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.
@article{arxiv.1909.09531,
title = {Designing dialogue systems: A mean, grumpy, sarcastic chatbot in the browser},
author = {Suzana Ilić and Reiichiro Nakano and Ivo Hajnal},
journal= {arXiv preprint arXiv:1909.09531},
year = {2019}
}