2025
EMNLP
EMNLP 2025
An Interdisciplinary Approach to Human-Centered Machine Translation
Abstract
AbstractMachine Translation (MT) tools are widely used today, often in contexts where professional translators are not present. Despite progress in MT technology, a gap persists between system development and real-world usage, particularly for non-expert users who may struggle to assess translation reliability.This paper advocates for a human-centered approach to MT, emphasizing the alignment of system design with diverse communicative goals and contexts of use. We survey the literature in Translation Studies and Human-Computer Interaction to recontextualize MT evaluation and design to address the diverse real-world scenarios in which MT is used today.
👥
Mega-Team
— 20 authors
🌉
Interdisciplinary Bridge
— Artificial Intelligence and Interdisciplinary and Natural Language Processing
🐝
Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Marine Carpuat
,
Omri Asscher
,
Kalika Bali
,
Luisa Bentivogli
,
Frédéric Blain
,
Lynne Bowker
,
Monojit Choudhury
,
Hal Daume III
,
Kevin Duh
,
Ge Gao
,
Alvin Grissom II
,
Marzena Karpinska
,
Elaine C. Khoong
,
William D. Lewis
,
André F. T. Martins
,
Mary Nurminen
,
Douglas W. Oard
,
Maja Popović
,
Michel Simard
,
François Yvon