@inproceedings{elango-uppal-2018-riddl,
title = "{RIDDL} at {S}em{E}val-2018 Task 1: Rage Intensity Detection with Deep Learning",
author = "Elango, Venkatesh and
Uppal, Karan",
booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S18-1054",
doi = "10.18653/v1/S18-1054",
pages = "358--363",
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.",
}
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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
%S Proceedings of The 12th International Workshop on Semantic Evaluation
%D 2018
%8 jun
%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)
ACL