Evaluating the feasibility of translation-based fine-tuning across six NLP tasks demonstrates that translation-based fine-tuning offers a scalable, resource-efficient, and empirically validated path for extending NLP to low-resource languages while advancing linguistic inclusivity and sustainability in artificial intelligence.
Abstract
BERT models have revolutionised Natural Language Processing (NLP) through their ability to process unstructured text across diverse domains. However, developing high-quality BERT models for non-English languages remains challenging due to limited annotated data and high computational demands. Translating non-English data into English and fine-tuning existing English BERT models offers a resource-efficient alternative, yet few studies have structurally compared translation-based fine-tuning with native-language BERT performance across tasks and languages. This study provides such a comparison, evaluating the feasibility of translation-based fine-tuning across six NLP tasks: Sentiment Analysis, Hate Speech Detection, Question Answering, Named Entity Recognition, Part-of-Speech Tagging, and Natural Language Inference, using datasets translated from Bulgarian, Chinese, Dutch, Italian, and Russian. Across all settings, the translation-based approach was comparable or superior in 53.3 percent of cases. Gains were most frequent in Question Answering, Part-of-Speech Tagging, and Natural Language Inference, while performance declines were common in Named Entity Recognition and Hate Speech Detection. The results show that translation-based fine-tuning is most effective for tasks relying on syntactic or structural patterns and for languages typologically close to English, such as Dutch, but less effective for token-level or culturally nuanced tasks, particularly in Chinese. Overall, this study demonstrates that translation-based fine-tuning offers a scalable, resource-efficient, and empirically validated path for extending NLP to low-resource languages while advancing linguistic inclusivity and sustainability in artificial intelligence.
Social scientists often machine-translate foreign-language texts into English and apply English-based natural language processing tools without systematically evaluating translation quality or annotation efficiency. To address this problem, this study provides evidence-based guidance for researchers applying English-centric natural language processing to domain-specific foreign-language corpora. We provide and empirically validate a structured framework combining multi-system machine translation evaluation and active learning for domain-specific text classification. Using 11,493 parallel Spanish and Arabic sentences aligned to English, we compare four machine translation systems (Google Translate, Deep, DeepL, OPUS) using SacreBLEU, METEOR, COMET, and BERTScore quality scores. Across languages and metrics, machine translation systems yield statistically comparable performance. We then evaluate eight active learning strategies using ConfliBERT for political conflict classification under a 20% annotation budget, corresponding to 1,155 samples from the training split. Binary classification exceeds F1 = 0.90, while QuadClass multi-class performance peaks around F $1~\approx ~0.75$ . The Ensemble Intersection strategy achieves the highest performance in 53% of tasks and often matches or surpasses full-dataset results using only a fraction of labeled data. These results provide a practical workflow for researchers using English-based natural language processing tools on foreign-language, domain-specific corpora.
Naif Alatrush, Luay Abdeljaber, Javier Osorio et al.· IEEE Access· 0 citations
Neural machine translation (NMT) systems are widely used, but their performance remains strongly dependent on the availability of large-scale digital corpora, making translation for low-resource languages a persistent challenge. In parallel, large language models (LLMs) have recently emerged as a promising paradigm for multilingual text generation and translation; however, their behavior in low-resource settings remains largely underexplored. The challenge becomes even more acute for historical languages. Chagatai, a historical Turkic literary language of Central Asia with no native speakers, unstable orthography, and parallel data, represents an extreme case of such a condition. This study investigates whether transliteration significantly affects translation performance and how LLM-based and NMT-based systems compare under an extremely low-resource setting. To address these questions, we evaluated four source-text configurations (original Arabic script, expert manual transliteration, LLM-based transliteration, and rule-based Uroman transliteration) for translation into six target languages: Kazakh, English, Uzbek, Uyghur, Turkish, Russian, and Arabic. The results show that manual transliteration consistently yields the best translation performance, while noisy automatic romanization reduces these gains. For model comparison, GPT-4o was assessed alongside two fine-tuned NMT baselines, NLLB and TranslateGemma. The findings further show that LLM-based translation can be competitive with, and in some settings outperform, fine-tuned NMT systems, although this advantage comes with lower interpretability. Overall, these findings show that, for extremely low-resource historical languages written in non-Latin scripts, source-side representation is a decisive factor and may be as important as the choice of translation model itself.
A. Mansurova, Meruert Bekmukhamedova, Bekarys Baibolat et al.· Electronics· 0 citations
General-purpose large language models (LLMs) may struggle in specialised machine translation, but the conditions under which fine-tuning improves translation performance remain unclear. This study compares full-parameter fine-tuning (FPFT) and parameter-efficient fine-tuning (PEFT) for Chinese-English political discourse translation using a purpose-built corpus and the Qwen3-14B model. Translation performance was assessed on three 50-item test sets using BLEU, ROUGE-L F1, METEOR, and BERTScore F1, together with BLEU pass-rate likelihood-ratio G2 tests, paired t-tests, and paired Cohen’s dz for item-level score differences. The results reveal a clear contrast between unseen in-domain evaluation, maximum-overlap benchmarking, and semantically related but non-fine-tuned evaluation. On Test Set A and Test Set C, neither fine-tuning strategy produced a statistically significant BLEU pass-rate advantage over the base model, and paired tests across the continuous metrics did not show consistent fine-tuning gains. On Test Set B, which was sampled from the fine-tuning corpus, both fine-tuned models substantially outperformed the base model across all four metrics, with FPFT achieving the highest scores and PEFT providing a more computationally efficient alternative. These findings indicate that textual overlap between training and deployment data, rather than broad domain similarity alone, strongly conditions the observed benefit of fine-tuning. The study offers an empirically grounded framework for selecting fine-tuning strategies in specialised machine translation.
Most endangered languages lack the parallel data required for machine translation, despite the existence of descriptive grammar books. We introduce a pipeline that uses large language models to extract grammatical rules, example sentences, and lexicons from grammar books and generate synthetic parallel corpora for fine-tuning-rather than feeding grammar content into prompts at inference time, as in prior work. Validated on three typologically diverse low-resource languages-Kalamang (Papuan), Tuatschin (Romance), and Mandan (Siouan)-we show that fine-tuning on synthetic data improves over seed-data baselines in 75% of configurations for Kalamang and 59% for Tuatschin, with best-case ChrF++ gains of +8.8, +5.3, and +3.3 respectively. Through a systematic factorial study across 96 configurations varying target part-of-speech, retrieval granularity, and sample volume, we identify which factor combinations drive gains and where they break down. Our results demonstrate that static linguistic documentation can be repurposed for machine translation fine-tuning, offering a practical path towards translation tools for severely under-resourced languages.
V. Ravikumar, Sina Ahmadi, Lena A. Jager et al.· 0 citations
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
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