# How Far Can 100 Samples Go? Unlocking Overall Zero-Shot Multilingual Translation via Tiny Multi-Parallel Data

arXiv: https://arxiv.org/abs/2401.12413 · HTML: https://mlsanity.com/paper/2401.12413/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 30 references that are in the map.
- First posted on arXiv 22 Jan 2024.

## Builds on (most cited first, 19 of 19)

- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [COMET: A Neural Framework for MT Evaluation](https://mlsanity.com/paper/2009.09025/) (2020)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [Improving Neural Machine Translation Models with Monolingual Data](https://mlsanity.com/paper/1511.06709/) (2015)
- [Beyond English-Centric Multilingual Machine Translation](https://mlsanity.com/paper/2010.11125/) (2020)
- [The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation](https://mlsanity.com/paper/2106.03193/) (2021)
- [Word Translation Without Parallel Data](https://mlsanity.com/paper/1710.04087/) (2017)
- [Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation](https://mlsanity.com/paper/1611.04558/) (2016)
- [Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation](https://mlsanity.com/paper/2004.11867/) (2020)
- [Contrastive Learning for Many-to-many Multilingual Neural Machine Translation](https://mlsanity.com/paper/2105.09501/) (2021)
- [The Missing Ingredient in Zero-Shot Neural Machine Translation](https://mlsanity.com/paper/1903.07091/) (2019)
- [Mitigating Hallucinations and Off-target Machine Translation with Source-Contrastive and Language-Contrastive Decoding](https://mlsanity.com/paper/2309.07098/) (2023)
- [Improving Zero-Shot Translation by Disentangling Positional Information](https://mlsanity.com/paper/2012.15127/) (2020)
- [Improving Multilingual Translation by Representation and Gradient Regularization](https://mlsanity.com/paper/2109.04778/) (2021)
- [Language Tags Matter for Zero-Shot Neural Machine Translation](https://mlsanity.com/paper/2106.07930/) (2021)
- [Towards a Better Understanding of Variations in Zero-Shot Neural Machine Translation Performance](https://mlsanity.com/paper/2310.10385/) (2023)
- [Beyond Shared Vocabulary: Increasing Representational Word Similarities across Languages for Multilingual Machine Translation](https://mlsanity.com/paper/2305.14189/) (2023)
- [Exploring the Impact of Layer Normalization for Zero-shot Neural Machine Translation](https://mlsanity.com/paper/2305.09312/) (2023)

## Abstract (arXiv)

Zero-shot translation aims to translate between language pairs not seen during training in Multilingual Machine Translation (MMT) and is largely considered an open problem. A common, albeit resource-consuming, solution is to add as many related translation directions as possible to the training corpus. In this paper, we show that for an English-centric model, surprisingly large zero-shot improvements can be achieved by simply fine-tuning with a very small amount of multi-parallel data. For example, on the EC30 dataset, we obtain up to +21.7 ChrF non-English overall improvements (870 directions) by using only 100 multi-parallel samples while preserving English-centric translation quality. When investigating the size effect of fine-tuning data and its transfer capabilities, we found that already a small, randomly sampled set of fine-tuning directions is sufficient to achieve comparable improvements. The resulting non-English performance is close to the complete translation upper bound. Even in a minimal setting -- fine-tuning with only one single sample -- the well-known off-target issue is almost completely resolved, explaining parts -- but not all -- of the observed improvements in translation quality.