Papers with post-editing
IntelliCAT: Intelligent Machine Translation Post-Editing with Quality Estimation and Translation Suggestion (2021.acl-demo)
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| Challenge: | Existing computer-aided translation tools require the translator to edit incorrect parts of a document, while ITP tools require fewer edits. |
| Approach: | They propose an interactive translation interface with neural models that streamline the post-editing process on machine translation output. |
| Outcome: | The proposed interface can significantly improve translation quality and a user study shows that it speeds up the post-editing process by 52.9% compared to translating from scratch. |
Improving Cross-Domain Low-Resource Text Generation through LLM Post-Editing: A Programmer-Interpreter Approach (2024.findings-eacl)
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| Challenge: | Large pre-trained language models such as GPT-3.5 and GPT-4 have gained significant attention in natural language research due to limited computational resources or inaccessible parameters. |
| Approach: | They propose a neural programmer-interpreter approach that preserves the domain generalization ability of LLMs while editing their output. |
| Outcome: | The proposed framework significantly improves GPT-3.5’s performance in logical form-to-text conversion and low-resource machine translation, surpassing other state-of-the-art (SOTA) LLM post-editing methods in cross-domain settings. |
An Exploration of Post-Editing Effectiveness in Text Summarization (2022.naacl-main)
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Vivian Lai, Alison Smith-Renner, Ke Zhang, Ruijia Cheng, Wenjuan Zhang, Joel Tetreault, Alejandro Jaimes-Larrarte
| Challenge: | Automated summarization methods are efficient but can suffer from low quality. |
| Approach: | They conducted an experiment with 72 participants to compare post-editing provided summaries with manual summarization for summary quality, human efficiency, and user experience. |
| Outcome: | The results show that post-editing improves summary quality, human efficiency, and user experience on formal (XSum news) and informal (Reddit posts) text. |
MMPE: A Multi-Modal Interface using Handwriting, Touch Reordering, and Speech Commands for Post-Editing Machine Translation (2020.acl-demos)
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Nico Herbig, Santanu Pal, Tim Düwel, Kalliopi Meladaki, Mahsa Monshizadeh, Vladislav Hnatovskiy, Antonio Krüger, Josef van Genabith
| Challenge: | a shift from traditional translation to post-editing (PE) of machine-translated text can save time and reduce errors, but it also affects the design of translation interfaces. |
| Approach: | They propose a prototype that combines traditional input modes with pen, touch, and speech modalities for post-editing of machine-translated (MT) they propose to use these modalités to cross out or hand-write new text, drag and drop words for reordering, or use spoken commands to update the text in place. |
| Outcome: | The proposed interfaces can be used to cross out or hand-write new text, drag and drop words for reordering, or use spoken commands to update the text in place. |
Global Optimization under Length Constraint for Neural Text Summarization (P19-1)
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| Challenge: | GOLC increases the probabilities of generating summaries that have high evaluation scores within a desired length. |
| Approach: | They propose a global optimization method under length constraint for neural text summarization models. |
| Outcome: | The proposed method generates fewer overlength summaries while maintaining the fastest processing speed. |
Test-Time Scaling of Reasoning Models for Machine Translation (2026.eacl-long)
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| Challenge: | Using TTS, Reasoning Models (RMs) are able to perform tasks such as math and coding with limited results. |
| Approach: | They evaluate 12 Reasoning Models across a diverse suite of MT benchmarks, examining three scenarios: direct translation, forced-reasoning extrapolation, and post-editing. |
| Outcome: | The proposed approach improves translation quality on three domains, with inconsistent results for general-purpose RMs and performance plateauing. |
MMPE: A Multi-Modal Interface for Post-Editing Machine Translation (2020.acl-main)
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Nico Herbig, Tim Düwel, Santanu Pal, Kalliopi Meladaki, Mahsa Monshizadeh, Antonio Krüger, Josef van Genabith
| Challenge: | Current advances in machine translation (MT) increase the need for translators to switch from traditional translation to post-editing (PE) of machine-translated text. |
| Approach: | They propose to combine traditional input modes with pen, touch, and speech modalities for post-editing of machine-translated text. |
| Outcome: | The proposed interfaces are designed to reduce errors and save time. |
Computer Assisted Translation with Neural Quality Estimation and Automatic Post-Editing (2020.findings-emnlp)
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| Challenge: | Using neural machine translation to approximate human parity is difficult due to the lack of parallel training corpora. |
| Approach: | They propose an end-to-end deep learning framework for quality estimation and automatic post-editing of machine translation output. |
| Outcome: | The proposed framework achieves state-of-the-art performance on the English–German dataset and human translators can significantly expedite their post-editing processing with the model. |
What Does LLM Refinement Actually Improve? A Systematic Study on Document-Level Literary Translation (2026.acl-long)
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Shaomu Tan, Dawei Zhu, Ke Tran, Michael Denkowski, Sony Trenous, Leonardo F. R. Ribeiro, Bill Byrne, Felix Hieber
| Challenge: | Large language models (LLMs) have made document-level machine translation increasingly practical, enabled by long-context modeling and strong generation quality. |
| Approach: | They propose to use document-level MT followed by segment-level refinement to find the strongest and most stable improvements across six LLMs and seven language pairs. |
| Outcome: | The proposed method outperforms error-specific prompting and evaluate-then-refine schemes in document-level translation. |
Automatic Post-Editing of Machine Translation: A Neural Programmer-Interpreter Approach (D18-1)
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| Challenge: | Existing approaches to inducing APE have suffered from over-correction, where the APE system tends to keep the machine translated text without any modification. |
| Approach: | They propose a neural programmer-interpreter approach to automated post-editing (APE) that mimics human perform post- editing using discrete edit operations . their model outperforms previous neural models for inducing PE programs on the WMT17 APE task for German-English up to +1 BLEU score and -0.7 TER scores. |
