# Toward Multilingual Neural Machine Translation with Universal Encoder and Decoder

arXiv: https://arxiv.org/abs/1611.04798 · HTML: https://mlsanity.com/paper/1611.04798/

## Key facts

- Cited by 76 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2016.

## Built on by (most cited first, 30 of 76)

- [WARP: Word-level Adversarial ReProgramming](https://mlsanity.com/paper/2101.00121/) (2021)
- [Neural Machine Translation for Low-Resource Languages: A Survey](https://mlsanity.com/paper/2106.15115/) (2021)
- [The Multilingual TEDx Corpus for Speech Recognition and Translation](https://mlsanity.com/paper/2102.01757/) (2021)
- [Survey of Low-Resource Machine Translation](https://mlsanity.com/paper/2109.00486/) (2021)
- [A Survey on Green Deep Learning](https://mlsanity.com/paper/2111.05193/) (2021)
- [Multilingual training for Software Engineering](https://mlsanity.com/paper/2112.02043/) (2021)
- [Improving Multilingual Translation by Representation and Gradient Regularization](https://mlsanity.com/paper/2109.04778/) (2021)
- [CUNI-KIT System for Simultaneous Speech Translation Task at IWSLT 2022](https://mlsanity.com/paper/2204.06028/) (2022)
- [Language Tags Matter for Zero-Shot Neural Machine Translation](https://mlsanity.com/paper/2106.07930/) (2021)
- [Middle-Layer Representation Alignment for Cross-Lingual Transfer in Fine-Tuned LLMs](https://mlsanity.com/paper/2502.14830/) (2025)
- [Importance-based Neuron Allocation for Multilingual Neural Machine Translation](https://mlsanity.com/paper/2107.06569/) (2021)
- [Causes and Cures for Interference in Multilingual Translation](https://mlsanity.com/paper/2212.07530/) (2022)
- [Multilingual Unsupervised Neural Machine Translation with Denoising Adapters](https://mlsanity.com/paper/2110.10472/) (2021)
- [Lego-MT: Learning Detachable Models for Massively Multilingual Machine Translation](https://mlsanity.com/paper/2212.10551/) (2022)
- [Towards the Next 1000 Languages in Multilingual Machine Translation: Exploring the Synergy Between Supervised and Self-Supervised Learning](https://mlsanity.com/paper/2201.03110/) (2022)
- [Distributionally Robust Multilingual Machine Translation](https://mlsanity.com/paper/2109.04020/) (2021)
- [Improving Neural Machine Translation by Bidirectional Training](https://mlsanity.com/paper/2109.07780/) (2021)
- [A Survey on Low-Resource Neural Machine Translation](https://mlsanity.com/paper/2107.04239/) (2021)
- [One Adapter for All Programming Languages? Adapter Tuning for Code Search and Summarization](https://mlsanity.com/paper/2303.15822/) (2023)
- [Multilingual Machine Translation with Hyper-Adapters](https://mlsanity.com/paper/2205.10835/) (2022)
- [Viewing Knowledge Transfer in Multilingual Machine Translation Through a Representational Lens](https://mlsanity.com/paper/2305.11550/) (2023)
- [Rethinking Zero-shot Neural Machine Translation: From a Perspective of Latent Variables](https://mlsanity.com/paper/2109.04705/) (2021)
- [Language-Family Adapters for Low-Resource Multilingual Neural Machine Translation](https://mlsanity.com/paper/2209.15236/) (2022)
- [Efficient Weight factorization for Multilingual Speech Recognition](https://mlsanity.com/paper/2105.03010/) (2021)
- [Multilingual Neural Machine Translation with Deep Encoder and Multiple Shallow Decoders](https://mlsanity.com/paper/2206.02079/) (2022)
- [MMTAfrica: Multilingual Machine Translation for African Languages](https://mlsanity.com/paper/2204.04306/) (2022)
- [Assessing Evaluation Metrics for Speech-to-Speech Translation](https://mlsanity.com/paper/2110.13877/) (2021)
- [Improving Zero-shot Multilingual Neural Machine Translation by Leveraging Cross-lingual Consistency Regularization](https://mlsanity.com/paper/2305.07310/) (2023)
- [Beyond Shared Vocabulary: Increasing Representational Word Similarities across Languages for Multilingual Machine Translation](https://mlsanity.com/paper/2305.14189/) (2023)
- [Towards Higher Pareto Frontier in Multilingual Machine Translation](https://mlsanity.com/paper/2305.15718/) (2023)

## Abstract (arXiv)

In this paper, we present our first attempts in building a multilingual Neural Machine Translation framework under a unified approach. We are then able to employ attention-based NMT for many-to-many multilingual translation tasks. Our approach does not require any special treatment on the network architecture and it allows us to learn minimal number of free parameters in a standard way of training. Our approach has shown its effectiveness in an under-resourced translation scenario with considerable improvements up to 2.6 BLEU points. In addition, the approach has achieved interesting and promising results when applied in the translation task that there is no direct parallel corpus between source and target languages.