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        <title>An efficient method for Natural Language Querying on Structured Data</title>
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        <namePart type="given">Hanoz</namePart>
        <namePart type="family">Bhathena</namePart>
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    <name type="personal">
        <namePart type="given">Aviral</namePart>
        <namePart type="family">Joshi</namePart>
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    <name type="personal">
        <namePart type="given">Prateek</namePart>
        <namePart type="family">Singh</namePart>
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            <title>Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track)</title>
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            <namePart type="given">Sunayana</namePart>
            <namePart type="family">Sitaram</namePart>
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        <name type="personal">
            <namePart type="given">Beata</namePart>
            <namePart type="family">Beigman Klebanov</namePart>
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    <abstract>We present an efficient and reliable approach to Natural Language Querying (NLQ) on databases (DB) which is not based on text-to-SQL type semantic parsing. Our approach simplifies the NLQ on structured data problem to the following “bread and butter” NLP tasks: (a) Domain classification, for choosing which DB table to query, whether the question is out-of-scope (b) Multi-head slot/entity extraction (SE) to extract the field criteria and other attributes such as its role (filter, sort etc) from the raw text and (c) Slot value disambiguation (SVD) to resolve/normalize raw spans from SE to format suitable to query a DB. This is a general purpose, DB language agnostic approach and the output can be used to query any DB and return results to the user. Also each of these tasks is extremely well studied, mature, easier to collect data for and enables better error analysis by tracing problems to specific components when something goes wrong.</abstract>
    <identifier type="citekey">bhathena-etal-2023-efficient</identifier>
    <identifier type="doi">10.18653/v1/2023.acl-industry.31</identifier>
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        <date>2023-07</date>
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            <start>322</start>
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%0 Conference Proceedings
%T An efficient method for Natural Language Querying on Structured Data
%A Bhathena, Hanoz
%A Joshi, Aviral
%A Singh, Prateek
%Y Sitaram, Sunayana
%Y Beigman Klebanov, Beata
%Y Williams, Jason D.
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F bhathena-etal-2023-efficient
%X We present an efficient and reliable approach to Natural Language Querying (NLQ) on databases (DB) which is not based on text-to-SQL type semantic parsing. Our approach simplifies the NLQ on structured data problem to the following “bread and butter” NLP tasks: (a) Domain classification, for choosing which DB table to query, whether the question is out-of-scope (b) Multi-head slot/entity extraction (SE) to extract the field criteria and other attributes such as its role (filter, sort etc) from the raw text and (c) Slot value disambiguation (SVD) to resolve/normalize raw spans from SE to format suitable to query a DB. This is a general purpose, DB language agnostic approach and the output can be used to query any DB and return results to the user. Also each of these tasks is extremely well studied, mature, easier to collect data for and enables better error analysis by tracing problems to specific components when something goes wrong.
%R 10.18653/v1/2023.acl-industry.31
%U https://aclanthology.org/2023.acl-industry.31
%U https://doi.org/10.18653/v1/2023.acl-industry.31
%P 322-331

Markdown (Informal)

[An efficient method for Natural Language Querying on Structured Data](https://aclanthology.org/2023.acl-industry.31) (Bhathena et al., ACL 2023)

• An efficient method for Natural Language Querying on Structured Data (Bhathena et al., ACL 2023)

ACL

• Hanoz Bhathena, Aviral Joshi, and Prateek Singh. 2023. An efficient method for Natural Language Querying on Structured Data. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track), pages 322–331, Toronto, Canada. Association for Computational Linguistics.