# Effective Strategies in Zero-Shot Neural Machine Translation

arXiv: https://arxiv.org/abs/1711.07893 · HTML: https://mlsanity.com/paper/1711.07893/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Nov 2017.

## Built on by (most cited first, 6 of 6)

- [Neural Machine Translation for Low-Resource Languages: A Survey](https://mlsanity.com/paper/2106.15115/) (2021)
- [Contrastive Learning for Many-to-many Multilingual Neural Machine Translation](https://mlsanity.com/paper/2105.09501/) (2021)
- [Low-Resource Neural Machine Translation for Southern African Languages](https://mlsanity.com/paper/2104.00366/) (2021)
- [Zero-shot Speech Translation](https://mlsanity.com/paper/2107.06010/) (2021)
- [Accurate Knowledge Distillation with n-best Reranking](https://mlsanity.com/paper/2305.12057/) (2023)
- [Unsupervised Transfer Learning in Multilingual Neural Machine Translation with Cross-Lingual Word Embeddings](https://mlsanity.com/paper/2103.06689/) (2021)

## Abstract (arXiv)

In this paper, we proposed two strategies which can be applied to a multilingual neural machine translation system in order to better tackle zero-shot scenarios despite not having any parallel corpus. The experiments show that they are effective in terms of both performance and computing resources, especially in multilingual translation of unbalanced data in real zero-resourced condition when they alleviate the language bias problem.