  The competitive performance of neural machine translation (NMT) critically
relies on large amounts of training data. However, acquiring high-quality
translation pairs requires expert knowledge and is costly. Therefore, how to
best utilize a given dataset of samples with diverse quality and
characteristics becomes an important yet understudied question in NMT.
Curriculum learning methods have been introduced to NMT to optimize a model's
performance by prescribing the data input order, based on heuristics such as
the assessment of noise and difficulty levels. However, existing methods
require training from scratch, while in practice most NMT models are
pre-trained on big data already. Moreover, as heuristics, they do not
generalize well. In this paper, we aim to learn a curriculum for improving a
pre-trained NMT model by re-selecting influential data samples from the
original training set and formulate this task as a reinforcement learning
problem. Specifically, we propose a data selection framework based on
Deterministic Actor-Critic, in which a critic network predicts the expected
change of model performance due to a certain sample, while an actor network
learns to select the best sample out of a random batch of samples presented to
it. Experiments on several translation datasets show that our method can
further improve the performance of NMT when original batch training reaches its
ceiling, without using additional new training data, and significantly
outperforms several strong baseline methods.
