@inproceedings{kamath-etal-2022-improving,
title = "Improving Human Annotation Effectiveness for Fact Collection by Identifying the Most Relevant Answers",
author = "Kamath, Pranav and
Sun, Yiwen and
Semere, Thomas and
Green, Adam and
Manley, Scott and
Qi, Xiaoguang and
Qian, Kun and
Li, Yunyao",
editor = "Dragut, Eduard and
Li, Yunyao and
Popa, Lucian and
Vucetic, Slobodan and
Srivastava, Shashank",
booktitle = "Proceedings of the Fourth Workshop on Data Science with Human-in-the-Loop (Language Advances)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.dash-1.10",
pages = "74--80",
abstract = "Identifying and integrating missing facts is a crucial task for knowledge graph completion to ensure robustness towards downstream applications such as question answering. Adding new facts for a knowledge graph in real world system often involves human verification effort, where candidate facts are verified for accuracy by human annotators. This process is labor-intensive, time-consuming, and inefficient since only a small number of missing facts can be identified. This paper proposes a simple but effective human-in-the-loop framework for fact collection that searches for a diverse set of highly relevant candidate facts for human annotation. Empirical results presented in this work demonstrate that the proposed solution leads to both improvements in i) the quality of the candidate facts as well as ii) the ability of discovering more facts to grow the knowledge graph without requiring additional human effort.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="kamath-etal-2022-improving">
<titleInfo>
<title>Improving Human Annotation Effectiveness for Fact Collection by Identifying the Most Relevant Answers</title>
</titleInfo>
<name type="personal">
<namePart type="given">Pranav</namePart>
<namePart type="family">Kamath</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yiwen</namePart>
<namePart type="family">Sun</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Thomas</namePart>
<namePart type="family">Semere</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Adam</namePart>
<namePart type="family">Green</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Scott</namePart>
<namePart type="family">Manley</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xiaoguang</namePart>
<namePart type="family">Qi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kun</namePart>
<namePart type="family">Qian</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yunyao</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2022-12</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Fourth Workshop on Data Science with Human-in-the-Loop (Language Advances)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Eduard</namePart>
<namePart type="family">Dragut</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yunyao</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Lucian</namePart>
<namePart type="family">Popa</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Slobodan</namePart>
<namePart type="family">Vucetic</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Shashank</namePart>
<namePart type="family">Srivastava</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Abu Dhabi, United Arab Emirates (Hybrid)</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Identifying and integrating missing facts is a crucial task for knowledge graph completion to ensure robustness towards downstream applications such as question answering. Adding new facts for a knowledge graph in real world system often involves human verification effort, where candidate facts are verified for accuracy by human annotators. This process is labor-intensive, time-consuming, and inefficient since only a small number of missing facts can be identified. This paper proposes a simple but effective human-in-the-loop framework for fact collection that searches for a diverse set of highly relevant candidate facts for human annotation. Empirical results presented in this work demonstrate that the proposed solution leads to both improvements in i) the quality of the candidate facts as well as ii) the ability of discovering more facts to grow the knowledge graph without requiring additional human effort.</abstract>
<identifier type="citekey">kamath-etal-2022-improving</identifier>
<location>
<url>https://aclanthology.org/2022.dash-1.10</url>
</location>
<part>
<date>2022-12</date>
<extent unit="page">
<start>74</start>
<end>80</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Improving Human Annotation Effectiveness for Fact Collection by Identifying the Most Relevant Answers
%A Kamath, Pranav
%A Sun, Yiwen
%A Semere, Thomas
%A Green, Adam
%A Manley, Scott
%A Qi, Xiaoguang
%A Qian, Kun
%A Li, Yunyao
%Y Dragut, Eduard
%Y Li, Yunyao
%Y Popa, Lucian
%Y Vucetic, Slobodan
%Y Srivastava, Shashank
%S Proceedings of the Fourth Workshop on Data Science with Human-in-the-Loop (Language Advances)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F kamath-etal-2022-improving
%X Identifying and integrating missing facts is a crucial task for knowledge graph completion to ensure robustness towards downstream applications such as question answering. Adding new facts for a knowledge graph in real world system often involves human verification effort, where candidate facts are verified for accuracy by human annotators. This process is labor-intensive, time-consuming, and inefficient since only a small number of missing facts can be identified. This paper proposes a simple but effective human-in-the-loop framework for fact collection that searches for a diverse set of highly relevant candidate facts for human annotation. Empirical results presented in this work demonstrate that the proposed solution leads to both improvements in i) the quality of the candidate facts as well as ii) the ability of discovering more facts to grow the knowledge graph without requiring additional human effort.
%U https://aclanthology.org/2022.dash-1.10
%P 74-80
Markdown (Informal)
[Improving Human Annotation Effectiveness for Fact Collection by Identifying the Most Relevant Answers](https://aclanthology.org/2022.dash-1.10) (Kamath et al., DaSH 2022)
ACL
- Pranav Kamath, Yiwen Sun, Thomas Semere, Adam Green, Scott Manley, Xiaoguang Qi, Kun Qian, and Yunyao Li. 2022. Improving Human Annotation Effectiveness for Fact Collection by Identifying the Most Relevant Answers. In Proceedings of the Fourth Workshop on Data Science with Human-in-the-Loop (Language Advances), pages 74–80, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.