AttnConvnet at SemEval-2018 Task 1: Attention-based Convolutional Neural Networks for Multi-label Emotion Classification

Yanghoon Kim, Hwanhee Lee, Kyomin Jung


Abstract
In this paper, we propose an attention-based classifier that predicts multiple emotions of a given sentence. Our model imitates human’s two-step procedure of sentence understanding and it can effectively represent and classify sentences. With emoji-to-meaning preprocessing and extra lexicon utilization, we further improve the model performance. We train and evaluate our model with data provided by SemEval-2018 task 1-5, each sentence of which has several labels among 11 given emotions. Our model achieves 5th/1st rank in English/Spanish respectively.
Anthology ID:
S18-1019
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:
141–145
Language:
URL:
https://aclanthology.org/S18-1019
DOI:
10.18653/v1/S18-1019
Bibkey:
Cite (ACL):
Yanghoon Kim, Hwanhee Lee, and Kyomin Jung. 2018. AttnConvnet at SemEval-2018 Task 1: Attention-based Convolutional Neural Networks for Multi-label Emotion Classification. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 141–145, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
AttnConvnet at SemEval-2018 Task 1: Attention-based Convolutional Neural Networks for Multi-label Emotion Classification (Kim et al., SemEval-*SEM 2018)
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PDF:
https://aclanthology.org/S18-1019.pdf