  Open-domain conversational agents or chatbots are becoming increasingly
popular in the natural language processing community. One of the challenges is
enabling them to converse in an empathetic manner. Current neural response
generation methods rely solely on end-to-end learning from large scale
conversation data to generate dialogues. This approach can produce socially
unacceptable responses due to the lack of large-scale quality data used to
train the neural models. However, recent work has shown the promise of
combining dialogue act/intent modelling and neural response generation. This
hybrid method improves the response quality of chatbots and makes them more
controllable and interpretable. A key element in dialog intent modelling is the
development of a taxonomy. Inspired by this idea, we have manually labeled 500
response intents using a subset of a sizeable empathetic dialogue dataset (25K
dialogues). Our goal is to produce a large-scale taxonomy for empathetic
response intents. Furthermore, using lexical and machine learning methods, we
automatically analysed both speaker and listener utterances of the entire
dataset with identified response intents and 32 emotion categories. Finally, we
use information visualization methods to summarize emotional dialogue exchange
patterns and their temporal progression. These results reveal novel and
important empathy patterns in human-human open-domain conversations and can
serve as heuristics for hybrid approaches.
