# Ensembling Factored Neural Machine Translation Models for Automatic Post-Editing and Quality Estimation

arXiv: https://arxiv.org/abs/1706.05083 · HTML: https://mlsanity.com/paper/1706.05083/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2017.

## Built on by (most cited first, 5 of 5)

- [Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Survey](https://mlsanity.com/paper/2104.06951/) (2021)
- [From Handcrafted Features to LLMs: A Brief Survey for Machine Translation Quality Estimation](https://mlsanity.com/paper/2403.14118/) (2024)
- [BitextEdit: Automatic Bitext Editing for Improved Low-Resource Machine Translation](https://mlsanity.com/paper/2111.06787/) (2021)
- [Bilingual Synchronization: Restoring Translational Relationships with Editing Operations](https://mlsanity.com/paper/2210.13163/) (2022)
- [Together We Can: Multilingual Automatic Post-Editing for Low-Resource Languages](https://mlsanity.com/paper/2410.17973/) (2024)

## Abstract (arXiv)

This work presents a novel approach to Automatic Post-Editing (APE) and Word-Level Quality Estimation (QE) using ensembles of specialized Neural Machine Translation (NMT) systems. Word-level features that have proven effective for QE are included as input factors, expanding the representation of the original source and the machine translation hypothesis, which are used to generate an automatically post-edited hypothesis. We train a suite of NMT models that use different input representations, but share the same output space. These models are then ensembled together, and tuned for both the APE and the QE task. We thus attempt to connect the state-of-the-art approaches to APE and QE within a single framework. Our models achieve state-of-the-art results in both tasks, with the only difference in the tuning step which learns weights for each component of the ensemble.