# Exploiting Domain Knowledge via Grouped Weight Sharing with Application to Text Categorization

## TL;DR

This paper introduces a novel method for leveraging external linguistic resources in neural NLP models through grouped weight sharing, leading to improved classification performance.

## Contribution

It presents a new approach that uses weight sharing to incorporate domain knowledge into neural models, moving beyond traditional model compression techniques.

## Key findings

- Improved classification accuracy with external resources
- Consistent performance gains over baseline models
- Flexible integration of prior knowledge into neural networks

## Abstract

A fundamental advantage of neural models for NLP is their ability to learn representations from scratch. However, in practice this often means ignoring existing external linguistic resources, e.g., WordNet or domain specific ontologies such as the Unified Medical Language System (UMLS). We propose a general, novel method for exploiting such resources via weight sharing. Prior work on weight sharing in neural networks has considered it largely as a means of model compression. In contrast, we treat weight sharing as a flexible mechanism for incorporating prior knowledge into neural models. We show that this approach consistently yields improved performance on classification tasks compared to baseline strategies that do not exploit weight sharing.