# Semi-Supervised Learning for Neural Machine Translation

arXiv: https://arxiv.org/abs/1606.04596 · HTML: https://mlsanity.com/paper/1606.04596/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2016.

## Built on by (most cited first, 30 of 36)

- [Neural Machine Translation for Low-Resource Languages: A Survey](https://mlsanity.com/paper/2106.15115/) (2021)
- [An Empirical Survey of Data Augmentation for Limited Data Learning in NLP](https://mlsanity.com/paper/2106.07499/) (2021)
- [Survey of Low-Resource Machine Translation](https://mlsanity.com/paper/2109.00486/) (2021)
- [Self-Training Sampling with Monolingual Data Uncertainty for Neural Machine Translation](https://mlsanity.com/paper/2106.00941/) (2021)
- [SAM as the Guide: Mastering Pseudo-Label Refinement in Semi-Supervised Referring Expression Segmentation](https://mlsanity.com/paper/2406.01451/) (2024)
- [Meta Back-translation](https://mlsanity.com/paper/2102.07847/) (2021)
- [Learning to Generalize to More: Continuous Semantic Augmentation for Neural Machine Translation](https://mlsanity.com/paper/2204.06812/) (2022)
- [Self-supervised and Supervised Joint Training for Resource-rich Machine Translation](https://mlsanity.com/paper/2106.04060/) (2021)
- [MMTAfrica: Multilingual Machine Translation for African Languages](https://mlsanity.com/paper/2204.04306/) (2022)
- [Extended Parallel Corpus for Amharic-English Machine Translation](https://mlsanity.com/paper/2104.03543/) (2021)
- [Semi-Supervised Wide-Angle Portraits Correction by Multi-Scale Transformer](https://mlsanity.com/paper/2109.08024/) (2021)
- [Duplex Sequence-to-Sequence Learning for Reversible Machine Translation](https://mlsanity.com/paper/2105.03458/) (2021)
- [Semi-supervised Fine-tuning for Large Language Models](https://mlsanity.com/paper/2410.14745/) (2024)
- [Modeling the Interaction between Agents in Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2102.06042/) (2021)
- [Addressing the Vulnerability of NMT in Input Perturbations](https://mlsanity.com/paper/2104.09810/) (2021)
- [Self-Training Vision Language BERTs with a Unified Conditional Model](https://mlsanity.com/paper/2201.02010/) (2022)
- [Token-wise Curriculum Learning for Neural Machine Translation](https://mlsanity.com/paper/2103.11088/) (2021)
- [Attentive fine-tuning of Transformers for Translation of low-resourced languages @LoResMT 2021](https://mlsanity.com/paper/2108.08556/) (2021)
- [Flow-Adapter Architecture for Unsupervised Machine Translation](https://mlsanity.com/paper/2204.12225/) (2022)
- [Alternated Training with Synthetic and Authentic Data for Neural Machine Translation](https://mlsanity.com/paper/2106.08582/) (2021)
- [Medical Imaging with Deep Learning for COVID- 19 Diagnosis: A Comprehensive Review](https://mlsanity.com/paper/2107.09602/) (2021)
- [Semisupervised Neural Proto-Language Reconstruction](https://mlsanity.com/paper/2406.05930/) (2024)
- [Fine-grained Video Dubbing Duration Alignment with Segment Supervised Preference Optimization](https://mlsanity.com/paper/2508.08550/) (2025)
- [Competency-Aware Neural Machine Translation: Can Machine Translation Know its Own Translation Quality?](https://mlsanity.com/paper/2211.13865/) (2022)
- [Text Style Transfer Back-Translation](https://mlsanity.com/paper/2306.01318/) (2023)
- [The Impact of Syntactic and Semantic Proximity on Machine Translation with Back-Translation](https://mlsanity.com/paper/2403.18031/) (2024)
- [Exploiting Domain-Specific Parallel Data on Multilingual Language Models for Low-resource Language Translation](https://mlsanity.com/paper/2412.19522/) (2024)
- [Learning from Multiple Sources for Data-to-Text and Text-to-Data](https://mlsanity.com/paper/2302.11269/) (2023)
- [Chem-FINESE: Validating Fine-Grained Few-shot Entity Extraction through Text Reconstruction](https://mlsanity.com/paper/2401.10189/) (2024)
- [Zero-Shot Language Transfer vs Iterative Back Translation for Unsupervised Machine Translation](https://mlsanity.com/paper/2104.00106/) (2021)

## Abstract (arXiv)

While end-to-end neural machine translation (NMT) has made remarkable progress recently, NMT systems only rely on parallel corpora for parameter estimation. Since parallel corpora are usually limited in quantity, quality, and coverage, especially for low-resource languages, it is appealing to exploit monolingual corpora to improve NMT. We propose a semi-supervised approach for training NMT models on the concatenation of labeled (parallel corpora) and unlabeled (monolingual corpora) data. The central idea is to reconstruct the monolingual corpora using an autoencoder, in which the source-to-target and target-to-source translation models serve as the encoder and decoder, respectively. Our approach can not only exploit the monolingual corpora of the target language, but also of the source language. Experiments on the Chinese-English dataset show that our approach achieves significant improvements over state-of-the-art SMT and NMT systems.