Topic-extended Emotional Conversation Generation Model Based on Joint Decoding
| Autor: | Le Xiao, Qing Li, Duan Mengshi |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
joint attention mechanism
Context model Dialogue generation model Joint attention topic expansion General Computer Science Computer science business.industry media_common.quotation_subject Speech recognition General Engineering Semantics Expression (mathematics) TK1-9971 User experience design General Materials Science Conversation Electrical engineering. Electronics. Nuclear engineering Electrical and Electronic Engineering joint decoder business Encoder Decoding methods media_common |
| Zdroj: | IEEE Access, Vol 9, Pp 89934-89940 (2021) |
| ISSN: | 2169-3536 |
| DOI: | 10.1109/access.2021.3090435 |
| Popis: | The research on the expression of emotion in human-computer dialogue can greatly improve the user experience. Existing research has paid a lot of attention to how to generate specific emotional content and how to improve the extraction rate of emotions, while ignoring the reduction of emotion expression caused by factors such as topics and emotions added to the encoder. This paper proposes a novel Topic-extended Emotional Conversation Generation Model Based on Joint Decoding (TECM-JD). The model embeds the specified emotion category as an additional input into the emotional independent unit of the decoder, in order to reduce the expression of the content affected by adding emotion into the model. The joint attention mechanism is used to obtain the input sequence content and the input sequence topic word content obtained by the Twitter LDA model, which ensures that the output topic and the input are under the same topic. The experimental results show that the proposed model can generate richer emotional content related to the topic and have good performance and are superior to traditional dialogue models. |
| Databáze: | OpenAIRE |
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