Emergent Multilingual Language Acquisition Using Developmental Networks

There has been work on language acquisition but such prior work was based on symbolic representations and non-incremental learning. Neural Networks are meant for incremental learning but their performance has been weak. This situation was mainly due to a "lack of logic" in neural networks. By language acquisition here we mean incremental learning from lifetime experience. Since developmental networks (DN) has clearly understandable emergent "logic" in terms of finite automata and Turing machines, this is the first work on language acquisition based on a clearly understandable emergent Turing machine. We show how symbolic words are represented by patterns instead of (handcrafted) symbols to simulate naturally grounded and emergent inputs. The context as states/actions are also represented by patterns to simulate naturally grounded and emergent inputs. Our work demonstrates that symbolic state features can be fully automated by emergent input-context pattern pairs. This is a step toward fully automated acquisition of language by a grounded robot, but we are not there yet.