Topic-extended Emotional Conversation Generation Model Based on Joint Decoding

Mengshi Duan

doi:10.1109/ACCESS.2021.3090435

2021-01-01

Mengshi Duan

Prof. LI Qing (Qing Li)

Le Xiao *

DOI：10.1109/ACCESS.2021.3090435

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摘要

En 中文

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.

Keyword:

Decoding

Semantics

Long short term memory

Context modeling

Licenses

Emotional responses

User experience

Dialogue generation model

topic expansion

joint attention mechanism

joint decoder