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Amobee at IEST 2018: Transfer Learning from Language Models
2018
Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
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This paper describes the system developed at Amobee for the WASSA 2018 implicit emotions shared task (IEST). The goal of this task was to predict the emotion expressed by missing words in tweets without an explicit mention of those words. We developed an ensemble system consisting of language models together with LSTM-based networks containing a CNN attention mechanism. Our approach represents a novel use of language models-specifically trained on a large Twitter dataset-to predict and classify
doi:10.18653/v1/w18-6207
dblp:conf/wassa/RozentalFK18
fatcat:xelmrahk4jajbdstzi2wcnrwn4