Natural Language Decomposition and Interpretation of Complex Utterances
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.
6462f11f2538819c72a0d665
Harsh Jhamtani
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2024-02-16T08:37:08.062Z
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Harsh Jhamtani
user
hjhamtani
6462f11f2538819c72a0d666
Hao Fang
claimed_verified
2023-05-17T08:04:27.312Z
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/avatars/4bd393ecdf092f9598144530e9bad25c.svg
Hao
user
haofang
6462f11f2538819c72a0d667
Patrick Xia
6462f11f2538819c72a0d668
Eran Levy
6462f11f2538819c72a0d669
Jacob Andreas
6462f11f2538819c72a0d66a
Ben Van Durme
Natural Language Decomposition and Interpretation of Complex Utterances
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.
An approach using hierarchical natural language decomposition and a pre-trained language model allows a language-to-code model to handle complex utterances with minimal training data, outperforming standard few-shot prompting.
language-to-code model hierarchical natural language decomposition pre-trained language model NL-to-program benchmark Decomposition of Complex Utterances few-shot prompting