Google translate struggling with translating Nepali to English

Abstract

This study examines the accuracy and limitations of Google Translate in converting text between Nepali and English, two linguistically and culturally distinct languages. While Google Translate has made significant strides through neural machine translation (NMT), it continues to struggle with lexical accuracy, syntactic structure, semantic clarity, and cultural nuance—particularly for low-resource languages like Nepali. Drawing on a corpus of 200 sentences sourced from academic texts, dictionaries, and natural speech, this research categorizes common translation errors into four types: lexical, syntactic, semantic, and cultural. The findings highlight frequent mistranslations, particularly involving idiomatic expressions, honorifics, and the Subject–Object–Verb (SOV) structure of Nepali. The paper concludes with recommendations to improve machine translation performance, including enhancing contextual modeling, expanding linguistic datasets, and incorporating culturally informed input from native speakers. These improvements are essential for building more accurate, inclusive, and context-sensitive translation tools.