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The Dialogue Dodecathlon: Open-Domain Knowledge and Image Grounded Conversational Agents (1911.03768v2)

Published 9 Nov 2019 in cs.CL and cs.AI

Abstract: 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.

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Authors (6)
  1. Kurt Shuster (28 papers)
  2. Da Ju (18 papers)
  3. Stephen Roller (27 papers)
  4. Emily Dinan (28 papers)
  5. Y-Lan Boureau (26 papers)
  6. Jason Weston (130 papers)
Citations (79)