Computational Modeling of Human Language Acquisition

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: is it possible to learn a language from usage data only, or some sort of innate knowledge and/or bias is needed to boost the process? This is a topic of interest to (psycho)linguists who study human language acquisition, as well as computational linguists who develop the knowledge sources necessary for largescale natural language processing systems. Children are a source of inspiration for any such study of language learnability. They learn language with ease, 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 increasing popular, mainly due to the advances in developing machine learning techniques, and the availability of vast 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 the cognitive plausibility criteria (such as memory and processing limitations that humans face), as well as to explain the developmental patterns observed in children. Such computational studies can provide insight into the plausible mechanisms involved in human language acquisition, and be a source of inspiration for developing better language models and techniques.