We introduce dodecaDialogue: a set of 12 tasks that measures if a conversational agent can communicate engagingly with personality and empathy, ask questions, answer questions by utilizing knowledge resources, discuss topics and situations, and perceive and converse about images. By multi-tasking on such a broad large-scale set of data, we hope to both move towards and measure progress in producing a single unified agent that can perceive, reason and converse with humans in an open-domain setting. We show that such multi-tasking improves over a BERT pre-trained baseline, largely due to multi-tasking with very large dialogue datasets in a similar domain, and that the multi-tasking in general provides gains to both text and image-based tasks using several metrics in both the fine-tune and task transfer settings. We obtain state-of-the-art results on many of the tasks, providing a strong baseline for this challenge.
@article{arxiv.1911.03768,
title = {The Dialogue Dodecathlon: Open-Domain Knowledge and Image Grounded Conversational Agents},
author = {Kurt Shuster and Da Ju and Stephen Roller and Emily Dinan and Y-Lan Boureau and Jason Weston},
journal= {arXiv preprint arXiv:1911.03768},
year = {2020}
}