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Training for Deployment: Methods for Small and Efficient NLP

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If you have a question about this talk, please contact Marinela Parovic.

Natural language models for translation and classification work relatively well, or at least well enough that there is demand for widespread use in real systems. Models developed for research however do not naturally translate to deployment scenarios, particularly on resource constrained devices like mobile phones. In this talk I will discuss two axes that make it difficult to deploy NLP models in practice: a) Serial generation in translation models makes them difficult to optimize, and b) Fine-tuned parameter size in classification makes models difficult to deploy to end-users. I propose two approaches that aim to circumvent these issues, and discuss some practical work on deploying large NLP models on edge devices.

This talk is part of the Language Technology Lab Seminars series.

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