@inproceedings{rozental-etal-2018-amobee,
title = "{A}mobee at {IEST} 2018: Transfer Learning from Language Models",
author = "Rozental, Alon and
Fleischer, Daniel and
Kelrich, Zohar",
editor = "Balahur, Alexandra and
Mohammad, Saif M. and
Hoste, Veronique and
Klinger, Roman",
booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
month = oct,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-6207",
doi = "10.18653/v1/W18-6207",
pages = "43--49",
abstract = "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 emotions. Our system reached 1st place with a macro F1 score of 0.7145.",
}
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%0 Conference Proceedings
%T Amobee at IEST 2018: Transfer Learning from Language Models
%A Rozental, Alon
%A Fleischer, Daniel
%A Kelrich, Zohar
%Y Balahur, Alexandra
%Y Mohammad, Saif M.
%Y Hoste, Veronique
%Y Klinger, Roman
%S Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F rozental-etal-2018-amobee
%X 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 emotions. Our system reached 1st place with a macro F1 score of 0.7145.
%R 10.18653/v1/W18-6207
%U https://aclanthology.org/W18-6207
%U https://doi.org/10.18653/v1/W18-6207
%P 43-49
Markdown (Informal)
[Amobee at IEST 2018: Transfer Learning from Language Models](https://aclanthology.org/W18-6207) (Rozental et al., WASSA 2018)
ACL
- Alon Rozental, Daniel Fleischer, and Zohar Kelrich. 2018. Amobee at IEST 2018: Transfer Learning from Language Models. In Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 43–49, Brussels, Belgium. Association for Computational Linguistics.