Sentence-level Semantics

At the other end of the spectrum, in the traditional semanticparsing literature, the problem is framedas predicting compositional semantic representations. These are mainly geared towards question-answering rather than task completion, and are usually directly executed against a knowledge base.Some of the standard datasets in this area include GeoQuery [24] and WebQuestions [2].Within both of these areas, neural approaches have supplanted previous feature-engineering basedapproaches in recent years [10, 14]. In the context of tree-structured semantic parsing, some otherinteresting approaches include Seq2Tree [7] which modifiesthe standard Seq2Seq decoder to betteroutput trees; SCANNER [5, 4] which extends the RNNG formulation specifically for semantic pars-ing such that the output is no longer coupled with the input; and TRANX [23] and Abstract SyntaxNetwork [19] which generate code along a programming language schema. For graph-structured se-mantic parsing [1, 11], SLING [21] produces graph-structured parses by modeling semantic parsingas a neural transition parsing problem with a more expressive transition tag set. While graph struc-tures can provide more detailed semantics, they are more difficult to parse and can be an overkill forunderstanding task oriented utterances. Improving Semantic Parsing for Task Oriented Dialog

• Tasks

  • Semantic Parsing
    • Code Generation
      • NL2SQL
      • NL2Python
        • Seq2Seq based
        • Retrieval-based
          • Retrieval-Based Neural Code Generation
        • CNN based
          • A Grammar-Based Structural CNN Decoder for Code Generation AAAI 19'
        • Transition-based
          • TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation ACL 18'
      • NL2Java
      • Python2Java

• Models

  • TRANX (TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation) ACL 18'

Dataset and Benchmark

HeartStone

• Description

• Download

  Github - Wang Ling Latent Predictor Networks for Code Generation ACL 16'

• Benchmark

  • Zeyu Sun A Grammar-Based Structural CNN Decoder for Code Generation AAAI 19'
    • Accuracy: 30.3%
    • BLEU: 79.6%

Django

• Description

• Download

  Github - Yusuke Oda Learning to Generate Pseudo-Code from Source Code Using Statistical Machine Translation ASE 15'

• Benchmark

  • Pengcheng Yin TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation) ACL 18'
    • Accuracy: 73.7%

WikiSQL

• Description

• Download

  Github - Victor Zhong Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning ArXiv 18'

• Benchmark

ATIS

• Description

• Download

• Benchmark

JOBS

• Description

• Download

• Benchmark

GEO

• Description

• Download

• Benchmark