@inproceedings{shuster-etal-2020-dialogue,
title = "The Dialogue Dodecathlon: Open-Domain Knowledge and Image Grounded Conversational Agents",
author = "Shuster, Kurt and
Ju, Da and
Roller, Stephen and
Dinan, Emily and
Boureau, Y-Lan and
Weston, Jason",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.222",
doi = "10.18653/v1/2020.acl-main.222",
pages = "2453--2470",
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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%0 Conference Proceedings
%T The Dialogue Dodecathlon: Open-Domain Knowledge and Image Grounded Conversational Agents
%A Shuster, Kurt
%A Ju, Da
%A Roller, Stephen
%A Dinan, Emily
%A Boureau, Y-Lan
%A Weston, Jason
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F shuster-etal-2020-dialogue
%X 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.
%R 10.18653/v1/2020.acl-main.222
%U https://aclanthology.org/2020.acl-main.222
%U https://doi.org/10.18653/v1/2020.acl-main.222
%P 2453-2470
Markdown (Informal)
[The Dialogue Dodecathlon: Open-Domain Knowledge and Image Grounded Conversational Agents](https://aclanthology.org/2020.acl-main.222) (Shuster et al., ACL 2020)
ACL