Case Two / language technology
This case study looks at machine translation — specifically Google Translate — and a distinction that runs through the whole project: getting the words right is not the same as getting the meaning across. and singlish
Machine translation can be accurate and still remove the personal, relational layer of language — tone, register, dialect, hesitation, idiom, rapport. People who used Google Translate described exactly this: the words were right, but it did not feel personal or connective.
The loss is structural: machine translation removes the personal layer by nature of what it cannot reliably carry. Lexical accuracy (the correct words) and communicative adequacy (the meaning actually landing) are different things.
That personal layer is not decoration. In an asylum hearing, it is the very thing a person is judged on — credibility is assessed on how someone speaks as much as what they say. So a translation that strips the personal layer strips the basis on which a person is believed.
This is why machine translation is inadequate for high-stakes settings, even when it is accurate: it removes the subtleties used to judge a person. And it is why human interpreting — which can carry that layer — must be done properly, because where it fails (wrong dialect, "good enough" standards), the same loss occurs.
Singlish particles like lah, leh, and meh carry stance, relation, emphasis, and social meaning. Correction systems mark them as errors. Meaning disappears quietly.