  In this work we explore a deep learning-based dialogue system that generates
sarcastic and humorous responses from a conversation design perspective. We
trained a seq2seq model on a carefully curated dataset of 3000
question-answering pairs, the core of our mean, grumpy, sarcastic chatbot. We
show that end-to-end systems learn patterns very quickly from small datasets
and thus, are able to transfer simple linguistic structures representing
abstract concepts to unseen settings. We also deploy our LSTM-based
encoder-decoder model in the browser, where users can directly interact with
the chatbot. Human raters evaluated linguistic quality, creativity and
human-like traits, revealing the system's strengths, limitations and potential
for future research.
