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Grounded Adaptation for Zero-shot Executable Semantic Parsing
We propose Grounded Adaptation for Zero-shot Executable Semantic Parsing...
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Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing
Research on parsing language to SQL has largely ignored the structure of...
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SmBoP: Semi-autoregressive Bottom-up Semantic Parsing
The de-facto standard decoding method for semantic parsing in recent yea...
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Decoupling Structure and Lexicon for Zero-Shot Semantic Parsing
Building a semantic parser quickly in a new domain is a fundamental chal...
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A Tale of Two Linkings: Dynamically Gating between Schema Linking and Structural Linking for Text-to-SQL Parsing
In Text-to-SQL semantic parsing, selecting the correct entities (tables ...
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Model-based Interactive Semantic Parsing: A Unified Framework and A Text-to-SQL Case Study
As a promising paradigm, interactive semantic parsing has shown to impro...
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AutoQA: From Databases To QA Semantic Parsers With Only Synthetic Training Data
We propose AutoQA, a methodology and toolkit to generate semantic parser...
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Global Reasoning over Database Structures for Text-to-SQL Parsing
State-of-the-art semantic parsers rely on auto-regressive decoding, emitting one symbol at a time. When tested against complex databases that are unobserved at training time (zero-shot), the parser often struggles to select the correct set of database constants in the new database, due to the local nature of decoding. In this work, we propose a semantic parser that globally reasons about the structure of the output query to make a more contextually-informed selection of database constants. We use message-passing through a graph neural network to softly select a subset of database constants for the output query, conditioned on the question. Moreover, we train a model to rank queries based on the global alignment of database constants to question words. We apply our techniques to the current state-of-the-art model for Spider, a zero-shot semantic parsing dataset with complex databases, increasing accuracy from 39.4 to 47.4
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