Dynamic Oracle for Neural Machine Translation in Decoding Phase

TL;DR

This paper introduces dynamic oracle-based methods to improve neural machine translation decoding by reducing training-inference discrepancy, leading to better translation quality.

Contribution

It proposes two novel dynamic oracle techniques that enhance scheduled sampling, addressing its limitations and improving NMT performance.

Findings

Improved translation quality over standard NMT.

Effective mitigation of training-inference discrepancy.

Enhanced training process with dynamic oracles.

Abstract

The past several years have witnessed the rapid progress of end-to-end Neural Machine Translation (NMT). However, there exists discrepancy between training and inference in NMT when decoding, which may lead to serious problems since the model might be in a part of the state space it has never seen during training. To address the issue, Scheduled Sampling has been proposed. However, there are certain limitations in Scheduled Sampling and we propose two dynamic oracle-based methods to improve it. We manage to mitigate the discrepancy by changing the training process towards a less guided scheme and meanwhile aggregating the oracle's demonstrations. Experimental results show that the proposed approaches improve translation quality over standard NMT system.