mlsanityData to 7 Oct 2026

Semantic Operator Prediction and Applications

arXiv 2301.00399 ↗First posted 1 Jan 2023cs.CLcited by 1

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Abstract (arXiv)

In the present paper, semantic parsing challenges are briefly introduced and QDMR formalism in semantic parsing is implemented using sequence to sequence model with attention but uses only part of speech(POS) as a representation of words of a sentence to make the training as simple and as fast as possible and also avoiding curse of dimensionality as well as overfitting. It is shown how semantic operator prediction could be augmented with other models like the CopyNet model or the recursive neural net model.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 21 of its 28 references that are in the map. First posted on arXiv 1 Jan 2023.

Builds on

21 of 28 references.

References · 21

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  2. 2016Incorporating Copying Mechanism in Sequence-to-Sequence Learningcited by 217
  3. 2019PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Textcited by 182
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  12. 2020Span-based Semantic Parsing for Compositional Generalizationcited by 77
9 more
  1. 2021TransferNet: An Effective and Transparent Framework for Multi-hop Question Answering over Relation Graphcited by 60
  2. 2020Leveraging Abstract Meaning Representation for Knowledge Base Question Answeringcited by 53
  3. 2018An Interpretable Reasoning Network for Multi-Relation Question Answeringcited by 47
  4. 2020Compositional Generalization via Semantic Taggingcited by 32
  5. 2022Knowledge Base Question Answering: A Semantic Parsing Perspectivecited by 22
  6. 2021Bottom-Up Constituency Parsing and Nested Named Entity Recognition with Pointer Networkscited by 21
  7. 2022Modeling Multi-hop Question Answering as Single Sequence Predictioncited by 21
  8. 2021A Two-Stage Approach towards Generalization in Knowledge Base Question Answeringcited by 15
  9. 2017Towards Universal Semantic Taggingcited by 12

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Citing papers per year: 2023: 1.

1 paper.

  1. 2023Semantic Tagging with LSTM-CRFcited by 0