Getting to Production with Few-shot Natural Language Generation Models

In this paper, we study the utilization of pretrained language models to enable few-shot Natural Language Generation (NLG) in task-oriented dialog systems. We introduce a system consisting of iterative self-training and an extensible mini-template framework that textualizes the structured input data into seminatural text to fully take advantage of pretrained language models. We compare various representations of NLG models' input and output and show that transforming the input and output to be similar to what the language model has seen before during pretraining improves the model's few-shot performance substantially. We show that neural models can be trained with as few as 300 annotated examples while providing high fidelity, considerably lowering the resource requirements for standing up a new domain or language. This level of data efficiency removes the need for crowd-sourced data collection resulting in higher quality data annotated by expert linguists. In addition, model maintenance and debugging processes will improve in this few-shot setting. Finally, we explore distillation and using a caching system to satisfy latency requirements of real-world systems.