# Beyond Accuracy: Community Perspectives on Machine Translation

arXiv: https://arxiv.org/abs/2606.09655 · HTML: https://mlsanity.com/paper/2606.09655/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 40 references that are in the map.
- First posted on arXiv 8 Jun 2026.

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

- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Large language models effectively leverage document-level context for literary translation, but critical errors persist](https://mlsanity.com/paper/2304.03245/) (2023)
- [ChatGPT: A Meta-Analysis after 2.5 Months](https://mlsanity.com/paper/2302.13795/) (2023)
- [(Perhaps) Beyond Human Translation: Harnessing Multi-Agent Collaboration for Translating Ultra-Long Literary Texts](https://mlsanity.com/paper/2405.11804/) (2024)
- [How Good Are LLMs for Literary Translation, Really? Literary Translation Evaluation with Humans and LLMs](https://mlsanity.com/paper/2410.18697/) (2024)
- [What Do Dialect Speakers Want? A Survey of Attitudes Towards Language Technology for German Dialects](https://mlsanity.com/paper/2402.11968/) (2024)
- [LiTransProQA: an LLM-based Literary Translation evaluation metric with Professional Question Answering](https://mlsanity.com/paper/2505.05423/) (2025)
- [Analyzing COVID-19 Tweets with Transformer-based Language Models](https://mlsanity.com/paper/2104.10259/) (2021)
- [Incremental Adaptation of NMT for Professional Post-editors: A User Study](https://mlsanity.com/paper/1906.08996/) (2019)
- ["Be My Cheese?": Cultural Nuance Benchmarking for Machine Translation in Multilingual LLMs](https://mlsanity.com/paper/2602.04729/) (2026)
- [Beyond Reproduction: A Paired-Task Framework for Assessing LLM Comprehension and Creativity in Literary Translation](https://mlsanity.com/paper/2604.18169/) (2026)

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

- [Last Translation Benchmark](https://mlsanity.com/paper/2609.04173/) (2026)

## Abstract (arXiv)

Despite remarkable progress in machine translation (MT), non-AI communities have raised growing concerns about MT systems, suggesting a noticeable gap between technical advancement and the needs of real-world users. For instance, while NLP researchers focus on benchmark performance, end users care about ethical concerns, trust, reliability, costs, and more. We argue that listening to various user communities is essential so that research efforts would be directed towards the problems that the communities care about. To this end, we present a large-scale analysis, for the first time, that investigates what four stakeholder communities (AI developers, professional translators, language learners, and language service providers) post about MT technology on social media. To do so, we construct a dataset of 79,286 posts and comments from Reddit, Facebook, Bluesky, and Mastodon from 2019 to 2025, and analyse where these communities disagree, and how and why. Overall, we find that communities often disagree, and even show strong conflicts due to polarised sentiments on topics such as translation quality, efficiency, and reliability. This is because these communities approach these topics differently: the AI community frames them as technical and computational problems, while non-AI (user) communities care more about quality nuances, time savings, user trust, and broader social issues.