In recent years, natural language understanding (NLU) has increasingly found business value, fueled by model improvements as well as the scalability and cost-efficiency of cloud-based infrastructure. Specifically, the Transformer deep learning architecture, often implemented in the form of BERT models, has been highly successful, but training, fine-tuning, and optimizing these models has proven to be a challenging problem. Thanks to the AWS and Hugging Face collaboration, it’s now simpler to train and optimize NLU models on Amazon SageMaker using the SageMaker Python SDK, but sourcing labeled data for these models is still difficult and time-consuming.
One NLU problem of particular business interest is the task of question answering. In this post, we demonstrate how to build a custom question answering dataset using Amazon SageMaker Ground Truth to train a Hugging Face question answering NLU model.
Question answering challenges
Question answering entails a model automatically producing an answer to a query

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