1. Examination and Extension of Strategies for Improving Personalized Language Modeling via Interpolation
- Author
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Shao, Liqun, Mantravadi, Sahitya, Manzini, Tom, Buendia, Alejandro, Knoertzer, Manon, Srinivasan, Soundar, and Quirk, Chris
- Subjects
Computer Science - Computation and Language ,Computer Science - Machine Learning - 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., Comment: ACL Natural Language Interface Workshop 2020, short paper
- Published
- 2020