Computational Modeling as a Methodology for Studying Human Language Learning

The nature and amount of information needed for learning a natural language, and the underlying mechanisms involved in this process, are the subject of much debate: how is the knowledge of language represented in the human brain? Is it possible to learn a language from usage data only, or is some sort of innate knowledge and/or bias needed to boost the process? Are di↵erent aspects of language learned in order? These are topics of interest to (psycho)linguists who study human language acquisition, as well as to computational linguists who develop the knowledge sources necessary for large-scale natural language processing systems. Children are the ultimate subjects of any study of language learnability. They learn language with ease, in a short period of time and their acquired knowledge of language is flexible and robust. Human language acquisition has been studied for centuries, but using computational modeling for such studies is a relatively recent trend. However, computational approaches to language learning have become increasingly popular, mainly due to advances in developing machine learning techniques, and the availability of large collections of experimental data on child language learning and child-adult interaction. Many of the existing computational models attempt to study the complex task of learning a language under cognitively plausible criteria (such as memory and processing limitations that humans face), and to explain the developmental stages observed in children. By simulating the process of child language learning, computational models can show us which linguistic representations are learnable from the input that children have access to in a reasonable amount of time, and which mechanisms yield the same patterns of behaviour that children exhibit during this process. In doing so, computational modeling provides insight into the plausible