The author discusses the localization challenge of whether to translate technical English terms into the target audience language or leave them as is. Stakeholders are divided, but the decision must center on the target audience's English proficiency and their perception of English use.
As machine-generated text becomes harder to distinguish from human writing and translation, the author proposes a shift in focus: from how the content was produced to its substance and level of quality.
Organizations must ensure that their events are accessible in real time to people of every language and ability. AI-powered speech translation and captioning make this kind of audience inclusion possible at scale, making the old excuses redundant.
We live in an era when linguists are losing their former monopoly on translation to machines. And yet, if we understand this change correctly, it can also bring benefits — especially for translators of literary texts.
The author presents five principles for ethically translating literature from marginalized groups, including balancing fidelity and accessibility, noting political context, and treating contested episodes with care.
At the 63rd Annual Meeting of the Association for Computational Linguistics, Welocalize and Duke University unveiled LangMark, a multilingual dataset designed to advance research in automatic post-editing of neural machine translation.
A student-led multilingual translation project in Japan adapts a Hiroshima survivor’s kamishibai into more than 100 languages using machine translation plus human review.
By augmenting and accelerating the literary translation process, artificial intelligence is making more stories than ever before accessible to readers around the world.
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