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
- Editors:
- Jonathan May, Ekaterina Shutova, Aurelie Herbelot, Xiaodan Zhu, Marianna Apidianaki, Saif M. Mohammad
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 282–286
- Language:
- URL:
- https://aclanthology.org/S19-2048
- DOI:
- 10.18653/v1/S19-2048
- Cite (ACL):
- Peixiang Zhong and Chunyan Miao. 2019. ntuer at SemEval-2019 Task 3: Emotion Classification with Word and Sentence Representations in RCNN. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 282–286, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
- Cite (Informal):
- ntuer at SemEval-2019 Task 3: Emotion Classification with Word and Sentence Representations in RCNN (Zhong & Miao, SemEval 2019)
- PDF:
- https://preview.aclanthology.org/ingest-2024-clasp/S19-2048.pdf
- Code
- zhongpeixiang/SemEval2019-Task3-EmotionDetection