  Designing natural language interfaces has historically required collecting
supervised data to translate user requests into carefully designed intent
representations. This requires enumerating and labeling a long tail of user
requests, which is challenging. At the same time, large language models (LLMs)
encode knowledge about goals and plans that can help conversational assistants
interpret user requests requiring numerous steps to complete. We introduce an
approach to handle complex-intent-bearing utterances from a user via a process
of hierarchical natural language decomposition and interpretation. Our approach
uses a pre-trained language model to decompose a complex utterance into a
sequence of simpler natural language steps and interprets each step using the
language-to-program model designed for the interface. To test our approach, we
collect and release DeCU -- a new NL-to-program benchmark to evaluate
Decomposition of Complex Utterances. Experiments show that the proposed
approach enables the interpretation of complex utterances with almost no
complex training data, while outperforming standard few-shot prompting
approaches.
