# Beyond General Purpose Machine Translation: The Need for Context-specific Empirical Research to Design for Appropriate User Trust

arXiv: https://arxiv.org/abs/2205.06920 · HTML: https://mlsanity.com/paper/2205.06920/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 64 references that are in the map.
- First posted on arXiv 13 May 2022.

## Builds on (most cited first, 15 of 15)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [Social Bias Frames: Reasoning about Social and Power Implications of Language](https://mlsanity.com/paper/1911.03891/) (2019)
- [Improving fairness in machine learning systems: What do industry practitioners need?](https://mlsanity.com/paper/1812.05239/) (2018)
- [Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims](https://mlsanity.com/paper/2004.07213/) (2020)
- [Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needs](https://mlsanity.com/paper/2101.09824/) (2021)
- [Assessing the Fairness of AI Systems: AI Practitioners' Processes, Challenges, and Needs for Support](https://mlsanity.com/paper/2112.05675/) (2021)
- [Unremarkable AI: Fitting Intelligent Decision Support into Critical, Clinical Decision-Making Processes](https://mlsanity.com/paper/1904.09612/) (2019)
- [Fluency Over Adequacy: A Pilot Study in Measuring User Trust in Imperfect MT](https://mlsanity.com/paper/1802.06041/) (2018)
- [Unmet Needs and Opportunities for Mobile Translation AI](https://mlsanity.com/paper/2002.12387/) (2020)
- [Backtranslation Feedback Improves User Confidence in MT, Not Quality](https://mlsanity.com/paper/2104.05688/) (2021)
- [Empowering Local Communities Using Artificial Intelligence](https://mlsanity.com/paper/2110.02007/) (2021)
- [Context-Aware Learning for Neural Machine Translation](https://mlsanity.com/paper/1903.04715/) (2019)

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

- [Translation in the Hands of Many:Centering Lay Users in Machine Translation Interactions](https://mlsanity.com/paper/2502.13780/) (2025)

## Abstract (arXiv)

Machine Translation (MT) has the potential to help people overcome language barriers and is widely used in high-stakes scenarios, such as in hospitals. However, in order to use MT reliably and safely, users need to understand when to trust MT outputs and how to assess the quality of often imperfect translation results. In this paper, we discuss research directions to support users to calibrate trust in MT systems. We share findings from an empirical study in which we conducted semi-structured interviews with 20 clinicians to understand how they communicate with patients across language barriers, and if and how they use MT systems. Based on our findings, we advocate for empirical research on how MT systems are used in practice as an important first step to addressing the challenges in building appropriate trust between users and MT tools.