@inproceedings{nyegaard-signori-etal-2018-ku,
title = "{KU}-{MTL} at {S}em{E}val-2018 Task 1: Multi-task Identification of Affect in Tweets",
author = "Nyegaard-Signori, Thomas and
Helms, Casper Veistrup and
Bjerva, Johannes and
Augenstein, Isabelle",
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/S18-1058",
doi = "10.18653/v1/S18-1058",
pages = "385--389",
abstract = "We take a multi-task learning approach to the shared Task 1 at SemEval-2018. The general idea concerning the model structure is to use as little external data as possible in order to preserve the task relatedness and reduce complexity. We employ multi-task learning with hard parameter sharing to exploit the relatedness between sub-tasks. As a base model, we use a standard recurrent neural network for both the classification and regression subtasks. Our system ranks 32nd out of 48 participants with a Pearson score of 0.557 in the first subtask, and 20th out of 35 in the fifth subtask with an accuracy score of 0.464.",
}
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%0 Conference Proceedings
%T KU-MTL at SemEval-2018 Task 1: Multi-task Identification of Affect in Tweets
%A Nyegaard-Signori, Thomas
%A Helms, Casper Veistrup
%A Bjerva, Johannes
%A Augenstein, Isabelle
%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 nyegaard-signori-etal-2018-ku
%X We take a multi-task learning approach to the shared Task 1 at SemEval-2018. The general idea concerning the model structure is to use as little external data as possible in order to preserve the task relatedness and reduce complexity. We employ multi-task learning with hard parameter sharing to exploit the relatedness between sub-tasks. As a base model, we use a standard recurrent neural network for both the classification and regression subtasks. Our system ranks 32nd out of 48 participants with a Pearson score of 0.557 in the first subtask, and 20th out of 35 in the fifth subtask with an accuracy score of 0.464.
%R 10.18653/v1/S18-1058
%U https://aclanthology.org/S18-1058
%U https://doi.org/10.18653/v1/S18-1058
%P 385-389
Markdown (Informal)
[KU-MTL at SemEval-2018 Task 1: Multi-task Identification of Affect in Tweets](https://aclanthology.org/S18-1058) (Nyegaard-Signori et al., SemEval 2018)
ACL