9.1: Introduction

Child language acquisition and machine learning are two different topics but the core questions are alike. Infants or algorithms are exposed to linguistic input. They are able to find patterns in the input and either produce input-like output (e.g. children start to speak.) or perform other task based on the input (e.g machine translation).

Language is a defining property to human beings. It is a cultural artifact and an important communication tool. Linguistically speaking, all languages in the world can be defined as a collection of sound/meaning pairs. People hear speech stream and make sense out to it. Moreover, they can also produce similar speech in order to communicate. Language is an extremely complex multi-modal system, however, it is acquired by normal developed infants in an effortless manner. For decades, linguistics and psychologists have been trying to understand the mechanism of language acquisition. Chomsky (1986) defined the question on language acquisition into two part: what constitutes knowledge of a language, and how is the knowledge acquired by its users? Theoretical linguists have been working on the first part of the question and psycholinguistics have been working on the second part of the question. The internal paradoxical tension between two parts has been noticed by Chomsky: “To achieve descriptive adequacy it often seems necessary to enrich the system of available devices, whereas to solve our case of Plato’s problem we must restrict the system of available devices so that only a few languages or just one are determined by the given data. It is the tension between these two tasks that makes the field an interesting one, in my view.” (Chomsky, 1986)

This contradiction between adequate descriptive device vs restricted system is also reflects in language acquisition of infants. They learn from the utterances of people around (parents), but the utterances are finite, incomplete, idiosyncratic… They learned language without explicit instructions, that nobody teaches a 2y/o to put a subject, a verb and an object in a sentence. They all learn it rapidly. Usually by the age of 5, children are able to communicate without difficulty. Also, the output of acquisition is uniformly. Typical developed children usually achieved the same level of fluency in their native language. To summarize the paradox here, children receive finite and limited set of input and produce infinite and highly original output. To solve this problem, linguists need to find a class of representations that is sufficiently rich to account for the observed dependencies in natural language.

In machine learning tasks, most of the goals are achieved by learning instances through neural network to build a representation of the instance and produce input-like output. Tom Mitchell (1997) definition of Machine Learning: A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E. The instances in language-related machine learning task would be sentences. The more instances for input the better representation could be formed. In language related machine learning tasks, the representation the algorithm needs to mirror is concepts in language, such as phonemes, word, or grammar.

In this way, language acquisition and machine learning are similar in the way that, both children and algorithms have to process input data, find features, build representation and perform desired tasks based on the representation they built. Given a stream of linguistic input, an algorithm or human brain incrementally learns a grammar that captures its statistical patterns, which can then be used to parse or generate new data. Therefore, putting language acquisition and machine learning tasks together could probably provide some new perspectives to solve these two challenges faced in both field. In the following paragraphs of this article, two specific tasks (speech categorization and word categorization) will be discussed in the perspective of child language acquisition and machine learning.