Beyond Accuracy: Community Perspectives on Machine Translation
- URL: http://arxiv.org/abs/2606.09655v1
- Date: Mon, 08 Jun 2026 15:42:49 GMT
- Title: Beyond Accuracy: Community Perspectives on Machine Translation
- Abstract summary: Non-AI communities have raised growing concerns about machine translation (MT) systems.<n>This suggests a noticeable gap between technical advancement and the needs of real-world users.<n>We present a large-scale analysis, for the first time, that investigates what four stakeholder communities post about MT technology on social media.
- Score: 34.87848886025228
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: 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.
Related papers
- An Interdisciplinary Approach to Human-Centered Machine Translation [67.70453480427132]
Machine Translation (MT) tools are widely used today, often in contexts where professional translators are not present.<n>Despite progress in MT technology, a gap persists between system development and real-world usage.<n>This paper advocates for a human-centered approach to MT, emphasizing the alignment of system design with diverse communicative goals.
arXiv Detail & Related papers (2025-06-16T13:27:44Z) - The AI Imperative: Scaling High-Quality Peer Review in Machine Learning [49.87236114682497]
We argue that AI-assisted peer review must become an urgent research and infrastructure priority.<n>We propose specific roles for AI in enhancing factual verification, guiding reviewer performance, assisting authors in quality improvement, and supporting ACs in decision-making.
arXiv Detail & Related papers (2025-06-09T18:37:14Z) - Community Moderation and the New Epistemology of Fact Checking on Social Media [124.26693978503339]
Social media platforms have traditionally relied on independent fact-checking organizations to identify and flag misleading content.<n>X (formerly Twitter) and Meta have shifted towards community-driven content moderation by launching their own versions of crowd-sourced fact-checking.<n>We examine the current approaches to misinformation detection across major platforms, explore the emerging role of community-driven moderation, and critically evaluate both the promises and challenges of crowd-checking at scale.
arXiv Detail & Related papers (2025-05-26T14:50:18Z) - Translation in the Hands of Many:Centering Lay Users in Machine Translation Interactions [20.620669039109245]
Machine Translation (MT) has become a global tool.<n>Widespread accessibility has extended MT's reach to a vast base of lay users.<n>We identify three factors -- usability, trust, and literacy -- that are central to shaping user interactions.
arXiv Detail & Related papers (2025-02-19T14:45:17Z) - AI in Support of Diversity and Inclusion [5.415339913320849]
We look at the challenges and progress in making large language models (LLMs) more transparent, inclusive, and aware of social biases.<n>We highlight AI's role in identifying biased content in media, which is important for improving representation.<n>We stress AI systems need diverse and inclusive training data.
arXiv Detail & Related papers (2025-01-16T13:36:24Z) - A Capabilities Approach to Studying Bias and Harm in Language Technologies [4.135516576952934]
We consider fairness, bias, and inclusion in Language Technologies through the lens of the Capabilities Approach.
The Capabilities Approach centers on what people are capable of achieving, given their intersectional social, political, and economic contexts.
We detail the Capabilities Approach, its relationship to multilingual and multicultural evaluation, and how the framework affords meaningful collaboration with community members in defining and measuring the harms of Language Technologies.
arXiv Detail & Related papers (2024-11-06T22:46:13Z) - The Call for Socially Aware Language Technologies [94.6762219597438]
We argue that many of these issues share a common core: a lack of awareness of the factors, context, and implications of the social environment in which NLP operates.<n>We argue that substantial challenges remain for NLP to develop social awareness and that we are just at the beginning of a new era for the field.
arXiv Detail & Related papers (2024-05-03T18:12:39Z) - Matching Social Issues to Technologies for Civic Tech by Association
Rule Mining using Weighted Casual Confidence [1.1470070927586016]
More than 80 civic tech communities in Japan are developing information technology (IT) systems to solve their regional issues.
Our objective is to realize a civic tech matchmaking system to assist such communities in finding better partners with IT experience in their issues.
arXiv Detail & Related papers (2021-12-17T11:18:09Z) - Empowering Local Communities Using Artificial Intelligence [70.17085406202368]
It has become an important topic to explore the impact of AI on society from a people-centered perspective.
Previous works in citizen science have identified methods of using AI to engage the public in research.
This article discusses the challenges of applying AI in Community Citizen Science.
arXiv Detail & Related papers (2021-10-05T12:51:11Z) - LioNets: A Neural-Specific Local Interpretation Technique Exploiting
Penultimate Layer Information [6.570220157893279]
Interpretable machine learning (IML) is an urgent topic of research.
This paper focuses on a local-based, neural-specific interpretation process applied to textual and time-series data.
arXiv Detail & Related papers (2021-04-13T09:39:33Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.