ntuer at SemEval-2019 Task 3: Emotion Classification with Word and Sentence Representations in RCNN

Peixiang Zhong, Chunyan Miao


Abstract
In this paper we present our model on the task of emotion detection in textual conversations in SemEval-2019. Our model extends the Recurrent Convolutional Neural Network (RCNN) by using external fine-tuned word representations and DeepMoji sentence representations. We also explored several other competitive pre-trained word and sentence representations including ELMo, BERT and InferSent but found inferior performance. In addition, we conducted extensive sensitivity analysis, which empirically shows that our model is relatively robust to hyper-parameters. Our model requires no handcrafted features or emotion lexicons but achieved good performance with a micro-F1 score of 0.7463.
Anthology ID:
S19-2048
Volume:
Proceedings of the 13th International Workshop on Semantic Evaluation
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota, USA
Venue:
*SEMEVAL
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
282–286
Language:
URL:
https://www.aclweb.org/anthology/S19-2048
DOI:
10.18653/v1/S19-2048
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PDF:
http://aclanthology.lst.uni-saarland.de/S19-2048.pdf