Paper Info
Title
EmoEM: Emotional Expression in a Multi-turn Dialogue Model
Abstract
Emotional intelligence is a crucial part for human-machine dialogue system. However, the existing research on dialogue mainly faces three problems: (1) focus on the content level of each response while ignoring the impact of emotional factors in the multi-turn dialogue; (2) lacking scalability and adaptability is that only the emotion categories specified by users are generated in a single-turn dialogue; (3) it is difficult to capture and perceive fine-grained emotions and the speaker's emotional state according to the emotional context. To address these problems, we propose an emotional expression model in multi-turn dialogue (EmoEM), which combines emotion-semantic graph with multitask learning mechanism, applying the dialogue generator based on seq2seq network and graph convolution network (GCN) to generate more natural and personalized emotional responses in a structured manner. Generally, EmoEM considers constructing emotion-semantic graph to describe explicit and implicit emotions dynamically. Then, the emotion-semantic graph is applied to the dialogue generator based on the seq2seq neural network, mainly to improve the semantic consistency and text quality in multi-turn dialogue. Moreover, multi-task learning mechanism is introduced to enhance the emotional expression of the text and obtain expected emotional responses. The experimental results show that EmoEM outperforms several baselines in BLEU, diversity and emotional expression.
Year
DOI
Venue
2020
10.1109/ICTAI50040.2020.00083
2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI)
Keywords
DocType
ISSN
dialogue system,emotional expression,diversity,emotion-semantic graph
Conference
1082-3409
ISBN
Citations 
PageRank 
978-1-7281-8536-1
0
0.34
References 
Authors
8
6
Name
Order
Citations
PageRank
ao zhang167.89
Shaojuan Wu202.03
Xiaowang Zhang316338.77
Shizhan Chen46722.79
Yuchun Shu500.34
zhiyong feng65520.86