AssamLegalTrans: A Parallel Corpus, Benchmark and Analysis for English-Assamese Machine Translation of Legal Judgments

COILD: An Indic-Centric Parallel Corpus and Benchmark for Machine Translation Across Indian Languages

COILD, an Indic-centric parallel corpus comprising over 1.16 million human-translated and human-verified sentence pairs, covers 20 Indian language pairs across the Indo-Aryan, Dravidian, Tibeto-Burman, and Austro-Asiatic language families, and provides a valuable training and evaluation resource for advancing multiling...

Bridging the linguistic divide: recent developments in machine translation for Indian languages

This paper analyses various recent state-of-the-art variants of large language models (LLMs) and neural machine translation (NMT) for Indian languages in comparison to statistical machine translation (SMT) and tackles key questions, such as idiomatic expressions, morphologically complex grammar or the scarceness of par...

Referenceless Evaluation of Machine Translation Models by Ranking Performance in Romanian to English Translate-train Settings

MaitH 1.0: A Parallel Corpus and Baseline for Low-Resource Maithili-Hindi Translation

A corpus containing both manually curated and synthetically generated sentences for low-resource Indian languages, such as Maithili is contributed and it is demonstrated that, even with a smaller corpus size, high-quality, task-specific data significantly enhance translation accuracy for low-resource Indian languages,...

Entailment, CONtradition and Neutral (E-CONAN) Benchmarks: Arabic Textual Entailment and Natural Inference Datasets

Natural Language Inference (NLI) processes pairs of sentences to extract their semantic relations. NLI has been a hot research topic, integrated as a main component in other NLP applications, from morphological spelling correction tasks to higher-level tasks such as machine translation and information extraction. Despi...

RunyaNER: Auxiliary Language Selection for Runyankore NER

Cross-lingual zero-shot transfer and multilingual fine-tuning are promising approaches for NLP tasks such as Named Entity Recognition (NER) in low-resource languages, but in the absence of target language benchmarks, it is unclear which auxiliary language selection strategy leads to the best transfer. We introduce Runy...