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

• URL: http://arxiv.org/abs/2006.05469v1
• Date: Tue, 9 Jun 2020 19:29:41 GMT
• Title: Examination and Extension of Strategies for Improving Personalized Language Modeling via Interpolation
• Authors: Liqun Shao, Sahitya Mantravadi, Tom Manzini, Alejandro Buendia, Manon Knoertzer, Soundar Srinivasan, and Chris Quirk
• Abstract summary: 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. We observe that over 80% of users receive a lift in perplexity, with an average of 5.2% in perplexity lift per user.
• Score: 59.35932511895986
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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