Advancements in Artificial Intelligence (AI) and Deep Learning (DL) have greatly enhanced the accuracy and quality of Machine Translation (MT). Some argue that human translation is no longer necessary.

Human faults are often addressed by self-invented solutions. The development of neural networks in machine translation has led to claims that intelligent systems can now translate as well as humans. Despite advancements in AI, language processing and translation remain challenges. Designing intelligent systems involves inherent biases. The way we design systems is influenced by our personal experiences and biases. The methodology followed in this research which is based on (Hunt at al., 2017) framework is language processing utilizing primary contextual and semantic analysis with reference to comprehensive dictionaries (formed by integrating dictionaries, thesauri, and databases of language and jargon awareness), along with connotation and contextual connotation databases, to perform a thorough parsing of text into parts of speech. The diversity of language structures and cultures makes it unlikely that intelligent robots, even with deep learning skills, can handle them as efficiently as humans can.