Bandit Structured Prediction for Neural Sequence-to-Sequence Learning

Julia Kreutzer; Artem Sokolov; Stefan Riezler

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arXiv · 1704.06497

Bandit Structured Prediction for Neural Sequence-to-Sequence Learning

Julia Kreutzer·Artem Sokolov·Stefan Riezler

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Abstract

Bandit structured prediction describes a stochastic optimization framework where learning is performed from partial feedback. This feedback is received in the form of a task loss evaluation to a predicted output structure, without having access to gold standard structures. We advance this framework by lifting linear bandit learning to neural sequence-to-sequence learning problems using attention-based recurrent neural networks. Furthermore, we show how to incorporate control variates into our learning algorithms for variance reduction and improved generalization. We present an evaluation on a neural machine translation task that shows improvements of up to 5.89 BLEU points for domain adaptation from simulated bandit feedback.

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BibTeX RIS

Julia Kreutzer, Artem Sokolov, Stefan Riezler. 2017-04-21. Bandit Structured Prediction for Neural Sequence-to-Sequence Learning. https://arxiv.org/abs/1704.06497

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