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Related Experiment Video

Updated: Feb 11, 2026

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Personalized response generation by Dual-learning based domain adaptation.

Min Yang1, Wenting Tu2, Qiang Qu1

  • 1Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.

Neural Networks : the Official Journal of the International Neural Network Society
|April 18, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces Personalized Response Generation by Dual-learning based Domain Adaptation (PRGDDA) to enhance AI conversations. The model improves personalized response generation by adapting to user-specific data after initial training on general conversation styles.

Keywords:
Deep reinforcement learningDomain adaptationDual learningPersonalized response generation

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Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • Machine Learning

Background:

  • Open-domain conversation AI is complex, requiring language understanding, reasoning, and common sense.
  • Existing models often lack personalization, limiting user engagement and response quality.

Purpose of the Study:

  • To improve AI response generation through personalization criteria.
  • To introduce a novel method for generating personalized conversational responses.

Main Methods:

  • Proposes Personalized Response Generation by Dual-learning based Domain Adaptation (PRGDDA).
  • Employs domain adaptation and dual learning theories for training.
  • Fine-tunes on small personalized datasets after initial general data training.

Main Results:

  • Demonstrates effectiveness on two real-world datasets (English and Chinese).
  • PRGDDA generates superior personalized responses tailored to individual users.
  • The dual learning mechanism enhances adaptation to user-specific conversational styles.

Conclusions:

  • PRGDDA effectively enhances personalized response generation in open-domain conversations.
  • The proposed method offers a viable approach for creating more engaging and user-centric AI chatbots.
  • Domain adaptation and dual learning are key to achieving effective personalization.