title: Natural Language Decomposition and Interpretation of Complex Utterances
summary: 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 find my meetings tomorrow or
  move my meeting with my manager to noon), users may also express
  more elaborate requests (like 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.
paper_authors._id: 6462f11f2538819c72a0d665
paper_authors.name: Harsh Jhamtani
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-02-16T08:37:08.062Z
paper_authors._id: 650c92c76b94d837c01a7bcb
paper_authors.avatarUrl: /avatars/f618180951083af62522f7fb137d645a.svg
paper_authors.fullname: Jhamtani
paper_authors.type: user
paper_authors.user: randompro19
paper_authors._id: 6462f11f2538819c72a0d666
paper_authors.name: Hao Fang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-05-17T08:04:27.312Z
paper_authors._id: 6402882dfa1acad60068143e
paper_authors.avatarUrl: /avatars/4bd393ecdf092f9598144530e9bad25c.svg
paper_authors.fullname: Hao
paper_authors.type: user
paper_authors.user: haofang
paper_authors._id: 6462f11f2538819c72a0d667
paper_authors.name: Patrick Xia
paper_authors._id: 6462f11f2538819c72a0d668
paper_authors.name: Eran Levy
paper_authors._id: 6462f11f2538819c72a0d669
paper_authors.name: Jacob Andreas
paper_authors._id: 6462f11f2538819c72a0d66a
paper_authors.name: Ben Van Durme
paper_title: Natural Language Decomposition and Interpretation of Complex Utterances
paper_summary: 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 find my meetings tomorrow or
  move my meeting with my manager to noon), users may also express
  more elaborate requests (like 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.
paper_ai_keywords: pre-trained language model language-to-code model hierarchical natural language decomposition Decomposition of Complex Utterances (DeCU) benchmark few-shot prompting approaches