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        <title>GL at SemEval-2019 Task 5: Identifying hateful tweets with a deep learning approach.</title>
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    <name type="personal">
        <namePart type="given">Gretel</namePart>
        <namePart type="given">Liz</namePart>
        <namePart type="family">De la Peña</namePart>
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            <title>Proceedings of the 13th International Workshop on Semantic Evaluation</title>
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            <namePart type="given">Jonathan</namePart>
            <namePart type="family">May</namePart>
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        <name type="personal">
            <namePart type="given">Ekaterina</namePart>
            <namePart type="family">Shutova</namePart>
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        <name type="personal">
            <namePart type="given">Aurelie</namePart>
            <namePart type="family">Herbelot</namePart>
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        <name type="personal">
            <namePart type="given">Xiaodan</namePart>
            <namePart type="family">Zhu</namePart>
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        <name type="personal">
            <namePart type="given">Marianna</namePart>
            <namePart type="family">Apidianaki</namePart>
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            <namePart type="given">Saif</namePart>
            <namePart type="given">M</namePart>
            <namePart type="family">Mohammad</namePart>
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    <abstract>This paper describes the system we developed for SemEval 2019 on Multilingual detection of hate speech against immigrants and women in Twitter (HatEval - Task 5). We use an approach based on an Attention-based Long Short-Term Memory Recurrent Neural Network. In particular, we build a Bidirectional LSTM to extract information from the word embeddings over the sentence, then apply attention over the hidden states to estimate the importance of each word and finally feed this context vector to another LSTM model to get a representation. Then, the output obtained with this model is used to get the prediction of each of the sub-tasks.</abstract>
    <identifier type="citekey">de-la-pena-2019-gl</identifier>
    <identifier type="doi">10.18653/v1/S19-2073</identifier>
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        <date>2019-06</date>
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%0 Conference Proceedings
%T GL at SemEval-2019 Task 5: Identifying hateful tweets with a deep learning approach.
%A De la Peña, Gretel Liz
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%S Proceedings of the 13th International Workshop on Semantic Evaluation
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota, USA
%F de-la-pena-2019-gl
%X This paper describes the system we developed for SemEval 2019 on Multilingual detection of hate speech against immigrants and women in Twitter (HatEval - Task 5). We use an approach based on an Attention-based Long Short-Term Memory Recurrent Neural Network. In particular, we build a Bidirectional LSTM to extract information from the word embeddings over the sentence, then apply attention over the hidden states to estimate the importance of each word and finally feed this context vector to another LSTM model to get a representation. Then, the output obtained with this model is used to get the prediction of each of the sub-tasks.
%R 10.18653/v1/S19-2073
%U https://aclanthology.org/S19-2073
%U https://doi.org/10.18653/v1/S19-2073
%P 416-419

Markdown (Informal)

[GL at SemEval-2019 Task 5: Identifying hateful tweets with a deep learning approach.](https://aclanthology.org/S19-2073) (De la Peña, SemEval 2019)

• GL at SemEval-2019 Task 5: Identifying hateful tweets with a deep learning approach. (De la Peña, SemEval 2019)

ACL

• Gretel Liz De la Peña. 2019. GL at SemEval-2019 Task 5: Identifying hateful tweets with a deep learning approach.. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 416–419, Minneapolis, Minnesota, USA. Association for Computational Linguistics.