# A Stochastic Decoder for Neural Machine Translation

arXiv: https://arxiv.org/abs/1805.10844 · HTML: https://mlsanity.com/paper/1805.10844/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2018.

## Built on by (most cited first, 5 of 5)

- [Survey of Low-Resource Machine Translation](https://mlsanity.com/paper/2109.00486/) (2021)
- [Reinforcement Learning with Large Action Spaces for Neural Machine Translation](https://mlsanity.com/paper/2210.03053/) (2022)
- [Rapid Review of Generative AI in Smart Medical Applications](https://mlsanity.com/paper/2406.06627/) (2024)
- [Generating Diverse Descriptions from Semantic Graphs](https://mlsanity.com/paper/2108.05659/) (2021)
- [WakaVT: A Sequential Variational Transformer for Waka Generation](https://mlsanity.com/paper/2104.00426/) (2021)

## Abstract (arXiv)

The process of translation is ambiguous, in that there are typically many valid trans- lations for a given sentence. This gives rise to significant variation in parallel cor- pora, however, most current models of machine translation do not account for this variation, instead treating the prob- lem as a deterministic process. To this end, we present a deep generative model of machine translation which incorporates a chain of latent variables, in order to ac- count for local lexical and syntactic varia- tion in parallel corpora. We provide an in- depth analysis of the pitfalls encountered in variational inference for training deep generative models. Experiments on sev- eral different language pairs demonstrate that the model consistently improves over strong baselines.