  We present Poetwannabe, a chatbot submitted by the University of Wroc{\l}aw
to the NIPS 2017 Conversational Intelligence Challenge, in which it ranked
first ex-aequo. It is able to conduct a conversation with a user in a natural
language. The primary functionality of our dialogue system is context-aware
question answering (QA), while its secondary function is maintaining user
engagement. The chatbot is composed of a number of sub-modules, which
independently prepare replies to user's prompts and assess their own
confidence. To answer questions, our dialogue system relies heavily on factual
data, sourced mostly from Wikipedia and DBpedia, data of real user interactions
in public forums, as well as data concerning general literature. Where
applicable, modules are trained on large datasets using GPUs. However, to
comply with the competition's requirements, the final system is compact and
runs on commodity hardware.
