Pragmatic Failures in Machine Translation: A Case Study on Arabic–English Data

Machine translation (MT) systems have come a long way in the last few decades. But pragmatic fails are still a big problem, especially when the two languages are very different in society and language, like when Arabic and English are very different. Sometimes machine translations make mistakes or don't say what the user meant them to say. This is called a pragmatic fail. This can lead to confusion and make the translation less good. A full case study is used in this article to show how pragmatic mistakes happen in machine translation from Arabic to English. The study looks at a group of translated texts from various genres, such as news stories, books, and everyday talks, to discover similar types of pragmatic mistakes. Some of these are misinterpreting subtle speech, not being polite, and not dealing with cultural references correctly. Ideas from pragmatics and language studies are used in the study to look at how these mistakes affect users' ability to understand and bliss. A mix of qualitative speech analysis, marking mistakes with numbers, and polls of users are used in the study. The results show that machine translation systems don't understand and copy details in language use very well. It's clear that practical skills should be a part of MT plans. This study shows important details that computer models often miss when translating from Arabic to English. This makes machine translation better. It also gives ideas for how the system could be made better in the future so that texts are more correct and take into account regional differences.