I jumped straight into n-gram models and character frequency distributions, which felt right, but I didn't think about scale until the interviewer nudged me.
Start by clarifying requirements such as the number of languages, input length, latency, and accuracy targets. Then propose a hybrid approach using character n-gram models for short texts and neural models for longer texts, and discuss trade-offs between accuracy, latency, and resource usage.
Pro tip: Emphasize the importance of handling code-switching and mixed-language texts, and suggest a confidence threshold to fall back to a more expensive model or human review when uncertain.
Ask about the number of languages, typical text length, latency constraints, and required accuracy. This shapes the choice of model and infrastructure.
Suggest a simple, fast method like character n-gram profiles with cosine similarity or a Naive Bayes classifier as a baseline for short texts.
For higher accuracy, propose a neural approach such as a CNN/RNN over character embeddings or a fine-tuned transformer model like XLM-R.
Discuss serving infrastructure, model size, latency, and how to handle code-switching. Consider a cascade where a fast model handles easy cases and a complex model handles ambiguous ones.
Define metrics (accuracy, F1, latency), plan for continuous evaluation, and mention handling of low-resource languages and domain adaptation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.