<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="zhang-etal-2020-cn">
    <titleInfo>
        <title>CN-HIT-IT.NLP at SemEval-2020 Task 4: Enhanced Language Representation with Multiple Knowledge Triples</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Yice</namePart>
        <namePart type="family">Zhang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Jiaxuan</namePart>
        <namePart type="family">Lin</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Yang</namePart>
        <namePart type="family">Fan</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Peng</namePart>
        <namePart type="family">Jin</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Yuanchao</namePart>
        <namePart type="family">Liu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Bingquan</namePart>
        <namePart type="family">Liu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2020-dec</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Fourteenth Workshop on Semantic Evaluation</title>
        </titleInfo>
        <originInfo>
            <publisher>International Committee for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Barcelona (online)</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>This paper describes our system that participated in the SemEval-2020 task 4: Commonsense Validation and Explanation. For this task, it is obvious that external knowledge, such as Knowledge graph, can help the model understand commonsense in natural language statements. But how to select the right triples for statements remains unsolved, so how to reduce the interference of irrelevant triples on model performance is a research focus. This paper adopt a modified K-BERT as the language encoder, to enhance language representation through triples from knowledge graphs. Experiments show that our method is better than models without external knowledge, and is slightly better than the original K-BERT. We got an accuracy score of 0.97 in subtaskA, ranking 1/45, and got an accuracy score of 0.948, ranking 2/35.</abstract>
    <identifier type="citekey">zhang-etal-2020-cn</identifier>
    <identifier type="doi">10.18653/v1/2020.semeval-1.60</identifier>
    <location>
        <url>https://aclanthology.org/2020.semeval-1.60</url>
    </location>
    <part>
        <date>2020-dec</date>
        <extent unit="page">
            <start>494</start>
            <end>500</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T CN-HIT-IT.NLP at SemEval-2020 Task 4: Enhanced Language Representation with Multiple Knowledge Triples
%A Zhang, Yice
%A Lin, Jiaxuan
%A Fan, Yang
%A Jin, Peng
%A Liu, Yuanchao
%A Liu, Bingquan
%S Proceedings of the Fourteenth Workshop on Semantic Evaluation
%D 2020
%8 dec
%I International Committee for Computational Linguistics
%C Barcelona (online)
%F zhang-etal-2020-cn
%X This paper describes our system that participated in the SemEval-2020 task 4: Commonsense Validation and Explanation. For this task, it is obvious that external knowledge, such as Knowledge graph, can help the model understand commonsense in natural language statements. But how to select the right triples for statements remains unsolved, so how to reduce the interference of irrelevant triples on model performance is a research focus. This paper adopt a modified K-BERT as the language encoder, to enhance language representation through triples from knowledge graphs. Experiments show that our method is better than models without external knowledge, and is slightly better than the original K-BERT. We got an accuracy score of 0.97 in subtaskA, ranking 1/45, and got an accuracy score of 0.948, ranking 2/35.
%R 10.18653/v1/2020.semeval-1.60
%U https://aclanthology.org/2020.semeval-1.60
%U https://doi.org/10.18653/v1/2020.semeval-1.60
%P 494-500

Markdown (Informal)

[CN-HIT-IT.NLP at SemEval-2020 Task 4: Enhanced Language Representation with Multiple Knowledge Triples](https://aclanthology.org/2020.semeval-1.60) (Zhang et al., SemEval 2020)

• CN-HIT-IT.NLP at SemEval-2020 Task 4: Enhanced Language Representation with Multiple Knowledge Triples (Zhang et al., SemEval 2020)

ACL

• Yice Zhang, Jiaxuan Lin, Yang Fan, Peng Jin, Yuanchao Liu, and Bingquan Liu. 2020. CN-HIT-IT.NLP at SemEval-2020 Task 4: Enhanced Language Representation with Multiple Knowledge Triples. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 494–500, Barcelona (online). International Committee for Computational Linguistics.