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Mention Extraction and Linking for SQL Query Generation

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arxiv 2012.10074 v1 pith:5EKVCAJJ submitted 2020-12-18 cs.CL cs.AI

classification cs.CLcs.AI
keywords approachbenchmarksystemswikisqlachievesannotationsappearingautomatically
verification ladder T0 review T1 audit T2 compute T3 formal
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On the WikiSQL benchmark, state-of-the-art text-to-SQL systems typically take a slot-filling approach by building several dedicated models for each type of slots. Such modularized systems are not only complex butalso of limited capacity for capturing inter-dependencies among SQL clauses. To solve these problems, this paper proposes a novel extraction-linking approach, where a unified extractor recognizes all types of slot mentions appearing in the question sentence before a linker maps the recognized columns to the table schema to generate executable SQL queries. Trained with automatically generated annotations, the proposed method achieves the first place on the WikiSQL benchmark.

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  1. Needle: A Generative AI-Powered Multi-modal Database for Answering Complex Natural Language Queries

    cs.IR 2024-12 conditional novelty 6.0 of 10

    Needle generates AI-made query images from text, embeds them with an ensemble of visual models, and uses nearest-neighbor search to retrieve matching real images, beating zero-shot text-image baselines on complex queries.

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