[Paper Review] Efficient Transfer Learning Schemes for Personalized Language Modeling using Recurrent Neural Network

Seunghyun Yoon, Hyeongu Yun+3 more|arXiv (Cornell University)|Jan 13, 2017

Topic ModelingComputer Science14 references16 citations

TL;DR

This paper proposes efficient transfer learning schemes for training personalized language models using LSTM-RNN, enabling fine-tuning on mobile devices with minimal user data and no data leakage. By transferring knowledge from a general language model and applying techniques like layer freezing and surplus layer insertion, the method achieves high stylistic similarity to user-specific dialogue patterns with significantly reduced training cost and preserved privacy.

ABSTRACT

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.

Motivation & Objective

• To enable personalized language modeling on resource-constrained mobile devices without transferring user data outside the device.
• To reduce the computational and data requirements for training personalized language models compared to end-to-end training.
• To preserve user privacy by training the personalized model locally using transfer learning from a general pre-trained model.
• To improve model generalization and style adaptation using efficient fine-tuning schemes on limited target data.

Proposed method

• A general language model is pre-trained on large-scale corpora (e.g., WMT, 'Friends' script) using high-performance machines.
• The pre-trained model's first n layers are transferred to a target model for personalized adaptation on the user's device.
• Fine-tuning is performed on the user's private dialogue data using transfer learning schemes: 'fixed-n layer' and 'surplus layer'.
• The 'fixed-n layer' scheme freezes the first n layers of the base model and fine-tunes only the remaining layers.
• The 'surplus layer' scheme adds an extra layer on top of the transferred layers to enhance representational capacity for personalized data.
• Model adaptation is performed locally on mobile devices, ensuring no user data leaves the device, preserving privacy.

Experimental results

Research questions

• RQ1 Can a personalized language model be effectively trained on mobile devices using only a small amount of private user data?
• RQ2 How can transfer learning from a general language model improve personalization while minimizing computational cost and data transfer?
• RQ3 Which transfer learning scheme—'fixed-n layer' or 'surplus layer'—yields better generalization and style adaptation for personalized language modeling?
• RQ4 To what extent does the fine-tuned model capture the linguistic style of the target user compared to the general model?

Key findings

• The 'surplus layer' and 'fixed-n layer' schemes achieved higher generalization performance than the baseline model while training significantly fewer parameters.
• The personalized language model showed higher similarity to the target user's dialogue style in both qualitative and quantitative evaluations.
• Cross-entropy loss was lowest when the model was fine-tuned on the 'Chandler' corpus, indicating better style alignment with the target user's language.
• The model trained on the 'Friends' script and fine-tuned on 'Chandler' data produced responses with higher similarity to Chandler’s actual dialogue than to other characters’ dialogues.
• The model achieved effective personalization with minimal user data, reducing the need for large-scale training data typically required in deep learning.