Coactive Learning for Interactive Machine Translation

Coactive learning describes the interaction between an online structured learner and a human user who corrects the learner by responding with weak feedback, that is, with an improved, but not necessarily optimal, structure. We apply this framework to discriminative learning in interactive machine translation. We present a generalization to latent variable models and give regret and generalization bounds for online learning with a feedback-based latent perceptron. We show experimentally that learning from weak feedback in machine translation leads to convergence in regret and translation error.

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