# Pieces of Eight: 8-bit Neural Machine Translation

arXiv: https://arxiv.org/abs/1804.05038 · HTML: https://mlsanity.com/paper/1804.05038/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Apr 2018.

## Built on by (most cited first, 7 of 7)

- [Efficient Methods for Natural Language Processing: A Survey](https://mlsanity.com/paper/2209.00099/) (2022)
- [Intriguing Properties of Quantization at Scale](https://mlsanity.com/paper/2305.19268/) (2023)
- [The Low-Resource Double Bind: An Empirical Study of Pruning for Low-Resource Machine Translation](https://mlsanity.com/paper/2110.03036/) (2021)
- [Gender Bias Amplification During Speed-Quality Optimization in Neural Machine Translation](https://mlsanity.com/paper/2106.00169/) (2021)
- [Understanding Parameter Sharing in Transformers](https://mlsanity.com/paper/2306.09380/) (2023)
- [MobileNMT: Enabling Translation in 15MB and 30ms](https://mlsanity.com/paper/2306.04235/) (2023)
- [Zero-Shot Dynamic Quantization for Transformer Inference](https://mlsanity.com/paper/2211.09744/) (2022)

## Abstract (arXiv)

Neural machine translation has achieved levels of fluency and adequacy that would have been surprising a short time ago. Output quality is extremely relevant for industry purposes, however it is equally important to produce results in the shortest time possible, mainly for latency-sensitive applications and to control cloud hosting costs. In this paper we show the effectiveness of translating with 8-bit quantization for models that have been trained using 32-bit floating point values. Results show that 8-bit translation makes a non-negligible impact in terms of speed with no degradation in accuracy and adequacy.