KU-MTL at SemEval-2018 Task 1: Multi-task Identification of Affect in Tweets


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.

In Proceedings of the 2018 Proceedings of the International Workshop on Semantic Evaluation at the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.