# Unmet Needs and Opportunities for Mobile Translation AI

arXiv: https://arxiv.org/abs/2002.12387 · HTML: https://mlsanity.com/paper/2002.12387/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2020.

## Built on by (most cited first, 14 of 14)

- [Gender Bias in Machine Translation](https://mlsanity.com/paper/2104.06001/) (2021)
- [Angler: Helping Machine Translation Practitioners Prioritize Model Improvements](https://mlsanity.com/paper/2304.05967/) (2023)
- [Translation in the Hands of Many:Centering Lay Users in Machine Translation Interactions](https://mlsanity.com/paper/2502.13780/) (2025)
- [An Interdisciplinary Approach to Human-Centered Machine Translation](https://mlsanity.com/paper/2506.13468/) (2025)
- [Towards cross-language prosody transfer for dialog](https://mlsanity.com/paper/2307.04123/) (2023)
- [Should I Share this Translation? Evaluating Quality Feedback for User Reliance on Machine Translation](https://mlsanity.com/paper/2505.24683/) (2025)
- [Spatial Speech Translation: Translating Across Space With Binaural Hearables](https://mlsanity.com/paper/2504.18715/) (2025)
- [Beyond General Purpose Machine Translation: The Need for Context-specific Empirical Research to Design for Appropriate User Trust](https://mlsanity.com/paper/2205.06920/) (2022)
- [Sociotechnical Effects of Machine Translation](https://mlsanity.com/paper/2503.20959/) (2025)
- [Toward Machine Translation Literacy: How Lay Users Perceive and Rely on Imperfect Translations](https://mlsanity.com/paper/2510.09994/) (2025)
- [A Conditional Companion: Lived Experiences of People with Mental Health Disorders Using LLMs](https://mlsanity.com/paper/2602.00402/) (2026)
- [Learnings from Technological Interventions in a Low Resource Language: Enhancing Information Access in Gondi](https://mlsanity.com/paper/2211.16172/) (2022)
- [Toward Machine Interpreting: Lessons from Human Interpreting Studies](https://mlsanity.com/paper/2508.07964/) (2025)
- [Measuring User's Mental Models of Speech Translation in Human-AI Collaboration](https://mlsanity.com/paper/2606.24644/) (2026)

## Abstract (arXiv)

Translation apps and devices are often presented in the context of providing assistance while traveling abroad. However, the spectrum of needs for cross-language communication is much wider. To investigate these needs, we conducted three studies with populations spanning socioeconomic status and geographic regions: (1) United States-based travelers, (2) migrant workers in India, and (3) immigrant populations in the United States. We compare frequent travelers' perception and actual translation needs with those of the two migrant communities. The latter two, with low language proficiency, have the greatest translation needs to navigate their daily lives. However, current mobile translation apps do not meet these needs. Our findings provide new insights on the usage practices and limitations of mobile translation tools. Finally, we propose design implications to help apps better serve these unmet needs.