Massive Exploration of Neural Machine Translation Architectures

TLDR

Neural Machine Translation has advanced rapidly, with production systems deployed to end‑users, yet it remains unclear which architectural components most influence translation quality. The study systematically analyzes how common hyperparameters affect NMT performance and releases an open‑source TensorFlow framework for reproducibility. The authors performed over 250,000 GPU‑hour experiments on the WMT English‑to‑German task, varying embedding size, network depth, RNN cell type, residual connections, attention mechanism, and decoding heuristics. Empirical results and variance metrics highlight the relative importance of these factors, providing practical guidance for architecture design.

Abstract

Neural Machine Translation (NMT) has shown remarkable progress over the past few years, with production systems now being deployed to end-users. As the field is moving rapidly, it has become unclear which elements of NMT architectures have a significant impact on translation quality. In this work, we present a large-scale analysis of the sensitivity of NMT architectures to common hyperparameters. We report empirical results and variance numbers for several hundred experimental runs, corresponding to over 250,000 GPU hours on a WMT English to German translation task. Our experiments provide practical insights into the relative importance of factors such as embedding size, network depth, RNN cell type, residual connections, attention mechanism, and decoding heuristics. As part of this contribution, we also release an open-source NMT framework in TensorFlow to make it easy for others to reproduce our results and perform their own experiments.