Enabling the machines with empathetic abilities to provide context-consistent responses is crucial on both semantic and emotional levels. The task of empathetic dialogue generation is proposed to address this problem. However, two challenges still exist in this task: perceiving nuanced emotions implied in the dialogue context and modelling emotional dependencies. Lacking useful external knowledge makes it challenging to perceive implicit fine-grained emotions . Missing the emotional interactions among interlocutors also restricts the performance of empathetic dialogue generation . To address  above challenges, we propose a knowledge-enhanced framework, named Know-EDG. We first enrich dialogue context by bunches of emotion-related concepts and construct a knowledge-enhanced context graph. Then we introduce a graph-aware Transformer encoder to learn graph's semantic and emotional representations , which are the prerequisites of the emotion identifier to predicate the target emotion signal . Finally, we propose an emotion-focused attention mechanism to exploit the emotional dependencies between dialogue context and target empathetic response. Conducted on a benchmark dataset, extensive experimental results show that our proposed framework outperforms state-of-the-art baselines in terms of automatic metrics and human evaluations.
Enabling the machines with empathetic abilities to provide context-consistent responses is crucial on both semantic and emotional levels. The task of empathetic dialogue generation is proposed to address this problem. However, lacking external knowledge makes it difficult to perceive implicit emotions from limited dialogue history . To address the above challenges, we propose to leverage multi-type knowledge, i.e, the commonsense knowledge and emotional lexicon, to explicitly understand and express emotions in empathetic dialogue generation. We first enrich the dialogue history by jointly interacting with two-type knowledge and construct an emotional context graph. Then we introduce a multi-type knowledge-aware context encoder to learn emotional context representations and distill emotional signals , which are the prerequisites to predicate emotions expressed in responses . Finally, we propose an emotional cross-attention mechanism to exploit the emotional dependencies between the emotional context graph and the target empathetic response. Conducted on a benchmark dataset, extensive experimental results show that our proposed framework outperforms state-of-the-art baselines in terms of automatic metrics and human evaluations.
R
two challenges still exist in this task: perceiving nuanced emotions implied in the dialogue context and modelling emotional dependencies. Lacking useful
lacking
R
challenging
difficult
R
fine-grained emotions . Missing the emotional interactions among interlocutors also restricts the performance of empathetic dialogue generation
emotions from limited dialogue history
A
the
R
a knowledge-enhanced framework, named Know-EDG.
to leverage multi-type knowledge, i.e, the commonsense knowledge and emotional lexicon, to explicitly understand and express emotions in empathetic dialogue generation.
R
dialogue context by bunches of emotion-related concepts and construct a knowledge-enhanced
the dialogue history by jointly interacting with two-type knowledge and construct an emotional
R
graph-aware Transformer
multi-type knowledge-aware context
R
graph's semantic and emotional representations
emotional context representations and distill emotional signals
R
of the emotion identifier to predicate the target emotion signal
to predicate emotions expressed in responses
R
emotion-focused attention
emotional cross-attention
R
dialogue context and
the emotional context graph and the