# Siamese Networks for Semantic Pattern Similarity

arXiv: https://arxiv.org/abs/1812.06604 · HTML: https://mlsanity.com/paper/1812.06604/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2018.

## Built on by (most cited first, 6 of 6)

- [Explaining Relationships Among Research Papers](https://mlsanity.com/paper/2402.13426/) (2024)
- [A Cognitive Study on Semantic Similarity Analysis of Large Corpora: A Transformer-based Approach](https://mlsanity.com/paper/2207.11716/) (2022)
- [PhysiQ: Off-site Quality Assessment of Exercise in Physical Therapy](https://mlsanity.com/paper/2211.08245/) (2022)
- [SuSana Distancia is all you need: Enforcing class separability in metric learning via two novel distance-based loss functions for few-shot image classification](https://mlsanity.com/paper/2305.09062/) (2023)
- [No Pairs Left Behind: Improving Metric Learning with Regularized Triplet Objective](https://mlsanity.com/paper/2210.09506/) (2022)
- [Object-based Probabilistic Similarity Evidence of Sparse Latent Features from Fully Convolutional Networks](https://mlsanity.com/paper/2307.13606/) (2023)

## Abstract (arXiv)

Semantic Pattern Similarity is an interesting, though not often encountered NLP task where two sentences are compared not by their specific meaning, but by their more abstract semantic pattern (e.g., preposition or frame). We utilize Siamese Networks to model this task, and show its usefulness in determining SQL patterns for unseen questions in a database-backed question answering scenario. Our approach achieves high accuracy and contains a built-in proxy for confidence, which can be used to keep precision arbitrarily high.