uOttawa at SemEval-2018 Task 1: Self-Attentive Hybrid GRU-Based Network
Ahmed Husseini Orabi, Mahmoud Husseini Orabi, Diana Inkpen, David Van Bruwaene
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.- Anthology ID:
- S18-1027
- Volume:
- Proceedings of the 12th International Workshop on Semantic Evaluation
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Venues:
- SemEval | *SEM
- SIGs:
- SIGLEX | SIGSEM
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 181–185
- Language:
- URL:
- https://aclanthology.org/S18-1027
- DOI:
- 10.18653/v1/S18-1027
- Cite (ACL):
- Ahmed Husseini Orabi, Mahmoud Husseini Orabi, Diana Inkpen, and David Van Bruwaene. 2018. uOttawa at SemEval-2018 Task 1: Self-Attentive Hybrid GRU-Based Network. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 181–185, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- uOttawa at SemEval-2018 Task 1: Self-Attentive Hybrid GRU-Based Network (Husseini Orabi et al., SemEval-*SEM 2018)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/S18-1027.pdf