The first machine translation study for the English and Pnar language pair is presented, establishing the first quantitative benchmark for this language pair and discussing future directions toward neural and multilingual machine translation for Pnar.
Abstract
Pnar, an Austroasiatic language spoken by approximately 0.4 million people in the Jaintia Hills of Meghalaya, lacks the digital corpora and natural language processing (NLP) resources. This paper presents the first machine translation study for the English and Pnar language pair. Using articles collected from the Wyrta newspaper, we built a parallel corpus comprising of 10,234 sentences and trained phrase-based statistical machine translation (SMT) systems the models using 9,563 parallel corpora under three configurations for each direction using Moses, GIZA++ , KenLM, varying lexicalized reordering and minimum error rate training (MERT) tuning. The models are evaluated on a held out test set of 371 sentences, the best performing system achieves a BLEU score of 14.97 (chrF2: 33.42, TER: 77.60) for Pnar to English and 11.16 (chrF2: 31.38, TER: 93.51) for English to Pnar, establishing the first quantitative benchmark for this language pair. Lexicalized reordering improves translation quality by 3.73 BLEU points for Pnar to English, reflecting the structural shift from the source language's SOV word order to the target language's SVO order, whereas MERT tuning degrades BLEU performance under low resource conditions. Finally, we analyze the remaining translation errors, including morphological out of vocabulary (OOV) words, long-distance reordering and Khasi code mixing and discuss future directions toward neural and multilingual machine translation for Pnar.
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 parallel corpora.
Jayanand A. Kamble, S. Jadhav, V. J. Kadam· International Journal of Inf...· 0 citations
Transformer-based neural machine translation (NMT) models have boosted translation accuracy for high-resource languages; however, research has largely bypassed unwritten and low-resource languages, particularly African languages such as Ebira. Ebira is an unwritten, low-resource language spoken by approximately 2.5 million people predominantly in Kogi State, Nigeria. Existing Ebira machine translation (MT) systems suffer from poor fluency, accuracy, and missed nuances, constrained by small datasets and rule-based methods. This study presents the development of a neural machine translation (NMT) system for English-to-Ebira translation using Google’s T5-base transformer model. A bilingual parallel corpus of 32,322 English-Ebira sentence pairs was compiled and used to fine-tune the model. The system achieved a corpus-level BLEU score of 40.95%, with 87% of evaluated sentences scoring 0.5 BLEU or higher, surpassing the prior rule-based system’s threshold result of 81.50%, corresponding to 6.75% relative improvement. Human evaluation by ten native Ebira speakers yielded a mean rating of 8.33/10 for fluency, accuracy, and cultural relevance. This research demonstrated that the application of transfer learning on transformer NMT model significantly improves the quality of (MT) systems; and also provides a foundational step for the development of computational resources for Ebira and supports the broader goal of linguistic inclusivity in artificial intelligence.
Musari Abdulmusawir Tijani, Amina Hassan Abubakar, A. Kana et al.· FUDMA Journal of Sciences· 0 citations
The findings show that manual transliteration consistently yields the best translation performance, while noisy automatic romanization reduces these gains, and that LLM-based translation can be competitive with, and in some settings outperform, fine-tuned NMT systems, although this advantage comes with lower interpretability.
A. Mansurova, Meruert Bekmukhamedova, Bekarys Baibolat et al.· Electronics· 0 citations
Machine translation (MT) technologies are currently undergoing a paradigm shift, transitioning
from specialized Neural Machine Translation (NMT) frameworks to the broader capabilities of
Large Language Models (LLMs). This paper examines the current standing of the Croatian language
within this technological evolution.
While bilingual NMT models often exhibit high precision, multilingual NMT leverage transfer
learning to enhance performance for low–resource language pairs, but with lower performance for
high–resource ones. Conversely, LLMs—whether general–purpose or fine–tuned for translation—
offer superior multilingual proficiency and context awareness. Unlike NMT, LLMs can process extended discourse, such as full paragraphs or documents, leading to significant improvements in
coreference resolution and gender agreement. Despite the substantial computational requirements
of LLMs, recent optimization techniques allow for smaller, more efficient versions that maintain
high output quality.
This study evaluates the performance of various NMT and LLM architectures specifically for
Croatian from/to English and Spanish using several automatic quality evaluation metrics. The findings demonstrate that open–source models can achieve, and occasionally surpass, the quality of
Google Translate, a widely used commercial NMT system. Furthermore, while our evaluation focuses on this specific language triad, the multilingual nature of the analysed systems suggests that
open–source models provide high–quality translation capabilities for Croatian across dozens, if not
hundreds, of language pairs.
Antoni Oliver, Sergi Álvarez–Vidal· Suvremena Lingvistika· 1 citation
This study highlights the importance of integrating in-depth linguistic analysis, contextual semantic modelling, and cultural awareness into natural language processing-based translation systems by combining traditional linguistic insights with computational methods.
Hilal Abdul-Raziq Sadiq, Zaxid Maxmudovich Islamov, R. Matibaeva et al.· Digital Technologies Researc...· 0 citations
The experimental results revealed the strengths and weaknesses of various approaches to subword segmentation and identified the most effective tokenization strategies under the conditions of the morphological complexity of the Tajik language.
M. Arabov, S. Khaybullina· Automatic Documentation and...· 0 citations
