Semantic Parsing
380 papers with code • 20 benchmarks • 42 datasets
Semantic Parsing is the task of transducing natural language utterances into formal meaning representations. The target meaning representations can be defined according to a wide variety of formalisms. This include linguistically-motivated semantic representations that are designed to capture the meaning of any sentence such as λ-calculus or the abstract meaning representations. Alternatively, for more task-driven approaches to Semantic Parsing, it is common for meaning representations to represent executable programs such as SQL queries, robotic commands, smart phone instructions, and even general-purpose programming languages like Python and Java.
Source: Tranx: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation
Benchmarks
These leaderboards are used to track progress in Semantic Parsing
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Libraries
Use these libraries to find Semantic Parsing models and implementationsDatasets
FrameNet
ATIS
WikiSQL
SCAN
Spider-Realistic
WikiTableQuestions
Occluded REID
ComplexWebQuestions
Subtasks
Most implemented papers
Compositional Semantic Parsing on Semi-Structured Tables
Two important aspects of semantic parsing for question answering are the breadth of the knowledge source and the depth of logical compositionality.
Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing
crazydonkey200/neural-symbolic-machines
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NeurIPS 2018
We present Memory Augmented Policy Optimization (MAPO), a simple and novel way to leverage a memory buffer of promising trajectories to reduce the variance of policy gradient estimate.
TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation
We present TRANX, a transition-based neural semantic parser that maps natural language (NL) utterances into formal meaning representations (MRs).
SParC: Cross-Domain Semantic Parsing in Context
ryanzhumich/editsql
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ACL 2019
The best model obtains an exact match accuracy of 20. 2% over all questions and less than10% over all interaction sequences, indicating that the cross-domain setting and the con-textual phenomena of the dataset present significant challenges for future research.
Content Enhanced BERT-based Text-to-SQL Generation
We present a simple methods to leverage the table content for the BERT-based model to solve the text-to-SQL problem.
RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers
Microsoft/rat-sql
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ACL 2020
The generalization challenge lies in (a) encoding the database relations in an accessible way for the semantic parser, and (b) modeling alignment between database columns and their mentions in a given query.
Break It Down: A Question Understanding Benchmark
Understanding natural language questions entails the ability to break down a question into the requisite steps for computing its answer.
Cityscapes-Panoptic-Parts and PASCAL-Panoptic-Parts datasets for Scene Understanding
In this technical report, we present two novel datasets for image scene understanding.
Learning Language Games through Interaction
We introduce a new language learning setting relevant to building adaptive natural language interfaces.
From Language to Programs: Bridging Reinforcement Learning and Maximum Marginal Likelihood
kelvinguu/lang2program
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ACL 2017
Our goal is to learn a semantic parser that maps natural language utterances into executable programs when only indirect supervision is available: examples are labeled with the correct execution result, but not the program itself.
