One-shot and few-shot learning of word embeddings

Andrew K. Lampinen; James L. McClelland

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arXiv · 1710.10280

One-shot and few-shot learning of word embeddings

Andrew K. Lampinen·James L. McClelland

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Abstract

Standard deep learning systems require thousands or millions of examples to learn a concept, and cannot integrate new concepts easily. By contrast, humans have an incredible ability to do one-shot or few-shot learning. For instance, from just hearing a word used in a sentence, humans can infer a great deal about it, by leveraging what the syntax and semantics of the surrounding words tells us. Here, we draw inspiration from this to highlight a simple technique by which deep recurrent networks can similarly exploit their prior knowledge to learn a useful representation for a new word from little data. This could make natural language processing systems much more flexible, by allowing them to learn continually from the new words they encounter.

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Andrew K. Lampinen, James L. McClelland. 2017-10-27. One-shot and few-shot learning of word embeddings. https://arxiv.org/abs/1710.10280

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