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    <titleInfo>
        <title>A Neural Network Approach to Create Minangkabau-Indonesia Bilingual Dictionary</title>
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    <name type="personal">
        <namePart type="given">Kartika</namePart>
        <namePart type="family">Resiandi</namePart>
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    <name type="personal">
        <namePart type="given">Yohei</namePart>
        <namePart type="family">Murakami</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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    <name type="personal">
        <namePart type="given">Arbi</namePart>
        <namePart type="given">Haza</namePart>
        <namePart type="family">Nasution</namePart>
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        <dateIssued>2022-06</dateIssued>
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            <title>Proceedings of the 1st Annual Meeting of the ELRA/ISCA Special Interest Group on Under-Resourced Languages</title>
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        <name type="personal">
            <namePart type="given">Maite</namePart>
            <namePart type="family">Melero</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Sakriani</namePart>
            <namePart type="family">Sakti</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Claudia</namePart>
            <namePart type="family">Soria</namePart>
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            <publisher>European Language Resources Association</publisher>
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    <abstract>Indonesia has many varieties of ethnic languages, and most come from the same language family, namely Austronesian languages. Coming from that same language family, the words in Indonesian ethnic languages are very similar. However, there is research stating that Indonesian ethnic languages are endangered. Thus, to prevent that, we proposed to create a bilingual dictionary between ethnic languages using a neural network approach to extract transformation rules using character level embedding and the Bi-LSTM method in a sequence-to-sequence model. The model has an encoder and decoder. The encoder functions read the input sequence, character by character, generate context, then extract a summary of the input. The decoder will produce an output sequence where every character in each time-step and the next character that comes out are affected by the previous character. The current case for experiment translation focuses on Minangkabau and Indonesian languages with 13761-word pairs. For evaluating the model’s performance, 5-Fold Cross-Validation is used.</abstract>
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        <url>https://aclanthology.org/2022.sigul-1.16</url>
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        <date>2022-06</date>
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            <start>122</start>
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%0 Conference Proceedings
%T A Neural Network Approach to Create Minangkabau-Indonesia Bilingual Dictionary
%A Resiandi, Kartika
%A Murakami, Yohei
%A Nasution, Arbi Haza
%Y Melero, Maite
%Y Sakti, Sakriani
%Y Soria, Claudia
%S Proceedings of the 1st Annual Meeting of the ELRA/ISCA Special Interest Group on Under-Resourced Languages
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F resiandi-etal-2022-neural
%X Indonesia has many varieties of ethnic languages, and most come from the same language family, namely Austronesian languages. Coming from that same language family, the words in Indonesian ethnic languages are very similar. However, there is research stating that Indonesian ethnic languages are endangered. Thus, to prevent that, we proposed to create a bilingual dictionary between ethnic languages using a neural network approach to extract transformation rules using character level embedding and the Bi-LSTM method in a sequence-to-sequence model. The model has an encoder and decoder. The encoder functions read the input sequence, character by character, generate context, then extract a summary of the input. The decoder will produce an output sequence where every character in each time-step and the next character that comes out are affected by the previous character. The current case for experiment translation focuses on Minangkabau and Indonesian languages with 13761-word pairs. For evaluating the model’s performance, 5-Fold Cross-Validation is used.
%U https://aclanthology.org/2022.sigul-1.16
%P 122-128

Markdown (Informal)

[A Neural Network Approach to Create Minangkabau-Indonesia Bilingual Dictionary](https://aclanthology.org/2022.sigul-1.16) (Resiandi et al., SIGUL 2022)

• A Neural Network Approach to Create Minangkabau-Indonesia Bilingual Dictionary (Resiandi et al., SIGUL 2022)

ACL

• Kartika Resiandi, Yohei Murakami, and Arbi Haza Nasution. 2022. A Neural Network Approach to Create Minangkabau-Indonesia Bilingual Dictionary. In Proceedings of the 1st Annual Meeting of the ELRA/ISCA Special Interest Group on Under-Resourced Languages, pages 122–128, Marseille, France. European Language Resources Association.