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        <namePart type="given">Barbara</namePart>
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            <namePart type="given">Chao-Hong</namePart>
            <namePart type="family">Liu</namePart>
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        <name type="personal">
            <namePart type="given">Preslav</namePart>
            <namePart type="family">Nakov</namePart>
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        <name type="personal">
            <namePart type="given">Nianwen</namePart>
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    <abstract>We present All-In-1, a simple model for multilingual text classification that does not require any parallel data. It is based on a traditional Support Vector Machine classifier exploiting multilingual word embeddings and character n-grams. Our model is simple, easily extendable yet very effective, overall ranking 1st (out of 12 teams) in the IJCNLP 2017 shared task on customer feedback analysis in four languages: English, French, Japanese and Spanish.</abstract>
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%0 Conference Proceedings
%T All-In-1 at IJCNLP-2017 Task 4: Short Text Classification with One Model for All Languages
%A Plank, Barbara
%Y Liu, Chao-Hong
%Y Nakov, Preslav
%Y Xue, Nianwen
%S Proceedings of the IJCNLP 2017, Shared Tasks
%D 2017
%8 December
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F plank-2017-1
%X We present All-In-1, a simple model for multilingual text classification that does not require any parallel data. It is based on a traditional Support Vector Machine classifier exploiting multilingual word embeddings and character n-grams. Our model is simple, easily extendable yet very effective, overall ranking 1st (out of 12 teams) in the IJCNLP 2017 shared task on customer feedback analysis in four languages: English, French, Japanese and Spanish.
%U https://aclanthology.org/I17-4024/
%P 143-148

Markdown (Informal)

[All-In-1 at IJCNLP-2017 Task 4: Short Text Classification with One Model for All Languages](https://aclanthology.org/I17-4024/) (Plank, IJCNLP 2017)

• All-In-1 at IJCNLP-2017 Task 4: Short Text Classification with One Model for All Languages (Plank, IJCNLP 2017)

ACL

• Barbara Plank. 2017. All-In-1 at IJCNLP-2017 Task 4: Short Text Classification with One Model for All Languages. In Proceedings of the IJCNLP 2017, Shared Tasks, pages 143–148, Taipei, Taiwan. Asian Federation of Natural Language Processing.