# On-Device Text Representations Robust To Misspellings via Projections

## TL;DR

This paper introduces LSH projection-based neural classifiers for on-device NLP tasks, demonstrating they are inherently more robust to misspellings and perturbations than traditional models like BERT and BiLSTMs.

## Contribution

The paper shows that LSH projection neural classifiers are naturally robust to misspellings, offering a privacy-preserving, low-memory alternative for on-device NLP applications.

## Key findings

- LSH projection classifiers outperform BiLSTMs and BERT in robustness to misspellings.
- Accuracy drop for LSH classifiers under misspelling attacks is only 2.94%.
- BERT's accuracy drops by 11.44% under similar conditions.

## Abstract

Recently, there has been a strong interest in developing natural language applications that live on personal devices such as mobile phones, watches and IoT with the objective to preserve user privacy and have low memory. Advances in Locality-Sensitive Hashing (LSH)-based projection networks have demonstrated state-of-the-art performance in various classification tasks without explicit word (or word-piece) embedding lookup tables by computing on-the-fly text representations. In this paper, we show that the projection based neural classifiers are inherently robust to misspellings and perturbations of the input text. We empirically demonstrate that the LSH projection based classifiers are more robust to common misspellings compared to BiLSTMs (with both word-piece & word-only tokenization) and fine-tuned BERT based methods. When subject to misspelling attacks, LSH projection based classifiers had a small average accuracy drop of 2.94% across multiple classifications tasks, while the fine-tuned BERT model accuracy had a significant drop of 11.44%.