MAD for Robust Reinforcement Learning in Machine Translation

TL;DR

This paper presents MAD, a distributed policy gradient algorithm that improves training stability and generalization in machine translation by using mean absolute deviation and variance reduction strategies.

Contribution

The paper introduces MAD, a novel distributed policy gradient method with variance reduction techniques, outperforming existing reward-aware training methods in machine translation.

Findings

MAD outperforms REINFORCE, MRT, and PPO in stability and generalization.

Policies trained with MAD perform well with greedy and beam search decoding.

The learned policies are sensitive to the reward functions used during training.

Abstract

We introduce a new distributed policy gradient algorithm and show that it outperforms existing reward-aware training procedures such as REINFORCE, minimum risk training (MRT) and proximal policy optimization (PPO) in terms of training stability and generalization performance when optimizing machine translation models. Our algorithm, which we call MAD (on account of using the mean absolute deviation in the importance weighting calculation), has distributed data generators sampling multiple candidates per source sentence on worker nodes, while a central learner updates the policy. MAD depends crucially on two variance reduction strategies: (1) a conditional reward normalization method that ensures each source sentence has both positive and negative reward translation examples and (2) a new robust importance weighting scheme that acts as a conditional entropy regularizer. Experiments on a variety of translation tasks show that policies learned using the MAD algorithm perform very well when using both greedy decoding and beam search, and that the learned policies are sensitive to the specific reward used during training.