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        <title>RIDDL at SemEval-2018 Task 1: Rage Intensity Detection with Deep Learning</title>
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    <name type="personal">
        <namePart type="given">Venkatesh</namePart>
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            <namePart type="given">Marianna</namePart>
            <namePart type="family">Apidianaki</namePart>
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            <namePart type="given">Saif</namePart>
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            <namePart type="given">Jonathan</namePart>
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    <abstract>We present our methods and results for affect analysis in Twitter developed as a part of SemEval-2018 Task 1, where the sub-tasks involve predicting the intensity of emotion, the intensity of sentiment, and valence for tweets. For modeling, though we use a traditional LSTM network, we combine our model with several state-of-the-art techniques to improve its performance in a low-resource setting. For example, we use an encoder-decoder network to initialize the LSTM weights. Without any task specific optimization we achieve competitive results (macro-average Pearson correlation coefficient 0.696) in the El-reg task. In this paper, we describe our development strategy in detail along with an exposition of our results.</abstract>
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%0 Conference Proceedings
%T RIDDL at SemEval-2018 Task 1: Rage Intensity Detection with Deep Learning
%A Elango, Venkatesh
%A Uppal, Karan
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Bethard, Steven
%Y Carpuat, Marine
%S Proceedings of the 12th International Workshop on Semantic Evaluation
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F elango-uppal-2018-riddl
%X We present our methods and results for affect analysis in Twitter developed as a part of SemEval-2018 Task 1, where the sub-tasks involve predicting the intensity of emotion, the intensity of sentiment, and valence for tweets. For modeling, though we use a traditional LSTM network, we combine our model with several state-of-the-art techniques to improve its performance in a low-resource setting. For example, we use an encoder-decoder network to initialize the LSTM weights. Without any task specific optimization we achieve competitive results (macro-average Pearson correlation coefficient 0.696) in the El-reg task. In this paper, we describe our development strategy in detail along with an exposition of our results.
%R 10.18653/v1/S18-1054
%U https://aclanthology.org/S18-1054
%U https://doi.org/10.18653/v1/S18-1054
%P 358-363

Markdown (Informal)

[RIDDL at SemEval-2018 Task 1: Rage Intensity Detection with Deep Learning](https://aclanthology.org/S18-1054) (Elango & Uppal, SemEval 2018)

• RIDDL at SemEval-2018 Task 1: Rage Intensity Detection with Deep Learning (Elango & Uppal, SemEval 2018)

ACL

• Venkatesh Elango and Karan Uppal. 2018. RIDDL at SemEval-2018 Task 1: Rage Intensity Detection with Deep Learning. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 358–363, New Orleans, Louisiana. Association for Computational Linguistics.