An Actor-Critic Algorithm for Sequence Prediction

TL;DR

This paper introduces an actor-critic reinforcement learning approach for sequence prediction tasks, improving training-test consistency and optimizing task-specific metrics like BLEU in natural language generation.

Contribution

It presents a novel supervised learning method using a critic network to better align training with testing conditions in sequence generation models.

Findings

Improved BLEU scores in machine translation.

Enhanced performance on synthetic sequence tasks.

Closer training-test alignment in sequence prediction.

Abstract

We present an approach to training neural networks to generate sequences using actor-critic methods from reinforcement learning (RL). Current log-likelihood training methods are limited by the discrepancy between their training and testing modes, as models must generate tokens conditioned on their previous guesses rather than the ground-truth tokens. We address this problem by introducing a \textit{critic} network that is trained to predict the value of an output token, given the policy of an \textit{actor} network. This results in a training procedure that is much closer to the test phase, and allows us to directly optimize for a task-specific score such as BLEU. Crucially, since we leverage these techniques in the supervised learning setting rather than the traditional RL setting, we condition the critic network on the ground-truth output. We show that our method leads to improved performance on both a synthetic task, and for German-English machine translation. Our analysis paves the way for such methods to be applied in natural language generation tasks, such as machine translation, caption generation, and dialogue modelling.