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Positively transitioned sentiment dialogue corpus for developing emotion-affective open-domain chatbots

arXiv 2208.04565 ↗First posted 9 Aug 2022cs.CLcited by 0

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Abstract (arXiv)

In this paper, we describe a data enhancement method for developing Emily, an emotion-affective open-domain chatbot. The proposed method is based on explicitly modeling positively transitioned (PT) sentiment data from multi-turn dialogues. We construct a dialogue corpus with PT sentiment data and will release it for public use. By fine-tuning a pretrained dialogue model using the produced PT-enhanced dialogues, we are able to develop an emotion-affective open-domain chatbot exhibiting close-to-human performance in various emotion-affective metrics. We evaluate Emily against a few state-of-the-art (SOTA) open-domain chatbots and show the effectiveness of the proposed approach. The corpus is made publicly available.

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References · 11

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generationcited by 842
  3. 2018MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversationscited by 636
  4. 2020Recipes for building an open-domain chatbotcited by 593
  5. 2021Towards Emotional Support Dialog Systemscited by 294
  6. 2017Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memorycited by 167
  7. 2019Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advancescited by 118
  8. 2018Generating Informative and Diverse Conversational Responses via Adversarial Information Maximizationcited by 114
  9. 2019MoEL: Mixture of Empathetic Listenerscited by 91
  10. 2020PLATO-2: Towards Building an Open-Domain Chatbot via Curriculum Learningcited by 79
  11. 2017MojiTalk: Generating Emotional Responses at Scalecited by 64

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