@inproceedings{lee-sedoc-2020-using,
title = "Using the Poly-encoder for a {COVID}-19 Question Answering System",
author = "Lee, Seolhwa and
Sedoc, Jo{\~a}o",
editor = "Verspoor, Karin and
Cohen, Kevin Bretonnel and
Conway, Michael and
de Bruijn, Berry and
Dredze, Mark and
Mihalcea, Rada and
Wallace, Byron",
booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
month = dec,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.nlpcovid19-2.33",
doi = "10.18653/v1/2020.nlpcovid19-2.33",
abstract = "To combat misinformation regarding COVID- 19 during this unprecedented pandemic, we propose a conversational agent that answers questions related to COVID-19. We adapt the Poly-encoder (Humeau et al., 2020) model for informational retrieval from FAQs. We show that after fine-tuning, the Poly-encoder can achieve a higher F1 score. We make our code publicly available for other researchers to use.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="lee-sedoc-2020-using">
<titleInfo>
<title>Using the Poly-encoder for a COVID-19 Question Answering System</title>
</titleInfo>
<name type="personal">
<namePart type="given">Seolhwa</namePart>
<namePart type="family">Lee</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">João</namePart>
<namePart type="family">Sedoc</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2020-12</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020</title>
</titleInfo>
<name type="personal">
<namePart type="given">Karin</namePart>
<namePart type="family">Verspoor</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kevin</namePart>
<namePart type="given">Bretonnel</namePart>
<namePart type="family">Cohen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Michael</namePart>
<namePart type="family">Conway</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Berry</namePart>
<namePart type="family">de Bruijn</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mark</namePart>
<namePart type="family">Dredze</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Rada</namePart>
<namePart type="family">Mihalcea</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>To combat misinformation regarding COVID- 19 during this unprecedented pandemic, we propose a conversational agent that answers questions related to COVID-19. We adapt the Poly-encoder (Humeau et al., 2020) model for informational retrieval from FAQs. We show that after fine-tuning, the Poly-encoder can achieve a higher F1 score. We make our code publicly available for other researchers to use.</abstract>
<identifier type="citekey">lee-sedoc-2020-using</identifier>
<identifier type="doi">10.18653/v1/2020.nlpcovid19-2.33</identifier>
<location>
<url>https://aclanthology.org/2020.nlpcovid19-2.33</url>
</location>
<part>
<date>2020-12</date>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Using the Poly-encoder for a COVID-19 Question Answering System
%A Lee, Seolhwa
%A Sedoc, João
%Y Verspoor, Karin
%Y Cohen, Kevin Bretonnel
%Y Conway, Michael
%Y de Bruijn, Berry
%Y Dredze, Mark
%Y Mihalcea, Rada
%Y Wallace, Byron
%S Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020
%D 2020
%8 December
%I Association for Computational Linguistics
%C Online
%F lee-sedoc-2020-using
%X To combat misinformation regarding COVID- 19 during this unprecedented pandemic, we propose a conversational agent that answers questions related to COVID-19. We adapt the Poly-encoder (Humeau et al., 2020) model for informational retrieval from FAQs. We show that after fine-tuning, the Poly-encoder can achieve a higher F1 score. We make our code publicly available for other researchers to use.
%R 10.18653/v1/2020.nlpcovid19-2.33
%U https://aclanthology.org/2020.nlpcovid19-2.33
%U https://doi.org/10.18653/v1/2020.nlpcovid19-2.33
Markdown (Informal)
[Using the Poly-encoder for a COVID-19 Question Answering System](https://aclanthology.org/2020.nlpcovid19-2.33) (Lee & Sedoc, NLP-COVID19 2020)
ACL