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- W3105379530 abstract "State-of-the-art NLP inference uses enormous neural architectures and models trained for GPU-months, well beyond the reach of most consumers of NLP. This has led to one-size-fits-all public API-based NLP service models by major AI companies, serving millions of clients. They cannot afford traditional fine tuning for individual clients. Many clients cannot even afford significant fine tuning, and own little or no labeled data. Recognizing that word usage and salience diversity across clients leads to reduced accuracy, we initiate a study of practical and lightweight adaptation of centralized NLP services to clients. Each client uses an unsupervised, corpus-based sketch to register to the service. The server modifies its network mildly to accommodate client sketches, and occasionally trains the augmented network over existing clients. When a new client registers with its sketch, it gets immediate accuracy benefits. We demonstrate the proposed architecture using sentiment labeling, NER, and predictive language modeling." @default.
- W3105379530 created "2020-11-23" @default.
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- W3105379530 date "2020-01-01" @default.
- W3105379530 modified "2023-09-26" @default.
- W3105379530 title "NLP Service APIs and Models for Efficient Registration of New Clients" @default.
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- W3105379530 doi "https://doi.org/10.18653/v1/2020.findings-emnlp.357" @default.
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