# Variational Neural Machine Translation

arXiv: https://arxiv.org/abs/1605.07869 · HTML: https://mlsanity.com/paper/1605.07869/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2016.

## Built on by (most cited first, 30 of 35)

- [Survey of Low-Resource Machine Translation](https://mlsanity.com/paper/2109.00486/) (2021)
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- [Conditional Variational Autoencoder for Sign Language Translation with Cross-Modal Alignment](https://mlsanity.com/paper/2312.15645/) (2023)
- [Why is constrained neural language generation particularly challenging?](https://mlsanity.com/paper/2206.05395/) (2022)
- [Modeling Bilingual Conversational Characteristics for Neural Chat Translation](https://mlsanity.com/paper/2107.11164/) (2021)
- [A Variational Hierarchical Model for Neural Cross-Lingual Summarization](https://mlsanity.com/paper/2203.03820/) (2022)
- [Latent Space Energy-Based Model of Symbol-Vector Coupling for Text Generation and Classification](https://mlsanity.com/paper/2108.11556/) (2021)
- [From Discourse to Narrative: Knowledge Projection for Event Relation Extraction](https://mlsanity.com/paper/2106.08629/) (2021)
- [Translatotron-V(ison): An End-to-End Model for In-Image Machine Translation](https://mlsanity.com/paper/2407.02894/) (2024)
- [Learning to Generalize to More: Continuous Semantic Augmentation for Neural Machine Translation](https://mlsanity.com/paper/2204.06812/) (2022)
- [WR-ONE2SET: Towards Well-Calibrated Keyphrase Generation](https://mlsanity.com/paper/2211.06862/) (2022)
- [Graph Neural Networks in Vision-Language Image Understanding: A Survey](https://mlsanity.com/paper/2303.03761/) (2023)
- [DySurv: dynamic deep learning model for survival analysis with conditional variational inference](https://mlsanity.com/paper/2310.18681/) (2023)
- [Leveraging Large Language Models for Bengali Math Word Problem Solving with Chain of Thought Reasoning](https://mlsanity.com/paper/2505.21354/) (2025)
- [Confidence Based Bidirectional Global Context Aware Training Framework for Neural Machine Translation](https://mlsanity.com/paper/2202.13663/) (2022)
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- [KGR^4: Retrieval, Retrospect, Refine and Rethink for Commonsense Generation](https://mlsanity.com/paper/2112.08266/) (2021)
- [Target-Side Augmentation for Document-Level Machine Translation](https://mlsanity.com/paper/2305.04505/) (2023)
- [Mitigating Data Imbalance and Representation Degeneration in Multilingual Machine Translation](https://mlsanity.com/paper/2305.12786/) (2023)
- [Flow-Adapter Architecture for Unsupervised Machine Translation](https://mlsanity.com/paper/2204.12225/) (2022)
- [Empowering Bengali Education with AI: Solving Bengali Math Word Problems through Transformer Models](https://mlsanity.com/paper/2501.02599/) (2025)
- [Discrete Auto-regressive Variational Attention Models for Text Modeling](https://mlsanity.com/paper/2106.08571/) (2021)
- [Langevin Autoencoders for Learning Deep Latent Variable Models](https://mlsanity.com/paper/2209.07036/) (2022)
- [MNAFT: modality neuron-aware fine-tuning of multimodal large language models for image translation](https://mlsanity.com/paper/2604.16943/) (2026)
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- [A Conditional Generative Matching Model for Multi-lingual Reply Suggestion](https://mlsanity.com/paper/2109.07046/) (2021)

## Abstract (arXiv)

Models of neural machine translation are often from a discriminative family of encoderdecoders that learn a conditional distribution of a target sentence given a source sentence. In this paper, we propose a variational model to learn this conditional distribution for neural machine translation: a variational encoderdecoder model that can be trained end-to-end. Different from the vanilla encoder-decoder model that generates target translations from hidden representations of source sentences alone, the variational model introduces a continuous latent variable to explicitly model underlying semantics of source sentences and to guide the generation of target translations. In order to perform efficient posterior inference and large-scale training, we build a neural posterior approximator conditioned on both the source and the target sides, and equip it with a reparameterization technique to estimate the variational lower bound. Experiments on both Chinese-English and English- German translation tasks show that the proposed variational neural machine translation achieves significant improvements over the vanilla neural machine translation baselines.