Explanation-based natural language acquisition using universal linguistic principles as innate domain theory

In this paper, we propose a new natural language acquisition model (called EBNLA) based on explanation-based language (EBL). To apply EBL to the natural language acquisition domain, suitable universal linguistic principles are incorporated as domain theory. The domain theory consists of two parts: static and dynamic. The static part, which is assumed to he invariant and innate to the model, includes theta theory in government-binding theory and universal feature instantiation principles in generalized phrase structure grammar. The dynamic part contains context-free grammar rules as well as syntactic and thematic features of lexicons. In parsing (problem solving), both parrs work together to parse input sentences. At parsing fails, learning is triggered to enrich and generalize the dynamic part by obeying the principles in the static part. By introducing EBL and the universal linguistic principles, portability of the model and leamability of knowledge in the real-world natural language acquisition domain can be improved.