文章基本信息

标题：Towards Emotion Recognition inHindi-English Code-Mixed Data: A Transformer Based Approach
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作者：Anshul Wadhawan ; Akshita Aggarwal
期刊名称：Conference on European Chapter of the Association for Computational Linguistics (EACL)
出版年度：2021
卷号：2021
页码：195-202
语种：English
出版社：ACL Anthology
摘要：In the last few years, emotion detection in social-media text has become a popular problem due to its wide ranging application in better understanding the consumers, in psychology, in aiding human interaction with computers, designing smart systems etc. Because of the availability of huge amounts of data from social-media, which is regularly used for expressing sentiments and opinions, this problem has garnered great attention. In this paper, we present a Hinglish dataset labelled for emotion detection. We highlight a deep learning based approach for detecting emotions using bilingual word embeddings derived from FastText and Word2Vec approaches in Hindi-English code mixed tweets. We experiment with various deep learning models, including CNNs, LSTMs, Bi-directional LSTMs (with and without attention), along with transformers like BERT, RoBERTa, and ALBERT. The transformer based BERT model outperforms all current state-of-the-art models giving the best performance with an accuracy of 71.43%.