The three main techniques used for machine translation are:
Rule-based machine translation (RBMT) uses a set of grammar rules to translate text from one language to another. This technique is often used for technical translations, as it can be very precise. However, it can be difficult to create rules for all possible language pairs, and RBMT systems can be slow. Statistical machine translation (SMT) uses statistical analysis of bilingual text to learn the most likely translation for a given word or phrase. This technique is often used for general-purpose translations, as it can be more accurate than RBMT. However, SMT systems can still produce errors, especially for complex or idiomatic language. Neural machine translation (NMT) uses deep learning to learn the translation of text from one language to another. This technique is the most recent and advanced machine translation technique, and it can produce translations that are indistinguishable from human translations. However, NMT systems can be computationally expensive, and they require a large amount of training data.