Reed at SemEval-2020 Task 9: Fine-Tuning and Bag-of-Words Approaches to Code-Mixed Sentiment Analysis

Vinay Gopalan, Mark Hopkins


Abstract
We explore the task of sentiment analysis on Hinglish (code-mixed Hindi-English) tweets as participants of Task 9 of the SemEval-2020 competition, known as the SentiMix task. We had two main approaches: 1) applying transfer learning by fine-tuning pre-trained BERT models and 2) training feedforward neural networks on bag-of-words representations. During the evaluation phase of the competition, we obtained an F-score of 71.3% with our best model, which placed 4th out of 62 entries in the official system rankings.
Anthology ID:
2020.semeval-1.176
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:
1304–1309
Language:
URL:
https://aclanthology.org/2020.semeval-1.176
DOI:
10.18653/v1/2020.semeval-1.176
Bibkey:
Cite (ACL):
Vinay Gopalan and Mark Hopkins. 2020. Reed at SemEval-2020 Task 9: Fine-Tuning and Bag-of-Words Approaches to Code-Mixed Sentiment Analysis. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1304–1309, Barcelona (online). International Committee for Computational Linguistics.
Cite (Informal):
Reed at SemEval-2020 Task 9: Fine-Tuning and Bag-of-Words Approaches to Code-Mixed Sentiment Analysis (Gopalan & Hopkins, SemEval 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.semeval-1.176.pdf
Data
GLUESentiMix