Effect of Cultural Misunderstanding Warning in MT-Mediated Communication

Pituxcoosuvarn, Mondheera; Murakami, Yohei; Lin, Donghui; Ishida, Toru

doi:10.1007/978-3-030-58157-2_8

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…In light of this, some research has begun to explore how to surface more detailed information about system behavior to end-users. Pituxcoosuvarn et al (2020) developed a system to warn MT users if a term could cause cross-cultural misunderstanding. The warnings weakly influenced awareness of translation sensitivity.…”

Section: Methods For Calibrating Trustmentioning

confidence: 99%

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Opportunities for Human-centered Evaluation of Machine Translation Systems

Liebling¹,

Heller²,

Robertson³

et al. 2022

Findings of the Association for Computational Linguistics: NAACL 2022

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Machine translation models are embedded in larger user-facing systems. Although model evaluation has matured, evaluation at the systems level is still lacking. We review literature from both the translation studies and HCI communities about who uses machine translation and for what purposes. We emphasize an important difference in evaluating machine translation models versus the physical and cultural systems in which they are embedded. We then propose opportunities for improved measurement of user-facing translation systems. We pay particular attention to the need for design and evaluation to aid engendering trust and enhancing user agency in future machine translation systems.

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Section: Methods For Calibrating Trustmentioning

confidence: 99%

“…To elicit spontaneous speech, some established protocols exist. Pituxcoosuvarn et al (2020) developed a MT-mediated chat app with a desert-island survival task (Lafferty, 1974). In this task, participants must choose and discuss items that they would desire should they be stranded on a desert island.…”

Section: Task Designmentioning

confidence: 99%