Translation of documents or long-form texts has always been a complex task. Manual translation is time-consuming and can be inconsistent due to human error. The traditional machine translation methods often struggle with maintaining the accuracy and context, especially for long documents, and fail to incorporate nuances of the source and target languages. It's also hard to scale manual translation for large amounts of text or when quick translation is needed.
The use of Artificial Intelligence (AI) has revolutionized document translation. With the help of Machine Learning algorithms and Natural Language Processing (NLP) techniques, AI-powered translation tools can process and translate large volumes of text in a fraction of the time compared to manual translation.
This is achieved by training the AI model on a large dataset consisting of source language-target language pairs. The model learns patterns, syntax, semantics, and context from this dataset, enabling it to generate translations that are not only accurate but also contextually relevant. Newer models such as transformers (e.g., Google's BERT, OpenAI's GPT series) have taken this a step further by understanding and generating translations based on the context of the entire document instead of translating sentence-by-sentence, which can often lose the overall context and meaning.