Investigation of Sentiment Controllable Chatbot
Conventional seq2seq chatbot models attempt only to find sentences with the highest probabilities conditioned on the input sequences, without considering the sentiment of the output sentences. In this paper, we investigate four models to scale or adjust the sentiment of the chatbot response: a persona-based model, reinforcement learning, a plug and play model, and CycleGAN, all based on the seq2seq model. We also develop machine-evaluated metrics to estimate whether the responses are reasonable given the input. These metrics, together with human evaluation, are used to analyze the performance of the four models in terms of different aspects; reinforcement learning and CycleGAN are shown to be very attractive.
Code (0)
등록된 구현이 없습니다.
Tasks
Chatbotreinforcement-learningReinforcement LearningReinforcement Learning (RL)Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Positively transitioned sentiment dialogue corpus for developing emotion-affective open-domain chatbots
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 mu…
ChatbotScalable Sentiment for Sequence-to-sequence Chatbot Response with Performance Analysis
Conventional seq2seq chatbot models only try to find the sentences with the highest probabilities conditioned on the input sequences, without considering the sentiment of the output sentences. Some research works trying …
Chatbotreinforcement-learningReinforcement LearningReinforcement Learning (RL)Increasing happiness through conversations with artificial intelligence
Chatbots powered by artificial intelligence (AI) have rapidly become a significant part of everyday life, with over a quarter of American adults using them multiple times per week. While these tools offer potential benef…
ChatbotSentiment AnalysisSEPRG: Sentiment aware Emotion controlled Personalized Response Generation
Social chatbots have gained immense popularity, and their appeal lies not just in their capacity to respond to the diverse requests from users, but also in the ability to develop an emotional connection with users. To fu…
ChatbotDialogue GenerationResponse GenerationEmpBot: A T5-based Empathetic Chatbot focusing on Sentiments
In this paper, we introduce EmpBot: an end-to-end empathetic chatbot. Empathetic conversational agents should not only understand what is being discussed, but also acknowledge the implied feelings of the conversation par…
ChatbotLanguage ModelingLanguage Modelling