# Addressing word-order Divergence in Multilingual Neural Machine Translation for extremely Low Resource Languages

arXiv: https://arxiv.org/abs/1811.00383 · HTML: https://mlsanity.com/paper/1811.00383/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2018.

## 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)
- [Survey of Low-Resource Machine Translation](https://mlsanity.com/paper/2109.00486/) (2021)
- [IndicMT Eval: A Dataset to Meta-Evaluate Machine Translation metrics for Indian Languages](https://mlsanity.com/paper/2212.10180/) (2022)
- [A Survey on Low-Resource Neural Machine Translation](https://mlsanity.com/paper/2107.04239/) (2021)
- [Low-resource neural machine translation with morphological modeling](https://mlsanity.com/paper/2404.02392/) (2024)
- [Improving Cross-Lingual Transfer through Subtree-Aware Word Reordering](https://mlsanity.com/paper/2310.13583/) (2023)
- [Disentangling the Roles of Target-Side Transfer and Regularization in Multilingual Machine Translation](https://mlsanity.com/paper/2402.01772/) (2024)
- [Linguistically-driven Multi-task Pre-training for Low-resource Neural Machine Translation](https://mlsanity.com/paper/2201.08070/) (2022)
- [Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages](https://mlsanity.com/paper/2112.09866/) (2021)
- [Denoising-based UNMT is more robust to word-order divergence than MASS-based UNMT](https://mlsanity.com/paper/2303.01191/) (2023)
- [Exploring NLP Benchmarks in an Extremely Low-Resource Setting](https://mlsanity.com/paper/2509.03962/) (2025)

## Abstract (arXiv)

Transfer learning approaches for Neural Machine Translation (NMT) train a NMT model on the assisting-target language pair (parent model) which is later fine-tuned for the source-target language pair of interest (child model), with the target language being the same. In many cases, the assisting language has a different word order from the source language. We show that divergent word order adversely limits the benefits from transfer learning when little to no parallel corpus between the source and target language is available. To bridge this divergence, We propose to pre-order the assisting language sentence to match the word order of the source language and train the parent model. Our experiments on many language pairs show that bridging the word order gap leads to significant improvement in the translation quality.