# Efficient Transfer Learning Schemes for Personalized Language Modeling using Recurrent Neural Network

arXiv: https://arxiv.org/abs/1701.03578 · HTML: https://mlsanity.com/paper/1701.03578/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jan 2017.

## Built on by (most cited first, 5 of 5)

- [Attention-based Contextual Language Model Adaptation for Speech Recognition](https://mlsanity.com/paper/2106.01451/) (2021)
- [PerPLM: Personalized Fine-tuning of Pretrained Language Models via Writer-specific Intermediate Learning and Prompts](https://mlsanity.com/paper/2309.07727/) (2023)
- [Preserving Privacy in Personalized Models for Distributed Mobile Services](https://mlsanity.com/paper/2101.05855/) (2021)
- [SODA: Protecting Proprietary Information in On-Device Machine Learning Models](https://mlsanity.com/paper/2312.15036/) (2023)
- [An Ensemble Approach to Personalized Real Time Predictive Writing for Experts](https://mlsanity.com/paper/2308.13576/) (2023)

## Abstract (arXiv)

In this paper, we propose an efficient transfer leaning methods for training a personalized language model using a recurrent neural network with long short-term memory architecture. With our proposed fast transfer learning schemes, a general language model is updated to a personalized language model with a small amount of user data and a limited computing resource. These methods are especially useful for a mobile device environment while the data is prevented from transferring out of the device for privacy purposes. Through experiments on dialogue data in a drama, it is verified that our transfer learning methods have successfully generated the personalized language model, whose output is more similar to the personal language style in both qualitative and quantitative aspects.