Natural language interfaces often require supervised data to translate user
requests into programs, database queries, or other structured intent
representations. During data collection, it can be difficult to anticipate and
formalize the full range of user needs -- for example, in a system designed to
handle simple requests (like $\textit{find my meetings tomorrow}$ or
$\textit{move my meeting with my manager to noon})$, users may also express
more elaborate requests (like $\textit{swap all my calls on Monday and
Tuesday}$). We introduce an approach for equipping a simple language-to-code
model to handle complex utterances via a process of hierarchical natural
language decomposition. Our approach uses a pre-trained language model to
decompose a complex utterance into a sequence of smaller natural language
steps, then interprets each step using the language-to-code model. 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.

该研究介绍了一种通过分层自然语言分解处理复杂话语的方法来装备简单的语言代码模型，以实现自然语言接口的功能，实现了对几乎没有复杂训练数据的复杂话语的解释，并优于标准的少量提示方法。