The Experts below are selected from a list of 15909 Experts worldwide ranked by ideXlab platform
Baoxun Wang - One of the best experts on this subject based on the ideXlab platform.
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ACL - Guiding Variational Response Generator to Exploit Persona
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020Co-Authors: Zongsheng Wang, Yifu Chen, Derek F. Wong, Qihang Feng, Junhong Huang, Baoxun WangAbstract:Leveraging Persona Information of users in Neural Response Generators (NRG) to perform Personalized conversations has been considered as an attractive and important topic in the research of conversational agents over the past few years. Despite of the promising progress achieved by recent studies in this field, Persona Information tends to be incorporated into neural networks in the form of user embeddings, with the expectation that the Persona can be involved via End-to-End learning. This paper proposes to adopt the Personality-related characteristics of human conversations into variational response generators, by designing a specific conditional variational autoencoder based deep model with two new regularization terms employed to the loss function, so as to guide the optimization towards the direction of generating both Persona-aware and relevant responses. Besides, to reasonably evaluate the performances of various Persona modeling approaches, this paper further presents three direct Persona-oriented metrics from different perspectives. The experimental results have shown that our proposed methodology can notably improve the performance of Persona-aware response generation, and the metrics are reasonable to evaluate the results.
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Guiding Variational Response Generator to Exploit Persona
arXiv: Computation and Language, 2019Co-Authors: Zongsheng Wang, Yifu Chen, Derek F. Wong, Qihang Feng, Junhong Huang, Baoxun WangAbstract:Leveraging Persona Information of users in Neural Response Generators (NRG) to perform Personalized conversations has been considered as an attractive and important topic in the research of conversational agents over the past few years. Despite of the promising progresses achieved by recent studies in this field, Persona Information tends to be incorporated into neural networks in the form of user embeddings, with the expectation that the Persona can be involved via the End-to-End learning. This paper proposes to adopt the Personality-related characteristics of human conversations into variational response generators, by designing a specific conditional variational autoencoder based deep model with two new regularization terms employed to the loss function, so as to guide the optimization towards the direction of generating both Persona-aware and relevant responses. Besides, to reasonably evaluate the performances of various Persona modeling approaches, this paper further presents three direct Persona-oriented metrics from different perspectives. The experimental results have shown that our proposed methodology can notably improve the performance of Persona-aware response generation, and the metrics are reasonable to evaluate the results.
Ting Liu - One of the best experts on this subject based on the ideXlab platform.
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IJCAI - Exploiting Persona Information for Diverse Generation of Conversational Responses.
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019Co-Authors: Haoyu Song, Wei-nan Zhang, Yiming Cui, Dong Wang, Ting LiuAbstract:In human conversations, due to their Personalities in mind, people can easily carry out and maintain the conversations. Giving conversational context with Persona Information to a chatbot, how to exploit the Information to generate diverse and sustainable conversations is still a non-trivial task. Previous work on Persona-based conversational models successfully make use of predefined Persona Information and have shown great promise in delivering more realistic responses. And they all learn with the assumption that given a source input, there is only one target response. However, in human conversations, there are massive appropriate responses to a given input message. In this paper, we propose a memory-augmented architecture to exploit Persona Information from context and incorporate a conditional variational autoencoder model together to generate diverse and sustainable conversations. We evaluate the proposed model on a benchmark Persona-chat dataset. Both automatic and human evaluations show that our model can deliver more diverse and more engaging Persona-based responses than baseline approaches.
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Exploiting Persona Information for Diverse Generation of Conversational Responses.
arXiv: Artificial Intelligence, 2019Co-Authors: Haoyu Song, Wei-nan Zhang, Yiming Cui, Dong Wang, Ting LiuAbstract:In human conversations, due to their Personalities in mind, people can easily carry out and maintain the conversations. Giving conversational context with Persona Information to a chatbot, how to exploit the Information to generate diverse and sustainable conversations is still a non-trivial task. Previous work on Persona-based conversational models successfully make use of predefined Persona Information and have shown great promise in delivering more realistic responses. And they all learn with the assumption that given a source input, there is only one target response. However, in human conversations, there are massive appropriate responses to a given input message. In this paper, we propose a memory-augmented architecture to exploit Persona Information from context and incorporate a conditional variational autoencoder model together to generate diverse and sustainable conversations. We evaluate the proposed model on a benchmark Persona-chat dataset. Both automatic and human evaluations show that our model can deliver more diverse and more engaging Persona-based responses than baseline approaches.
Zongsheng Wang - One of the best experts on this subject based on the ideXlab platform.
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ACL - Guiding Variational Response Generator to Exploit Persona
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020Co-Authors: Zongsheng Wang, Yifu Chen, Derek F. Wong, Qihang Feng, Junhong Huang, Baoxun WangAbstract:Leveraging Persona Information of users in Neural Response Generators (NRG) to perform Personalized conversations has been considered as an attractive and important topic in the research of conversational agents over the past few years. Despite of the promising progress achieved by recent studies in this field, Persona Information tends to be incorporated into neural networks in the form of user embeddings, with the expectation that the Persona can be involved via End-to-End learning. This paper proposes to adopt the Personality-related characteristics of human conversations into variational response generators, by designing a specific conditional variational autoencoder based deep model with two new regularization terms employed to the loss function, so as to guide the optimization towards the direction of generating both Persona-aware and relevant responses. Besides, to reasonably evaluate the performances of various Persona modeling approaches, this paper further presents three direct Persona-oriented metrics from different perspectives. The experimental results have shown that our proposed methodology can notably improve the performance of Persona-aware response generation, and the metrics are reasonable to evaluate the results.
