A cross-lingual QA framework shows users build stronger mental models of MT systems through practice and source language knowledge mainly by spotting surface-level errors, with transcriptions helping further.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CL 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Machine interpreting should shift from fidelity metrics to three design priorities—agency, grounding, and experience—drawn from interpreting studies to close the usability gap with human-mediated communication.
citing papers explorer
-
Measuring User's Mental Models of Speech Translation in Human-AI Collaboration
A cross-lingual QA framework shows users build stronger mental models of MT systems through practice and source language knowledge mainly by spotting surface-level errors, with transcriptions helping further.
-
Bridging the Usability Gap: Lessons from Interpreting Studies for Machine Interpreting Design
Machine interpreting should shift from fidelity metrics to three design priorities—agency, grounding, and experience—drawn from interpreting studies to close the usability gap with human-mediated communication.