DMCB at SemEval-2018 Task 1: Transfer Learning of Sentiment Classification Using Group LSTM for Emotion Intensity prediction

Youngmin Kim, Hyunju Lee


Abstract
This paper describes a system attended in the SemEval-2018 Task 1 “Affect in tweets” that predicts emotional intensities. We use Group LSTM with an attention model and transfer learning with sentiment classification data as a source data (SemEval 2017 Task 4a). A transfer model structure consists of a source domain and a target domain. Additionally, we try a new dropout that is applied to LSTMs in the Group LSTM. Our system ranked 8th at the subtask 1a (emotion intensity regression). We also show various results with different architectures in the source, target and transfer models.
Anthology ID:
S18-1044
Volume:
Proceedings of The 12th International Workshop on Semantic Evaluation
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
300–304
Language:
URL:
https://www.aclweb.org/anthology/S18-1044
DOI:
10.18653/v1/S18-1044
Bib Export formats:
BibTeX MODS XML EndNote
PDF:
http://aclanthology.lst.uni-saarland.de/S18-1044.pdf