Zero-shot Sequence Labeling: Transferring Knowledge from Sentences to Tokens

Marek Rei; Anders Søgaard

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arXiv · 1805.02214

Zero-shot Sequence Labeling: Transferring Knowledge from Sentences to Tokens

Marek Rei·Anders Søgaard

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Abstract

Can attention- or gradient-based visualization techniques be used to infer token-level labels for binary sequence tagging problems, using networks trained only on sentence-level labels? We construct a neural network architecture based on soft attention, train it as a binary sentence classifier and evaluate against token-level annotation on four different datasets. Inferring token labels from a network provides a method for quantitatively evaluating what the model is learning, along with generating useful feedback in assistance systems. Our results indicate that attention-based methods are able to predict token-level labels more accurately, compared to gradient-based methods, sometimes even rivaling the supervised oracle network.

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Marek Rei, Anders Søgaard. 2018-05-06. Zero-shot Sequence Labeling: Transferring Knowledge from Sentences to Tokens. https://arxiv.org/abs/1805.02214

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