# Zero-Resource Translation with Multi-Lingual Neural Machine Translation

arXiv: https://arxiv.org/abs/1606.04164 · HTML: https://mlsanity.com/paper/1606.04164/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2016.

## Built on by (most cited first, 30 of 46)

- [Two-Stream Network for Sign Language Recognition and Translation](https://mlsanity.com/paper/2211.01367/) (2022)
- [Neural Machine Translation for Low-Resource Languages: A Survey](https://mlsanity.com/paper/2106.15115/) (2021)
- [Building Machine Translation Systems for the Next Thousand Languages](https://mlsanity.com/paper/2205.03983/) (2022)
- [Task Aligned Generative Meta-learning for Zero-shot Learning](https://mlsanity.com/paper/2103.02185/) (2021)
- [Zero-Shot Cross-lingual Semantic Parsing](https://mlsanity.com/paper/2104.07554/) (2021)
- [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)
- [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)
- [Alternative Input Signals Ease Transfer in Multilingual Machine Translation](https://mlsanity.com/paper/2110.07804/) (2021)
- [MMTAfrica: Multilingual Machine Translation for African Languages](https://mlsanity.com/paper/2204.04306/) (2022)
- [Multilingual Translation via Grafting Pre-trained Language Models](https://mlsanity.com/paper/2109.05256/) (2021)
- [UM4: Unified Multilingual Multiple Teacher-Student Model for Zero-Resource Neural Machine Translation](https://mlsanity.com/paper/2207.04900/) (2022)
- [Revealing the Parallel Multilingual Learning within Large Language Models](https://mlsanity.com/paper/2403.09073/) (2024)
- [Bridging the Linguistic Divide: A Survey on Leveraging Large Language Models for Machine Translation](https://mlsanity.com/paper/2504.01919/) (2025)
- [Low-Resource Neural Machine Translation for Southern African Languages](https://mlsanity.com/paper/2104.00366/) (2021)
- [Multilingual Mix: Example Interpolation Improves Multilingual Neural Machine Translation](https://mlsanity.com/paper/2203.07627/) (2022)
- [Simultaneous Multi-Pivot Neural Machine Translation](https://mlsanity.com/paper/2104.07410/) (2021)
- [Modality-Balanced Embedding for Video Retrieval](https://mlsanity.com/paper/2204.08182/) (2022)
- [Investigating Multi-Pivot Ensembling with Massively Multilingual Machine Translation Models](https://mlsanity.com/paper/2311.07439/) (2023)
- [AUGVIC: Exploiting BiText Vicinity for Low-Resource NMT](https://mlsanity.com/paper/2106.05141/) (2021)
- [Twist Decoding: Diverse Generators Guide Each Other](https://mlsanity.com/paper/2205.09273/) (2022)
- [Building Multilingual Machine Translation Systems That Serve Arbitrary X-Y Translations](https://mlsanity.com/paper/2206.14982/) (2022)
- [ParCourE: A Parallel Corpus Explorer for a Massively Multilingual Corpus](https://mlsanity.com/paper/2107.06632/) (2021)
- [Languages Transferred Within the Encoder: On Representation Transfer in Zero-Shot Multilingual Translation](https://mlsanity.com/paper/2406.08092/) (2024)
- [CharED: Character-wise Ensemble Decoding for Large Language Models](https://mlsanity.com/paper/2407.11009/) (2024)
- [Revisiting Language Encoding in Learning Multilingual Representations](https://mlsanity.com/paper/2102.08357/) (2021)
- [Robustness of Multi-Source MT to Transcription Errors](https://mlsanity.com/paper/2305.16894/) (2023)
- [ZRIGF: An Innovative Multimodal Framework for Zero-Resource Image-Grounded Dialogue Generation](https://mlsanity.com/paper/2308.00400/) (2023)
- [Unlikelihood Tuning on Negative Samples Amazingly Improves Zero-Shot Translation](https://mlsanity.com/paper/2309.16599/) (2023)
- [How To Build Competitive Multi-gender Speech Translation Models For Controlling Speaker Gender Translation](https://mlsanity.com/paper/2310.15114/) (2023)

## Abstract (arXiv)

In this paper, we propose a novel finetuning algorithm for the recently introduced multi-way, mulitlingual neural machine translate that enables zero-resource machine translation. When used together with novel many-to-one translation strategies, we empirically show that this finetuning algorithm allows the multi-way, multilingual model to translate a zero-resource language pair (1) as well as a single-pair neural translation model trained with up to 1M direct parallel sentences of the same language pair and (2) better than pivot-based translation strategy, while keeping only one additional copy of attention-related parameters.