Google Translate primarily uses machine learning algorithms, specifically neural machine translation (NMT), to power its translations. NMT models translate entire sentences at once, taking into account the context and meaning of the entire sentence, rather than translating individual words or phrases in isolation. This approach helps to improve the accuracy and fluency of the translations.
Google Translate also uses a vast amount of data to train its models, including bilingual text corpora, monolingual text corpora, and translation memories. The bilingual text corpora consist of large collections of translated texts in different languages, which are used to train the models to recognize patterns and relationships between languages. Monolingual text corpora, on the other hand, are used to improve the fluency and naturalness of the translations. Translation memories are databases of previously translated texts that are used to improve the consistency and accuracy of the translations.
In terms of accuracy, Google Translate is generally quite accurate for common language pairs, such as English-Spanish, where it has a reported accuracy rate of around 90%. However, the accuracy can vary greatly depending on the language pair and the complexity of the text. For less common language pairs, the accuracy can be much lower, with some reports suggesting that it can be as low as 55% for English-Armenian. Additionally, Google Translate can struggle with colloquial phrases and idiomatic expressions, which can result in translations that are technically correct but lack the nuance and cultural context of a human translation.
Overall, while Google Translate can be a useful tool for quick, basic translations, it is not a replacement for a professional human translator, especially for complex or nuanced texts. Human translators bring a level of cultural and linguistic expertise that machines simply cannot replicate, and they are better equipped to handle the subtleties and complexities of language.