection framework with three variants that outperforms existing baselines and demonstrates robustness across seven languages and adversarial settings.
The paper investigates how to fairly compare language models across languages, noting that current evaluation methods vary widely and lack empirical validation. By training controlled monolingual models on parallel data and testing multilingual LLMs, the authors find that many normalized metrics suffer from biases due to tokenization, encoding, and orthographic differences. Instead, they recommend using sentence‑level negative log‑likelihood over semantically equivalent sequences for more reliable cross‑lingual comparisons.
arXiv:2608.12018v2 Announce Type: replace Abstract: Neural Machine Translation (NMT) and Large Language Models (LLMs) excel at cross-lingual tasks but often fail to capture intra-lingual morphologica...
IndicTriMix presents a new benchmark and models for token‑level language identification in tri‑language code‑mixed text involving Hindi, Gujarati, and Bengali. The authors reformulate the task as sequence labeling and fine‑tune transformer models MuRIL and XLM‑RoBERTa, evaluating them on manually annotated test sets. They also introduce two code‑mixing generation methods using parallel sentences and release the datasets and fine‑tuned models for public use.
arXiv:2610.15705v1 Announce Type: cross Abstract: India's Eighth Schedule recognises 22 languages, yet parallel data is uneven across them, and many language pairs, especially among lower-resource la...
arXiv:2607. 23344v1 Announce Type: cross Abstract: Named Entity Recognition (NER) for low-resource languages such as Marathi remains a challenging task due to limited annotated resources and linguistic complexity.