RIDDL at SemEval-2018 Task 1: Rage Intensity Detection with Deep Learning

Venkatesh Elango, Karan Uppal


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.
Anthology ID:
S18-1054
Volume:
Proceedings of The 12th International Workshop on Semantic Evaluation
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
358–363
Language:
URL:
https://www.aclweb.org/anthology/S18-1054
DOI:
10.18653/v1/S18-1054
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S18-1054.pdf