Principles Derived from Neurolinguistics of Brain for Design of Translation Machines

What is it about?

Recently, it was extended by the author the declarative/procedural model to a new semantic/syntactic/episodic model for language, extended to encompass the sentential meanings to the neural linguistic processes. In this article, it is applied this new semantic/syntactic/episodic model of brain to derive three feasible principles to direct machine translation respectively. First, it is necessary to establish the dictionary for translation of words and phrases. Second, it is also necessary to read out the grammar of language to be translated from and to comply with the grammar of language to be translated into, arranging such parts of speech as noun, verb and adjective into order. Third, it is in further necessary to determine one correct meaning of some words of multiple meanings by matching them with statistical associations with others. Whereas, due to the lack of scientific guidance from neurolinguistics, it has mostly been adopted two linguistic processes in the present machine translation, either with only word and grammar translation, or with only word and statistical translation, and therefore has been unsatisfactory. Through comparison, it is pointed out that the machine translation with three principles would exceed the human brain in all three linguistic aspects respectively. In this regard, herein it is newly formulated the three principles derived from the semantic/syntactic/episodic neurolinguistic model of brain for machine translation. Prospectively, it is a significant progression leading the new technological leap of artificial intelligence with acquisition of ability in natural language equal to and even superior to that of human brain.

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Photo by Glenn Carstens-Peters on Unsplash

Photo by Glenn Carstens-Peters on Unsplash

Why is it important?

The 3 principles are derived from the new semantic/syntactic/episodic linguistic model of Cai.

Perspectives