YNU-HPCC at SemEval-2020 Task 8: Using a Parallel-Channel Model for Memotion Analysis

Li Yuan, Jin Wang, Xuejie Zhang


Abstract
this paper proposed a parallel-channel model to process the textual and visual information in memes and then analyze the sentiment polarity of memes. In the shared task of identifying and categorizing memes, we preprocess the dataset according to the language behaviors on social media. Then, we adapt and fine-tune the Bidirectional Encoder Representations from Transformers (BERT), and two types of convolutional neural network models (CNNs) were used to extract the features from the pictures. We applied an ensemble model that combined the BiLSTM, BIGRU, and Attention models to perform cross domain suggestion mining. The officially released results show that our system performs better than the baseline algorithm
Anthology ID:
2020.semeval-1.116
Volume:
Proceedings of the Fourteenth Workshop on Semantic Evaluation
Month:
December
Year:
2020
Address:
Barcelona (online)
Editors:
Aurelie Herbelot, Xiaodan Zhu, Alexis Palmer, Nathan Schneider, Jonathan May, Ekaterina Shutova
Venue:
SemEval
SIG:
SIGLEX
Publisher:
International Committee for Computational Linguistics
Note:
Pages:
916–921
Language:
URL:
https://aclanthology.org/2020.semeval-1.116
DOI:
10.18653/v1/2020.semeval-1.116
Bibkey:
Cite (ACL):
Li Yuan, Jin Wang, and Xuejie Zhang. 2020. YNU-HPCC at SemEval-2020 Task 8: Using a Parallel-Channel Model for Memotion Analysis. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 916–921, Barcelona (online). International Committee for Computational Linguistics.
Cite (Informal):
YNU-HPCC at SemEval-2020 Task 8: Using a Parallel-Channel Model for Memotion Analysis (Yuan et al., SemEval 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.semeval-1.116.pdf
Code
 YuanLi95/Semveal2020-Task8-emotion-analysis