Artificial Intelligence and Translation: Exploring Current Applications, Limitations and Future Potential of Language Models Through Japanese-English Translation
It is noted that current use cases highly depend on the severity and context of the situation in which translations are being produced, but that the future of language models supporting accessible and quality translations is optimistic.
Abstract
This thesis highlights the recent improvements and capabilities of Large Language Models (LLMs), specifically their ability to produce translations between different languages. The continued up-scaling of model sizes has led to breakthroughs in the level of their observed intelligence, allowing them to produce translations that are similar in quality to highly skilled human translators. However, to facilitate the reasoning processes that LLMs now possess, their demand for computational power and the supporting hardware and resources has increased proportionally. Considering the impacts of this technology on the environment, energy resources, and its accessibility, my research explores the possibilities of smaller, highly trained models that can run on low-level consumer hardware while still producing quality translations between languages. Through experimenting with ten small models running locally on my own desktop’s hardware, I produced, evaluated, and analyzed the generated translations created by prompting the models on Japanese to English texts in the contexts of fictional prose, historical writing, and spoken conversation. I conclude my thesis by noting that current use cases highly depend on the severity and context of the situation in which translations are being produced, but that the future of language models supporting accessible and quality translations is optimistic.
Light is shed on the process-oriented nature of translation strategies and empirical evidence for integrating technological tools into translation pedagogy is offered.
Xuefeng Wu, Chenchen Liu· English Language Teaching· 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
The results show that the quality of small ``base'' models can be greatly enhanced, and that reinforcement learning with verifiable rewards can be applied to NMT in the legal domain and surpasses the translation quality of supervised fine-tuning.
Aixiu An, Michael Jungo, Eloi Eynard 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
The evaluation of the quality of translation is an essential and important aspect in translation, which is mostly done by human judgment, whether in machine translation or human translation. Current automatic systems lack in capturing the subtleties of context, and the quality of the translation, which makes them not so scalable or accurate for real-world use. The research introduces a novel approach combining the Dynamic Ant Colony-Transformer-based Enhanced Long Short-Term Memory (DynAc-Trans-ELSTM) model with an AI-Enabled Intelligent Evaluation Modeling System (AI-IEMS) to assess the quality of English translations. The dataset is a large collection of parallel corpora, such as human-generated translations and expert evaluations from different translation platforms. Data pre-processing includes WordPiece Tokenization, BERT for subword text segmentation pre-processing and semantic learning. To extract, identify, and weight terms in the data set that are important, based on their frequency and importance, the term frequency-inverse document frequency (TF-IDF) is used. DynAc-Trans-ELSTM is a novel idea in the area of machine translation evaluation because it dynamically modifies the evaluation process by an intelligent model that is a combination of ACA, Transformer and ELSTM networks. The results show that DynAc-Trans-ELSTM shows better performance than all the baselines and with better accuracy (93.68%). The proposed AI-IEMS based on DynAc-Trans-ELSTM is a potential solution to the previous drawbacks of the models and will be more effective with regard to quality evaluation in a wider range of translation contexts.
Yijun Wu, Yonghong Qin· Discover Internet of Things· 0 citations
CALAMITA is conceived as a rolling benchmark, enabling continuous integration of new tasks and models, and argues that this combination offers a blueprint for other languages and communities seeking inclusive and rigorous LLM evaluation practices.
Malvina Nissim, Danilo Croce, V. Patti et al.· Italian Journal of Computati...· 0 citations