uOttawa at SemEval-2018 Task 1: Self-Attentive Hybrid GRU-Based Network
Ahmed Husseini Orabi, Mahmoud Husseini Orabi, Diana Zaiu Inkpen, David Van Bruwaene · 2018
We propose a novel attentive hybrid GRUbased 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.