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CERG: Chinese Emotional Response Generator with Retrieval Method.

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This study introduces CERG, a novel neural network-based dialogue generator with emotion recognition. The system generates contextually relevant responses, enhancing human-computer interaction in non-task-oriented dialogue systems.

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Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • Computational Linguistics

Background:

  • Most existing dialogue systems are task-oriented, leaving significant room for improvement in non-task-oriented systems.
  • Developing non-task-oriented dialogue systems that can recognize and respond to emotions is a key challenge in AI.

Purpose of the Study:

  • To propose a data-driven, neural network-based dialogue generator named CERG for non-task-oriented conversations.
  • To enable the dialogue system with emotion recognition capabilities for generating contextually appropriate responses.

Main Methods:

  • Utilized a large dataset of Chinese Weibo post-response pairs with emotion labels from NTCIR-14 STC-3 CECG.
  • Employed an encoder-decoder framework using improved transformer blocks, masking response characters for training.
  • Incorporated regularization techniques to mitigate overcorrection and exposure bias, and a retrieval method to enhance semantic relevance.

Main Results:

  • The CERG model demonstrated the ability to generate distinct responses tailored to different emotions.
  • Manual evaluations confirmed that the model improves the human-computer interaction experience by providing emotion-aware responses.

Conclusions:

  • The proposed CERG model offers a promising approach for developing advanced non-task-oriented dialogue systems.
  • This technology has potential applications in social applications, such as automated reply robots.