  Empathy, which is widely used in psychological counselling, is a key trait of
everyday human conversations. Equipped with commonsense knowledge, current
approaches to empathetic response generation focus on capturing implicit
emotion within dialogue context, where the emotions are treated as a static
variable throughout the conversations. However, emotions change dynamically
between utterances, which makes previous works difficult to perceive the
emotion flow and predict the correct emotion of the target response, leading to
inappropriate response. Furthermore, simply importing commonsense knowledge
without harmonization may trigger the conflicts between knowledge and emotion,
which confuse the model to choose incorrect information to guide the generation
process. To address the above problems, we propose a Serial Encoding and
Emotion-Knowledge interaction (SEEK) method for empathetic dialogue generation.
We use a fine-grained encoding strategy which is more sensitive to the emotion
dynamics (emotion flow) in the conversations to predict the emotion-intent
characteristic of response. Besides, we design a novel framework to model the
interaction between knowledge and emotion to generate more sensible response.
Extensive experiments on EmpatheticDialogues demonstrate that SEEK outperforms
the strong baselines in both automatic and manual evaluations.
