@inproceedings{husseini-orabi-etal-2018-uottawa,
title = "u{O}ttawa at {S}em{E}val-2018 Task 1: Self-Attentive Hybrid {GRU}-Based Network",
author = "Husseini Orabi, Ahmed and
Husseini Orabi, Mahmoud and
Inkpen, Diana and
Van Bruwaene, David",
editor = "Apidianaki, Marianna and
Mohammad, Saif M. and
May, Jonathan and
Shutova, Ekaterina and
Bethard, Steven and
Carpuat, Marine",
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/S18-1027",
doi = "10.18653/v1/S18-1027",
pages = "181--185",
abstract = "We propose a novel attentive hybrid GRU-based network (SAHGN), which we used at SemEval-2018 Task 1: Affect in Tweets. Our network has two main characteristics, 1) has the ability to internally optimize its feature representation using attention mechanisms, and 2) provides a hybrid representation using a character level Convolutional Neural Network (CNN), as well as a self-attentive word-level encoder. The key advantage of our model is its ability to signify the relevant and important information that enables self-optimization. Results are reported on the valence intensity regression task.",
}
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%0 Conference Proceedings
%T uOttawa at SemEval-2018 Task 1: Self-Attentive Hybrid GRU-Based Network
%A Husseini Orabi, Ahmed
%A Husseini Orabi, Mahmoud
%A Inkpen, Diana
%A Van Bruwaene, David
%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 husseini-orabi-etal-2018-uottawa
%X We propose a novel attentive hybrid GRU-based network (SAHGN), which we used at SemEval-2018 Task 1: Affect in Tweets. Our network has two main characteristics, 1) has the ability to internally optimize its feature representation using attention mechanisms, and 2) provides a hybrid representation using a character level Convolutional Neural Network (CNN), as well as a self-attentive word-level encoder. The key advantage of our model is its ability to signify the relevant and important information that enables self-optimization. Results are reported on the valence intensity regression task.
%R 10.18653/v1/S18-1027
%U https://aclanthology.org/S18-1027
%U https://doi.org/10.18653/v1/S18-1027
%P 181-185
Markdown (Informal)
[uOttawa at SemEval-2018 Task 1: Self-Attentive Hybrid GRU-Based Network](https://aclanthology.org/S18-1027) (Husseini Orabi et al., SemEval 2018)
ACL