Semantic Operator Prediction and Applications

TL;DR

This paper introduces a simplified sequence-to-sequence approach for semantic parsing using POS tags, demonstrating how semantic operator prediction can be enhanced with models like CopyNet and recursive neural networks.

Contribution

It presents a lightweight semantic parsing method leveraging POS tags and explores augmenting semantic operator prediction with advanced neural models.

Findings

POS-based semantic parsing is effective and efficient.

Augmentation with CopyNet improves prediction accuracy.

Recursive neural nets enhance semantic operator prediction.

Abstract

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.