@inproceedings{liednikova-etal-2021-gathering,
title = "Gathering Information and Engaging the User {C}om{B}ot: A Task-Based, Serendipitous Dialog Model for Patient-Doctor Interactions",
author = "Liednikova, Anna and
Jolivet, Philippe and
Durand-Salmon, Alexandre and
Gardent, Claire",
editor = "Shivade, Chaitanya and
Gangadharaiah, Rashmi and
Gella, Spandana and
Konam, Sandeep and
Yuan, Shaoqing and
Zhang, Yi and
Bhatia, Parminder and
Wallace, Byron",
booktitle = "Proceedings of the Second Workshop on Natural Language Processing for Medical Conversations",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.nlpmc-1.3",
doi = "10.18653/v1/2021.nlpmc-1.3",
pages = "21--29",
abstract = "We focus on dialog models in the context of clinical studies where the goal is to help gather, in addition to the close information collected based on a questionnaire, serendipitous information that is medically relevant. To promote user engagement and address this dual goal (collecting both a predefined set of data points and more informal information about the state of the patients), we introduce an ensemble model made of three bots: a task-based, a follow-up and a social bot. We introduce a generic method for developing follow-up bots. We compare different ensemble configurations and we show that the combination of the three bots (i) provides a better basis for collecting information than just the information seeking bot and (ii) collects information in a more user-friendly, more efficient manner that an ensemble model combining the information seeking and the social bot.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="liednikova-etal-2021-gathering">
<titleInfo>
<title>Gathering Information and Engaging the User ComBot: A Task-Based, Serendipitous Dialog Model for Patient-Doctor Interactions</title>
</titleInfo>
<name type="personal">
<namePart type="given">Anna</namePart>
<namePart type="family">Liednikova</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Philippe</namePart>
<namePart type="family">Jolivet</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Alexandre</namePart>
<namePart type="family">Durand-Salmon</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Claire</namePart>
<namePart type="family">Gardent</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2021-06</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Second Workshop on Natural Language Processing for Medical Conversations</title>
</titleInfo>
<name type="personal">
<namePart type="given">Chaitanya</namePart>
<namePart type="family">Shivade</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Rashmi</namePart>
<namePart type="family">Gangadharaiah</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Spandana</namePart>
<namePart type="family">Gella</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sandeep</namePart>
<namePart type="family">Konam</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Shaoqing</namePart>
<namePart type="family">Yuan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yi</namePart>
<namePart type="family">Zhang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Parminder</namePart>
<namePart type="family">Bhatia</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Byron</namePart>
<namePart type="family">Wallace</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Online</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>We focus on dialog models in the context of clinical studies where the goal is to help gather, in addition to the close information collected based on a questionnaire, serendipitous information that is medically relevant. To promote user engagement and address this dual goal (collecting both a predefined set of data points and more informal information about the state of the patients), we introduce an ensemble model made of three bots: a task-based, a follow-up and a social bot. We introduce a generic method for developing follow-up bots. We compare different ensemble configurations and we show that the combination of the three bots (i) provides a better basis for collecting information than just the information seeking bot and (ii) collects information in a more user-friendly, more efficient manner that an ensemble model combining the information seeking and the social bot.</abstract>
<identifier type="citekey">liednikova-etal-2021-gathering</identifier>
<identifier type="doi">10.18653/v1/2021.nlpmc-1.3</identifier>
<location>
<url>https://aclanthology.org/2021.nlpmc-1.3</url>
</location>
<part>
<date>2021-06</date>
<extent unit="page">
<start>21</start>
<end>29</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Gathering Information and Engaging the User ComBot: A Task-Based, Serendipitous Dialog Model for Patient-Doctor Interactions
%A Liednikova, Anna
%A Jolivet, Philippe
%A Durand-Salmon, Alexandre
%A Gardent, Claire
%Y Shivade, Chaitanya
%Y Gangadharaiah, Rashmi
%Y Gella, Spandana
%Y Konam, Sandeep
%Y Yuan, Shaoqing
%Y Zhang, Yi
%Y Bhatia, Parminder
%Y Wallace, Byron
%S Proceedings of the Second Workshop on Natural Language Processing for Medical Conversations
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F liednikova-etal-2021-gathering
%X We focus on dialog models in the context of clinical studies where the goal is to help gather, in addition to the close information collected based on a questionnaire, serendipitous information that is medically relevant. To promote user engagement and address this dual goal (collecting both a predefined set of data points and more informal information about the state of the patients), we introduce an ensemble model made of three bots: a task-based, a follow-up and a social bot. We introduce a generic method for developing follow-up bots. We compare different ensemble configurations and we show that the combination of the three bots (i) provides a better basis for collecting information than just the information seeking bot and (ii) collects information in a more user-friendly, more efficient manner that an ensemble model combining the information seeking and the social bot.
%R 10.18653/v1/2021.nlpmc-1.3
%U https://aclanthology.org/2021.nlpmc-1.3
%U https://doi.org/10.18653/v1/2021.nlpmc-1.3
%P 21-29
Markdown (Informal)
[Gathering Information and Engaging the User ComBot: A Task-Based, Serendipitous Dialog Model for Patient-Doctor Interactions](https://aclanthology.org/2021.nlpmc-1.3) (Liednikova et al., NLPMC 2021)
ACL