Papers with structure-aware model

2 papers
Discourse Representation Structure Parsing (P18-1)

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Challenge: Existing semantic parsers are data-driven using annotated examples consisting of utterances and their meaning representations.
Approach: They propose a method which transforms Discourse Representation Structures (DRSs) to trees and develop a structure-aware model which decomposes the decoding process into three stages.
Outcome: The proposed model outperforms baseline models on the Groningen Meaning Bank (GMB) by a wide margin.
Analysis of Tree-Structured Architectures for Code Generation (2021.findings-acl)

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Challenge: Code generation is the task of generating code snippets from input user specifications written in natural language (NL).
Approach: They evaluate the significance of input parse trees for code generation by using constituency-based parsers as input and an abstract syntax tree as the target.
Outcome: The proposed models on a Python-based code generation dataset and a semantic parsing dataset show that constituency trees encoded using a structure-aware model improve performance.

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