Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks

TL;DR

This paper introduces scheduled sampling, a curriculum learning method that gradually shifts training from using true previous tokens to generated ones, reducing discrepancy errors in sequence prediction with RNNs, and improves performance in tasks like image captioning.

Contribution

It proposes a novel scheduled sampling technique that bridges training and inference in RNNs, leading to better sequence generation performance.

Findings

Significant improvements in sequence prediction tasks.

Successful application in MSCOCO image captioning challenge 2015.

Reduces error accumulation during sequence generation.

Abstract

Recurrent Neural Networks can be trained to produce sequences of tokens given some input, as exemplified by recent results in machine translation and image captioning. The current approach to training them consists of maximizing the likelihood of each token in the sequence given the current (recurrent) state and the previous token. At inference, the unknown previous token is then replaced by a token generated by the model itself. This discrepancy between training and inference can yield errors that can accumulate quickly along the generated sequence. We propose a curriculum learning strategy to gently change the training process from a fully guided scheme using the true previous token, towards a less guided scheme which mostly uses the generated token instead. Experiments on several sequence prediction tasks show that this approach yields significant improvements. Moreover, it was used successfully in our winning entry to the MSCOCO image captioning challenge, 2015.