2007 NIPS NeurIPS 2007

A Bayesian Framework for Cross-Situational Word-Learning

Abstract

For infants, early word learning is a chicken-and-egg problem. One way to learn a word is to observe that it co-occurs with a particular referent across different situations. Another way is to use the social context of an utterance to infer the in- tended referent of a word. Here we present a Bayesian model of cross-situational word learning, and an extension of this model that also learns which social cues are relevant to determining reference. We test our model on a small corpus of mother-infant interaction and find it performs better than competing models. Fi- nally, we show that our model accounts for experimental phenomena including mutual exclusivity, fast-mapping, and generalization from social cues. To understand the difficulty of an infant word-learner, imagine walking down the street with a friend who suddenly says “dax blicket philbin na fivy!” while at the same time wagging her elbow. If you knew any of these words you might infer from the syntax of her sentence that blicket is a novel noun, and hence the name of a novel object. At the same time, if you knew that this friend indicated her attention by wagging her elbow at objects, you might infer that she intends to refer to an object in a nearby show window. On the other hand if you already knew that “blicket” meant the object in the window, you might be able to infer these elements of syntax and social cues. Thus, the problem of early word-learning is a classic chicken-and-egg puzzle: in order to learn word meanings, learners must use their knowledge of the rest of language (including rules of syntax, parts of speech, and other word meanings) as well as their knowledge of social situations. But in order to learn about the facts of their language they must first learn some words, and in order to determine which cues matter for establishing reference (for instance, pointing and looking at an object but normally not waggling your elbow) they must first have a way to know the intended referent in some situation

🌉 Interdisciplinary Bridge — Artificial Intelligence and Interdisciplinary and Machine Learning
📈 Trend Setter — Computational Linguistics
🧭 Keyword Pioneer — cross-situational word learning
🐝 Cross-Pollinator — Artificial Intelligence, Computer Vision, Deep Learning, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Speech & Audio
🐣 Hot Topic Early Bird — semantic analysis