| Outcome: | The proposed model outperforms previous neural models for inducing PE programs on the WMT17 APE task for German-English up to +1 BLEU score and -0.7 TER scores. |
WeTS: A Benchmark for Translation Suggestion (2022.emnlp-main)
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| Challenge: | Existing studies focus on overall performance of machine translation but ignore TS performance, authors say . if TS is applied into post-editing, it will reduce the time and cost of post-production. |
| Approach: | They propose to use a golden corpus annotated by experts to generate a translation suggestion model. |
| Outcome: | The proposed model improves on the golden corpus annotated by translators on four translation directions. |
Mid-Air Hand Gestures for Post-Editing of Machine Translation (2021.acl-long)
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| Challenge: | In a well-connected world, translation is of everincreasing importance. |
| Approach: | They propose to use mid-air hand gestures in combination with the keyboard for editing in machine translation and post-editing workflows to improve quality. |
| Outcome: | The proposed prototype supports mid-air hand gestures for cursor placement, text selection, deletion, and reordering. |
DivEMT: Neural Machine Translation Post-Editing Effort Across Typologically Diverse Languages (2022.emnlp-main)
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| Challenge: | Recent advances in neural language modeling and multilingual training have prompted widespread adoption of machine translation (MT) technologies across an unprecedented range of world languages. |
| Approach: | They propose to use a dataset to assess the impact of two state-of-the-art NMT systems, Google Translate and the multilingual mBART-50 model, on translation productivity. |
| Outcome: | The proposed model is faster than translation from scratch, but the magnitude of productivity gains varies widely across systems and languages. |
Automatic Input Rewriting Improves Translation with Large Language Models (2025.naacl-long)
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| Challenge: | LLMs can rewrite inputs but in machine translation, they are primarily used to re-write outputs via post-editing. |
| Approach: | They propose to use LLMs to rewrite inputs automatically to improve machine translation (MT) they propose to simplify inputs and use quality estimation to assess translatability. |
| Outcome: | The proposed methods can be improved by using quality estimation to assess translatability. |
Evaluating Automatic Subtitling: Correlating Post-editing Effort and Automatic Metrics (2024.lrec-main)
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| Challenge: | Existing metrics for automatic subtitling are not yet fully explored. |
| Approach: | They propose to use machine translation metrics to measure post-editing effort in automatic subtitling to collect data on product-, process- and participant-based data. |
| Outcome: | The proposed metrics correlate with measures of post-editing effort in automatic subtitling. |
MMTE: Corpus and Metrics for Evaluating Machine Translation Quality of Metaphorical Language (2024.emnlp-main)
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| Challenge: | Existing evaluation methods focus on fluency and factual reliability, while neglecting figurative quality. |
| Approach: | They propose a set of human evaluation metrics focused on the translation of figurative language and a parallel metaphor corpus generated by post-editing. |
| Outcome: | The proposed evaluation protocol estimates four aspects of MT: Metaphorical Equivalence, Emotion, Authenticity, and Quality. |
Correcting Diverse Factual Errors in Abstractive Summarization via Post-Editing and Language Model Infilling (2022.emnlp-main)
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| Challenge: | Abstractive summarization models often generate inconsistent summaries containing factual errors or fabricated content. |
| Approach: | They propose to generate representative examples of non-factual summaries through infilling language models and train a robust fact-correction model to post-edit them to improve factual consistency. |
| Outcome: | The proposed model outperforms previous methods in correcting factual errors on two popular summarization datasets. |
Exploring the Potential of Multimodal LLM with Knowledge-Intensive Multimodal ASR (2024.findings-emnlp)
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| Challenge: | Recent advances in multimodal large language models have made significant progress in integrating information across various modalities, yet real-world applications in educational and scientific domains remain challenging. |
| Approach: | They propose a task that focuses on transcribing scientific conference videos by leveraging visual information from slides to enhance the accuracy of technical terminologies. |
| Outcome: | The proposed framework improves transcript quality through post-editing and improves performance over speech-only baselines. |
Investigating the Helpfulness of Word-Level Quality Estimation for Post-Editing Machine Translation Output (2021.emnlp-main)
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| Challenge: | Post-editing (PE) machine translation (MT) output can save time and reduce errors. |
| Approach: | They propose to use automatic word-level quality estimation to predict correctness of MT output to flag problematic output. |
| Outcome: | The proposed model is not good enough to support human translations, but is based on a visualization reflecting uncertainty of the model. |
PEGRL: Improving Machine Translation by Post-Editing Guided Reinforcement Learning (2026.findings-acl)
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| Challenge: | Reinforcement learning (RL) has shown strong promise for LLM-based machine translation . however, translation-oriented RL remains challenged by high-variance policy gradients induced by Monte Carlo baselines and large trajectory space that favors global exploration over fine-grained local optimization. |
| Approach: | They propose a two-stage RL framework that uses post-editing as an auxiliary task to stabilize training and guide overall optimization. |
| Outcome: | The proposed framework supports global exploration and fine-grained optimization while supporting global exploration. |
Unsupervised Word-level Quality Estimation for Machine Translation Through the Lens of Annotators (Dis)agreement (2025.emnlp-main)
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| Challenge: | Modern WQE techniques rely on expensive inference with large language models or ad-hoc training with large amounts of human-labeled data. |
| Approach: | They propose to use word-level quality estimation to identify translation errors from the inner workings of translation models to quantify the impact of human label variation on metric performance. |
| Outcome: | The proposed methods identify translation errors from the inner workings of translation models using human labels. |