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Guiding Variational Response Generator to Exploit Persona
arXiv: Computation and Language, 2019Co-Authors: Zongsheng Wang, Yifu Chen, Derek F. Wong, Qihang Feng, Junhong Huang, Baoxun WangAbstract:Leveraging Persona Information of users in Neural Response Generators (NRG) to perform Personalized conversations has been considered as an attractive and important topic in the research of conversational agents over the past few years. Despite of the promising progresses achieved by recent studies in this field, Persona Information tends to be incorporated into neural networks in the form of user embeddings, with the expectation that the Persona can be involved via the End-to-End learning. This paper proposes to adopt the Personality-related characteristics of human conversations into variational response generators, by designing a specific conditional variational autoencoder based deep model with two new regularization terms employed to the loss function, so as to guide the optimization towards the direction of generating both Persona-aware and relevant responses. Besides, to reasonably evaluate the performances of various Persona modeling approaches, this paper further presents three direct Persona-oriented metrics from different perspectives. The experimental results have shown that our proposed methodology can notably improve the performance of Persona-aware response generation, and the metrics are reasonable to evaluate the results.
Haoyu Song - One of the best experts on this subject based on the ideXlab platform.
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IJCAI - Exploiting Persona Information for Diverse Generation of Conversational Responses.
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019Co-Authors: Haoyu Song, Wei-nan Zhang, Yiming Cui, Dong Wang, Ting LiuAbstract:In human conversations, due to their Personalities in mind, people can easily carry out and maintain the conversations. Giving conversational context with Persona Information to a chatbot, how to exploit the Information to generate diverse and sustainable conversations is still a non-trivial task. Previous work on Persona-based conversational models successfully make use of predefined Persona Information and have shown great promise in delivering more realistic responses. And they all learn with the assumption that given a source input, there is only one target response. However, in human conversations, there are massive appropriate responses to a given input message. In this paper, we propose a memory-augmented architecture to exploit Persona Information from context and incorporate a conditional variational autoencoder model together to generate diverse and sustainable conversations. We evaluate the proposed model on a benchmark Persona-chat dataset. Both automatic and human evaluations show that our model can deliver more diverse and more engaging Persona-based responses than baseline approaches.
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Exploiting Persona Information for Diverse Generation of Conversational Responses.
arXiv: Artificial Intelligence, 2019Co-Authors: Haoyu Song, Wei-nan Zhang, Yiming Cui, Dong Wang, Ting LiuAbstract:In human conversations, due to their Personalities in mind, people can easily carry out and maintain the conversations. Giving conversational context with Persona Information to a chatbot, how to exploit the Information to generate diverse and sustainable conversations is still a non-trivial task. Previous work on Persona-based conversational models successfully make use of predefined Persona Information and have shown great promise in delivering more realistic responses. And they all learn with the assumption that given a source input, there is only one target response. However, in human conversations, there are massive appropriate responses to a given input message. In this paper, we propose a memory-augmented architecture to exploit Persona Information from context and incorporate a conditional variational autoencoder model together to generate diverse and sustainable conversations. We evaluate the proposed model on a benchmark Persona-chat dataset. Both automatic and human evaluations show that our model can deliver more diverse and more engaging Persona-based responses than baseline approaches.
Yifu Chen - One of the best experts on this subject based on the ideXlab platform.
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ACL - Guiding Variational Response Generator to Exploit Persona
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020Co-Authors: Zongsheng Wang, Yifu Chen, Derek F. Wong, Qihang Feng, Junhong Huang, Baoxun WangAbstract:Leveraging Persona Information of users in Neural Response Generators (NRG) to perform Personalized conversations has been considered as an attractive and important topic in the research of conversational agents over the past few years. Despite of the promising progress achieved by recent studies in this field, Persona Information tends to be incorporated into neural networks in the form of user embeddings, with the expectation that the Persona can be involved via End-to-End learning. This paper proposes to adopt the Personality-related characteristics of human conversations into variational response generators, by designing a specific conditional variational autoencoder based deep model with two new regularization terms employed to the loss function, so as to guide the optimization towards the direction of generating both Persona-aware and relevant responses. Besides, to reasonably evaluate the performances of various Persona modeling approaches, this paper further presents three direct Persona-oriented metrics from different perspectives. The experimental results have shown that our proposed methodology can notably improve the performance of Persona-aware response generation, and the metrics are reasonable to evaluate the results.
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Guiding Variational Response Generator to Exploit Persona
arXiv: Computation and Language, 2019Co-Authors: Zongsheng Wang, Yifu Chen, Derek F. Wong, Qihang Feng, Junhong Huang, Baoxun WangAbstract:Leveraging Persona Information of users in Neural Response Generators (NRG) to perform Personalized conversations has been considered as an attractive and important topic in the research of conversational agents over the past few years. Despite of the promising progresses achieved by recent studies in this field, Persona Information tends to be incorporated into neural networks in the form of user embeddings, with the expectation that the Persona can be involved via the End-to-End learning. This paper proposes to adopt the Personality-related characteristics of human conversations into variational response generators, by designing a specific conditional variational autoencoder based deep model with two new regularization terms employed to the loss function, so as to guide the optimization towards the direction of generating both Persona-aware and relevant responses. Besides, to reasonably evaluate the performances of various Persona modeling approaches, this paper further presents three direct Persona-oriented metrics from different perspectives. The experimental results have shown that our proposed methodology can notably improve the performance of Persona-aware response generation, and the metrics are reasonable to evaluate the results.