@inproceedings{gupta-etal-2018-retrieve,
title = "Retrieve and Re-rank: A Simple and Effective {IR} Approach to Simple Question Answering over Knowledge Graphs",
author = "Gupta, Vishal and
Chinnakotla, Manoj and
Shrivastava, Manish",
booktitle = "Proceedings of the First Workshop on Fact Extraction and {VER}ification ({FEVER})",
month = nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-5504",
doi = "10.18653/v1/W18-5504",
pages = "22--27",
abstract = "SimpleQuestions is a commonly used benchmark for single-factoid question answering (QA) over Knowledge Graphs (KG). Existing QA systems rely on various components to solve different sub-tasks of the problem (such as entity detection, entity linking, relation prediction and evidence integration). In this work, we propose a different approach to the problem and present an information retrieval style solution for it. We adopt a two-phase approach: candidate generation and candidate re-ranking to answer questions. We propose a Triplet-Siamese-Hybrid CNN (TSHCNN) to re-rank candidate answers. Our approach achieves an accuracy of 80{\%} which sets a new state-of-the-art on the SimpleQuestions dataset.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="gupta-etal-2018-retrieve">
<titleInfo>
<title>Retrieve and Re-rank: A Simple and Effective IR Approach to Simple Question Answering over Knowledge Graphs</title>
</titleInfo>
<name type="personal">
<namePart type="given">Vishal</namePart>
<namePart type="family">Gupta</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Manoj</namePart>
<namePart type="family">Chinnakotla</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Manish</namePart>
<namePart type="family">Shrivastava</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2018-nov</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)</title>
</titleInfo>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Brussels, Belgium</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>SimpleQuestions is a commonly used benchmark for single-factoid question answering (QA) over Knowledge Graphs (KG). Existing QA systems rely on various components to solve different sub-tasks of the problem (such as entity detection, entity linking, relation prediction and evidence integration). In this work, we propose a different approach to the problem and present an information retrieval style solution for it. We adopt a two-phase approach: candidate generation and candidate re-ranking to answer questions. We propose a Triplet-Siamese-Hybrid CNN (TSHCNN) to re-rank candidate answers. Our approach achieves an accuracy of 80% which sets a new state-of-the-art on the SimpleQuestions dataset.</abstract>
<identifier type="citekey">gupta-etal-2018-retrieve</identifier>
<identifier type="doi">10.18653/v1/W18-5504</identifier>
<location>
<url>https://aclanthology.org/W18-5504</url>
</location>
<part>
<date>2018-nov</date>
<extent unit="page">
<start>22</start>
<end>27</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Retrieve and Re-rank: A Simple and Effective IR Approach to Simple Question Answering over Knowledge Graphs
%A Gupta, Vishal
%A Chinnakotla, Manoj
%A Shrivastava, Manish
%S Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)
%D 2018
%8 nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F gupta-etal-2018-retrieve
%X SimpleQuestions is a commonly used benchmark for single-factoid question answering (QA) over Knowledge Graphs (KG). Existing QA systems rely on various components to solve different sub-tasks of the problem (such as entity detection, entity linking, relation prediction and evidence integration). In this work, we propose a different approach to the problem and present an information retrieval style solution for it. We adopt a two-phase approach: candidate generation and candidate re-ranking to answer questions. We propose a Triplet-Siamese-Hybrid CNN (TSHCNN) to re-rank candidate answers. Our approach achieves an accuracy of 80% which sets a new state-of-the-art on the SimpleQuestions dataset.
%R 10.18653/v1/W18-5504
%U https://aclanthology.org/W18-5504
%U https://doi.org/10.18653/v1/W18-5504
%P 22-27
Markdown (Informal)
[Retrieve and Re-rank: A Simple and Effective IR Approach to Simple Question Answering over Knowledge Graphs](https://aclanthology.org/W18-5504) (Gupta et al., 2018)
ACL