Examination and Extension of Strategies for Improving Personalized Language Modeling via Interpolation

TL;DR

This paper introduces novel interpolation strategies for personalized language models, combining global LSTM and user-specific n-gram models, to enhance performance and handle OOV tokens effectively.

Contribution

It proposes new interpolation methods and OOV handling techniques that significantly improve user-level language modeling performance.

Findings

Over 80% of users see perplexity improvements.

Average perplexity reduction of 5.2% per user.

Enhanced robustness of metrics for downstream NLP tasks.

Abstract

In this paper, we detail novel strategies for interpolating personalized language models and methods to handle out-of-vocabulary (OOV) tokens to improve personalized language models. Using publicly available data from Reddit, we demonstrate improvements in offline metrics at the user level by interpolating a global LSTM-based authoring model with a user-personalized n-gram model. By optimizing this approach with a back-off to uniform OOV penalty and the interpolation coefficient, we observe that over 80% of users receive a lift in perplexity, with an average of 5.2% in perplexity lift per user. In doing this research we extend previous work in building NLIs and improve the robustness of metrics for downstream tasks.