@inproceedings{rodriguez-etal-2020-information,
title = "Information Seeking in the Spirit of Learning: A Dataset for Conversational Curiosity",
author = "Rodriguez, Pedro and
Crook, Paul and
Moon, Seungwhan and
Wang, Zhiguang",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.655",
doi = "10.18653/v1/2020.emnlp-main.655",
pages = "8153--8172",
abstract = "Open-ended human learning and information-seeking are increasingly mediated by digital assistants. However, such systems often ignore the user{'}s pre-existing knowledge. Assuming a correlation between engagement and user responses such as {``}liking{''} messages or asking followup questions, we design a Wizard-of-Oz dialog task that tests the hypothesis that engagement increases when users are presented with facts related to what they know. Through crowd-sourcing of this experiment, we collect and release 14K dialogs (181K utterances) where users and assistants converse about geographic topics like geopolitical entities and locations. This dataset is annotated with pre-existing user knowledge, message-level dialog acts, grounding to Wikipedia, and user reactions to messages. Responses using a user{'}s prior knowledge increase engagement. We incorporate this knowledge into a multi-task model that reproduces human assistant policies and improves over a bert content model by 13 mean reciprocal rank points.",
}
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<abstract>Open-ended human learning and information-seeking are increasingly mediated by digital assistants. However, such systems often ignore the user’s pre-existing knowledge. Assuming a correlation between engagement and user responses such as “liking” messages or asking followup questions, we design a Wizard-of-Oz dialog task that tests the hypothesis that engagement increases when users are presented with facts related to what they know. Through crowd-sourcing of this experiment, we collect and release 14K dialogs (181K utterances) where users and assistants converse about geographic topics like geopolitical entities and locations. This dataset is annotated with pre-existing user knowledge, message-level dialog acts, grounding to Wikipedia, and user reactions to messages. Responses using a user’s prior knowledge increase engagement. We incorporate this knowledge into a multi-task model that reproduces human assistant policies and improves over a bert content model by 13 mean reciprocal rank points.</abstract>
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%0 Conference Proceedings
%T Information Seeking in the Spirit of Learning: A Dataset for Conversational Curiosity
%A Rodriguez, Pedro
%A Crook, Paul
%A Moon, Seungwhan
%A Wang, Zhiguang
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F rodriguez-etal-2020-information
%X Open-ended human learning and information-seeking are increasingly mediated by digital assistants. However, such systems often ignore the user’s pre-existing knowledge. Assuming a correlation between engagement and user responses such as “liking” messages or asking followup questions, we design a Wizard-of-Oz dialog task that tests the hypothesis that engagement increases when users are presented with facts related to what they know. Through crowd-sourcing of this experiment, we collect and release 14K dialogs (181K utterances) where users and assistants converse about geographic topics like geopolitical entities and locations. This dataset is annotated with pre-existing user knowledge, message-level dialog acts, grounding to Wikipedia, and user reactions to messages. Responses using a user’s prior knowledge increase engagement. We incorporate this knowledge into a multi-task model that reproduces human assistant policies and improves over a bert content model by 13 mean reciprocal rank points.
%R 10.18653/v1/2020.emnlp-main.655
%U https://aclanthology.org/2020.emnlp-main.655
%U https://doi.org/10.18653/v1/2020.emnlp-main.655
%P 8153-8172
Markdown (Informal)
[Information Seeking in the Spirit of Learning: A Dataset for Conversational Curiosity](https://aclanthology.org/2020.emnlp-main.655) (Rodriguez et al., EMNLP 2020)
ACL