@inproceedings{gopalan-hopkins-2020-reed,
title = "Reed at {S}em{E}val-2020 Task 9: Fine-Tuning and Bag-of-Words Approaches to Code-Mixed Sentiment Analysis",
author = "Gopalan, Vinay and
Hopkins, Mark",
editor = "Herbelot, Aurelie and
Zhu, Xiaodan and
Palmer, Alexis and
Schneider, Nathan and
May, Jonathan and
Shutova, Ekaterina",
booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
month = dec,
year = "2020",
address = "Barcelona (online)",
publisher = "International Committee for Computational Linguistics",
url = "https://aclanthology.org/2020.semeval-1.176",
doi = "10.18653/v1/2020.semeval-1.176",
pages = "1304--1309",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Reed at SemEval-2020 Task 9: Fine-Tuning and Bag-of-Words Approaches to Code-Mixed Sentiment Analysis
%A Gopalan, Vinay
%A Hopkins, Mark
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y May, Jonathan
%Y Shutova, Ekaterina
%S Proceedings of the Fourteenth Workshop on Semantic Evaluation
%D 2020
%8 December
%I International Committee for Computational Linguistics
%C Barcelona (online)
%F gopalan-hopkins-2020-reed
%X 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.
%R 10.18653/v1/2020.semeval-1.176
%U https://aclanthology.org/2020.semeval-1.176
%U https://doi.org/10.18653/v1/2020.semeval-1.176
%P 1304-1309
Markdown (Informal)
[Reed at SemEval-2020 Task 9: Fine-Tuning and Bag-of-Words Approaches to Code-Mixed Sentiment Analysis](https://aclanthology.org/2020.semeval-1.176) (Gopalan & Hopkins, SemEval 2020)
ACL