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Semantic Parsing.md

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more refer:

acl2018-semantic-parsing-tutorial
https://github.com/allenai/acl2018-semantic-parsing-tutorial

NLP progress
https://github.com/sebastianruder/NLP-progress/blob/master/english/semantic_parsing.md

Semantic Parsing

CCG Semantic Parsing

  • Complex discrete learning algorithms

  • But, grammars hopefully generalize to unseen data well!

  • Difficult to engineer: few people can do it and it takes a lot of time

Neural Semantic Parsing

  • Treat meaning as a string...
  • Apply NMT
  • Close to SOTA performance!!!
  • Much easier to build (with toolkits)

Constrained Decoding

Token-based Decoding

The output space is tokens, but they are constrained to be relevant at each time step.

paper

Dong and Lapata. 2016. Language to Logical Form with Neural Attention. In ACL. Goldman, Latcinnik, Naveh, Globerson and Berant. 2018. Weakly-supervised Semantic Parsing with Abstract Examples. In ACL.

Grammar-based Decoding

The output space is production rules, and a grammar defines the constraints.

paper

Xiao, Dymetman, and Gardent. 2016. Sequence-based Structured Prediction for Semantic Parsing. In ACL. Krishnamurthy, Dasigi, and Gardner. 2017. Neural Semantic Parsing with Type Constraints for Semi-Structured Tables. In EMNLP.

Coarse-to-Fine Decoding
paper
  • Coarse-to-Fine Decoding for Neural Semantic Parsing, Li Dong, Mirella Lapata, ACL 2018 arxiv | code

Tools