# Correct Me If You Can: Learning from Error Corrections and Markings

arXiv: https://arxiv.org/abs/2004.11222 · HTML: https://mlsanity.com/paper/2004.11222/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2020.

## Built on by (most cited first, 11 of 11)

- [Error Span Annotation: A Balanced Approach for Human Evaluation of Machine Translation](https://mlsanity.com/paper/2406.11580/) (2024)
- [Consistency is Key: Disentangling Label Variation in Natural Language Processing with Intra-Annotator Agreement](https://mlsanity.com/paper/2301.10684/) (2023)
- [A Critical Look At Tokenwise Reward-Guided Text Generation](https://mlsanity.com/paper/2406.07780/) (2024)
- [Fine-Grained Reward Optimization for Machine Translation using Error Severity Mappings](https://mlsanity.com/paper/2411.05986/) (2024)
- [MT-RewardTree: A Comprehensive Framework for Advancing LLM-Based Machine Translation via Reward Modeling](https://mlsanity.com/paper/2503.12123/) (2025)
- [Modeling User Preferences with Automatic Metrics: Creating a High-Quality Preference Dataset for Machine Translation](https://mlsanity.com/paper/2410.07779/) (2024)
- [How to Evaluate a Summarizer: Study Design and Statistical Analysis for Manual Linguistic Quality Evaluation](https://mlsanity.com/paper/2101.11298/) (2021)
- [Could AI Democratise Education? Socio-Technical Imaginaries of an EdTech Revolution](https://mlsanity.com/paper/2112.02034/) (2021)
- [Enhancing Supervised Learning with Contrastive Markings in Neural Machine Translation Training](https://mlsanity.com/paper/2307.08416/) (2023)
- [Prompting Large Language Models with Human Error Markings for Self-Correcting Machine Translation](https://mlsanity.com/paper/2406.02267/) (2024)
- [LQM: Linguistically Motivated Multidimensional Quality Metrics for Machine Translation](https://mlsanity.com/paper/2604.18490/) (2026)

## Abstract (arXiv)

Sequence-to-sequence learning involves a trade-off between signal strength and annotation cost of training data. For example, machine translation data range from costly expert-generated translations that enable supervised learning, to weak quality-judgment feedback that facilitate reinforcement learning. We present the first user study on annotation cost and machine learnability for the less popular annotation mode of error markings. We show that error markings for translations of TED talks from English to German allow precise credit assignment while requiring significantly less human effort than correcting/post-editing, and that error-marked data can be used successfully to fine-tune neural machine translation models.