뇌는 인공지능처럼 언어를 학습하는가?: 고빈도 입력데이터와 언어학습
Does brain learn a language as AI learns?: High frequency input data and language learning
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본 논문은 인공지능의 모델인 인공신경망이 모국어 및 제2언어를 학습하는 인간의 뇌의 실제 신경망과 어떻게 닮았는지 이해하는 데에 목적이 있다. 인간언어의 핵심이라고 할 수 있는 통사처리에 대한 최근 뇌과학의 통합적인발견은 과거 신경세포단위에서의 정보전달체계와 기억 학습의 원리와 함께 이해의 폭이 넓어져서 인공지능의 원리와 비교하는 것이 가능해졌다. 본 논문은 이러한 비교를 통해서 인공지능과 뇌신경망은 유사한 점이 많은 것으로결론짓고 또한 전통적인 제2언어습득연구 역시 인공지능, 뇌과학 안에서 설명될 수 있는 가능성이 높다고 논의한다. 입력층과 출력층 사이에 인공신경망이 설정하고 있는 숨은층은 뇌과학에서는 신경전달물질을 분사하는 인터뉴런의 역할에 상응하는 것으로 유추할 수 있고, 전통적인 제2언어습득이론에서는 언어입력과 언어산출 또는 습득사이에 결정적인 역할을 하는 여러 가지 변인들, 매개변인들이 그 역할을 한다는 공통적인 연구프레임을 가지고있음을 논의한다. 효과적인 숨은층 개발을 위해서는 언어, 뇌과학과의 융합이 있어야 할 것이다. 언어입력이 충분히 확보되어야 언어학습이 가능하다는 것은 모든 언어습득이론과 세 분야의 모델이 공통적으로 시사하는 교육적시사점이다.
Abstract
The purpose of this paper is to understand how artificial neural network(ANN) resembles human brain in learning a language. Recent neuroscientific findings on the integrative neural network for syntax along with those of the molecular level mechanisms for memory consolidation enabled us to search their similarities to artificial neural network mechanism. Analogy between the two systems are discussed as highly probable in this paper. Also the traditional language acquisition research is discussed as analogous to both fields of study as well. The research frame shared by the three fields of study is the postulation of hidden layer in ANN, which is analogous to the critical role of interneurons in neuroscience and to the various intermediate variables between the input and output proposed in SLA theories. The large size high frequency input data that feeds ANN to learning is in parallel to language input to the learners that passes through various intermediate variables and results in acquisition. Human integrative neural network embodies the whole process. Securing the enough amount of language input is suggested as one of the most clear implications for learning and teaching a foreign language.
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