  As a human choosing a supervised learning algorithm, it is natural to begin
by reading a text description of the dataset and documentation for the
algorithms you might use. We demonstrate that the same idea improves the
performance of automated machine learning methods. We use language embeddings
from modern NLP to improve state-of-the-art AutoML systems by augmenting their
recommendations with vector embeddings of datasets and of algorithms. We use
these embeddings in a neural architecture to learn the distance between
best-performing pipelines. The resulting (meta-)AutoML framework improves on
the performance of existing AutoML frameworks. Our zero-shot AutoML system
using dataset metadata embeddings provides good solutions instantaneously,
running in under one second of computation. Performance is competitive with
AutoML systems OBOE, AutoSklearn, AlphaD3M, and TPOT when each framework is
allocated a minute of computation. We make our data, models, and code publicly
available.
