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State-of-the-art NLP inference uses enormous neural architectures and modelstrained for GPU-months, well beyond the reach of most consumers of NLP. Thishas led to one-size-fits-all public API-based NLP service models by major AIcompanies, serving large numbers of clients. Neither (hardware deficient)clients nor (heavily subscribed) servers can afford traditional fine tuning.Many clients own little or no labeled data. We initiate a study of adaptationof centralized NLP services to clients, and present one practical andlightweight approach. Each client uses an unsupervised, corpus-based sketch toregister to the service. The server uses an auxiliary network to map the sketchto an abstract vector representation, which then informs the main labelingnetwork. When a new client registers with its sketch, it gets immediateaccuracy benefits. We demonstrate the success of the proposed architectureusing sentiment labeling, NER, and predictive language modeling

NLP Service APIs and Models for Efficient Registration of New Clients