# Target Conditioned Sampling: Optimizing Data Selection for Multilingual Neural Machine Translation

arXiv: https://arxiv.org/abs/1905.08212 · HTML: https://mlsanity.com/paper/1905.08212/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2019.

## Built on by (most cited first, 11 of 11)

- [Neural Machine Translation for Low-Resource Languages: A Survey](https://mlsanity.com/paper/2106.15115/) (2021)
- [Uncertainty-Aware Balancing for Multilingual and Multi-Domain Neural Machine Translation Training](https://mlsanity.com/paper/2109.02284/) (2021)
- [Meta Back-translation](https://mlsanity.com/paper/2102.07847/) (2021)
- [A Survey on Low-Resource Neural Machine Translation](https://mlsanity.com/paper/2107.04239/) (2021)
- [Towards a Better Understanding of Variations in Zero-Shot Neural Machine Translation Performance](https://mlsanity.com/paper/2310.10385/) (2023)
- [Competence-based Curriculum Learning for Multilingual Machine Translation](https://mlsanity.com/paper/2109.04002/) (2021)
- [Beyond Triplet: Leveraging the Most Data for Multimodal Machine Translation](https://mlsanity.com/paper/2212.10313/) (2022)
- [Secoco: Self-Correcting Encoding for Neural Machine Translation](https://mlsanity.com/paper/2108.12137/) (2021)
- [Rethinking what Matters: Effective and Robust Multilingual Realignment for Low-Resource Languages](https://mlsanity.com/paper/2511.06497/) (2025)
- [Cross-Lingual Transfer for Machine Translation in Turkic Languages](https://mlsanity.com/paper/2607.29355/) (2026)
- [Online Language Adaptive Sampling for Better Distributed Cross-lingual Gains](https://mlsanity.com/paper/2609.14969/) (2026)

## Abstract (arXiv)

To improve low-resource Neural Machine Translation (NMT) with multilingual corpora, training on the most related high-resource language only is often more effective than using all data available (Neubig and Hu, 2018). However, it is possible that an intelligent data selection strategy can further improve low-resource NMT with data from other auxiliary languages. In this paper, we seek to construct a sampling distribution over all multilingual data, so that it minimizes the training loss of the low-resource language. Based on this formulation, we propose an efficient algorithm, Target Conditioned Sampling (TCS), which first samples a target sentence, and then conditionally samples its source sentence. Experiments show that TCS brings significant gains of up to 2 BLEU on three of four languages we test, with minimal training overhead.