Artificial Intelligence (AI) has made very rapid advances over the past few years and has changed how quickly we can use AI's capabilities via neural networks and deep learning for linguistic translation. Industries like law and medicine have depended more on AI-generated translations, and we will need stringent evaluation frameworks as we move into the future with these applications of AI. This paper will compare the methods used to assess whether a translation is adequate qualitatively and quantitatively. The qualitative approach and assessment methods will include but are not limited to the following: expert reviews of language; user satisfaction surveys, and the ability of these methods to capture cultural nuance and context-related fidelity even though they are subjectively based. Quantitative assessment methods are generally based on the number of words translated correctly and the percentage of words translated correctly; for instance, they might refer to BLEU or METEOR scoring based on user feedback. Additionally, although quantitative assessment methods are constantly changing as they get better with increased data availability, the challenges of current evaluation models will include the fact that all current models have a degree of training data bias, and static models cannot adapt to language change or evolution over time. In conclusion, this paper advocates for establishing hybrid methods, taking advantage of the digital abilities of AI alongside the human ability to be interpretive. Future research will require not only collaboration between various fields of study, the ability to learn from adaptive learning processes, and the establishment of common reporting guidelines to ensure that translations are high-quality and culturally appropriate.
| Mualliflar | Raimov, Lazizjon, Раимов, Лазизжон, Raimov, Lazizjon |
|---|---|
| Jurnal | Хорижий лингвистика ва лингводидактика |
| Nashr sanasi | 2025-11-25 |
| Jild | 3 |
| Son | 10/S |
| Betlar | 378-383 |
| Til | Ingliz |
| DOI | 10.47689/2181-3701-vol3-iss10/s-pp378-383 |
DOI: 10.47689/2181-3701-vol3-iss10/s-pp378-383 · Maqolaning asl sahifasi
Artificial Intelligence (AI) Translation, Machine Translation Evaluation, Qualitative Assessment, Quantitative Metrics, BLEU Score, Hybrid Methodology, Natural Language Processing (NLP), Linguistic Adequacy, Deep Learning, Перевод с помощью искусственного интеллекта (ИИ), Оценка машинного перевода, Качественная оценка, Количественные метрики, Оценка BLEU, Гибридная методология, Обработка естественного языка (NLP), Лингвистическая адекватность, Глубокое обучение, Sun’iy intellekt (SI) tarjimasi, mashina tarjimasini baholash, sifat baholash, miqdoriy metrikalar, BLEU bahosi, gibrid metodologiya, tabiiy tilni qayta ishlash (NLP), lingvistik muvofiqlik, chuqur o‘rganish
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