# Log-linear Combinations of Monolingual and Bilingual Neural Machine Translation Models for Automatic Post-Editing

arXiv: https://arxiv.org/abs/1605.04800 · HTML: https://mlsanity.com/paper/1605.04800/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2016.

## Built on by (most cited first, 13 of 13)

- [Guiding Large Language Models to Post-Edit Machine Translation with Error Annotations](https://mlsanity.com/paper/2404.07851/) (2024)
- [Exploiting Biased Models to De-bias Text: A Gender-Fair Rewriting Model](https://mlsanity.com/paper/2305.11140/) (2023)
- [DirectQE: Direct Pretraining for Machine Translation Quality Estimation](https://mlsanity.com/paper/2105.07149/) (2021)
- [Netmarble AI Center's WMT21 Automatic Post-Editing Shared Task Submission](https://mlsanity.com/paper/2109.06515/) (2021)
- [RoundTripOCR: A Data Generation Technique for Enhancing Post-OCR Error Correction in Low-Resource Devanagari Languages](https://mlsanity.com/paper/2412.15248/) (2024)
- [Giving the Old a Fresh Spin: Quality Estimation-Assisted Constrained Decoding for Automatic Post-Editing](https://mlsanity.com/paper/2501.17265/) (2025)
- [Transfer Learning for Sequence Generation: from Single-source to Multi-source](https://mlsanity.com/paper/2105.14809/) (2021)
- [Bilingual Synchronization: Restoring Translational Relationships with Editing Operations](https://mlsanity.com/paper/2210.13163/) (2022)
- [Improving Language Model Integration for Neural Machine Translation](https://mlsanity.com/paper/2306.05077/) (2023)
- [Advancing Semi-Supervised Learning for Automatic Post-Editing: Data-Synthesis by Mask-Infilling with Erroneous Terms](https://mlsanity.com/paper/2204.03896/) (2022)
- [Automatic Correction of Human Translations](https://mlsanity.com/paper/2206.08593/) (2022)
- [An Empirical Study of Automatic Post-Editing](https://mlsanity.com/paper/2209.07759/) (2022)
- [LangMark: A Multilingual Dataset for Automatic Post-Editing](https://mlsanity.com/paper/2511.17153/) (2025)

## Abstract (arXiv)

This paper describes the submission of the AMU (Adam Mickiewicz University) team to the Automatic Post-Editing (APE) task of WMT 2016. We explore the application of neural translation models to the APE problem and achieve good results by treating different models as components in a log-linear model, allowing for multiple inputs (the MT-output and the source) that are decoded to the same target language (post-edited translations). A simple string-matching penalty integrated within the log-linear model is used to control for higher faithfulness with regard to the raw machine translation output. To overcome the problem of too little training data, we generate large amounts of artificial data. Our submission improves over the uncorrected baseline on the unseen test set by -3.2% TER and +5.5% BLEU and outperforms any other system submitted to the shared-task by a large margin.