A Talker Ensemble: the University of Wroc{\l}aw's Entry to the NIPS 2017 Conversational Intelligence Challenge

TL;DR

The paper introduces Poetwannabe, a context-aware chatbot that excels in conversational question answering and user engagement, winning first place in the NIPS 2017 Conversational Intelligence Challenge.

Contribution

It presents a modular, data-driven dialogue system that combines multiple reply modules and confidence assessment, optimized for real-time performance on standard hardware.

Findings

Ranked first ex-aequo in NIPS 2017 Challenge

Effective integration of Wikipedia, DBpedia, and forum data

Achieved high-quality, context-aware conversations

Abstract

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.