# Together We Can: Multilingual Automatic Post-Editing for Low-Resource Languages

arXiv: https://arxiv.org/abs/2410.17973 · HTML: https://mlsanity.com/paper/2410.17973/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 38 references that are in the map.
- First posted on arXiv 23 Oct 2024.

## Builds on (most cited first, 11 of 11)

- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation](https://mlsanity.com/paper/1611.04558/) (2016)
- [Massively Multilingual Neural Machine Translation](https://mlsanity.com/paper/1903.00089/) (2019)
- [Multi-Task Learning as a Bargaining Game](https://mlsanity.com/paper/2202.01017/) (2022)
- [IndicTrans2: Towards High-Quality and Accessible Machine Translation Models for all 22 Scheduled Indian Languages](https://mlsanity.com/paper/2305.16307/) (2023)
- [eSCAPE: a Large-scale Synthetic Corpus for Automatic Post-Editing](https://mlsanity.com/paper/1803.07274/) (2018)
- [Unbabel's Submission to the WMT2019 APE Shared Task: BERT-based Encoder-Decoder for Automatic Post-Editing](https://mlsanity.com/paper/1905.13068/) (2019)
- [Ensembling Factored Neural Machine Translation Models for Automatic Post-Editing and Quality Estimation](https://mlsanity.com/paper/1706.05083/) (2017)
- [Netmarble AI Center's WMT21 Automatic Post-Editing Shared Task Submission](https://mlsanity.com/paper/2109.06515/) (2021)
- [Harnessing Cross-lingual Features to Improve Cognate Detection for Low-resource Languages](https://mlsanity.com/paper/2112.08789/) (2021)
- [Challenge Dataset of Cognates and False Friend Pairs from Indian Languages](https://mlsanity.com/paper/2112.09526/) (2021)

## Built on by (most cited first, 1 of 1)

- [Confident but Wrong: A Constrained Decoding Diagnostic for Low-Resource Automatic Post-Editing](https://mlsanity.com/paper/2609.29680/) (2026)

## Abstract (arXiv)

This exploratory study investigates the potential of multilingual Automatic Post-Editing (APE) systems to enhance the quality of machine translations for low-resource Indo-Aryan languages. Focusing on two closely related language pairs, English-Marathi and English-Hindi, we exploit the linguistic similarities to develop a robust multilingual APE model. To facilitate cross-linguistic transfer, we generate synthetic Hindi-Marathi and Marathi-Hindi APE triplets. Additionally, we incorporate a Quality Estimation (QE)-APE multi-task learning framework. While the experimental results underline the complementary nature of APE and QE, we also observe that QE-APE multitask learning facilitates effective domain adaptation. Our experiments demonstrate that the multilingual APE models outperform their corresponding English-Hindi and English-Marathi single-pair models by 2.5 and 2.39 TER points, respectively, with further notable improvements over the multilingual APE model observed through multi-task learning (+1.29 and +1.44 TER points), data augmentation (+0.53 and +0.45 TER points) and domain adaptation (+0.35 and +0.45 TER points). We release the synthetic data, code, and models accrued during this study publicly at https://github.com/cfiltnlp/Multilingual-APE.