Voice@SRIB at SemEval-2020 Tasks 9 and 12: Stacked Ensemblingmethod for Sentiment and Offensiveness detection in Social Media

Abhishek Singh, Surya Pratap Singh Parmar


Abstract
In social-media platforms such as Twitter, Facebook, and Reddit, people prefer to use code-mixed language such as Spanish-English, Hindi-English to express their opinions. In this paper, we describe different models we used, using the external dataset to train embeddings, ensembling methods for Sentimix, and OffensEval tasks. The use of pre-trained embeddings usually helps in multiple tasks such as sentence classification, and machine translation. In this experiment, we have used our trained code-mixed embeddings and twitter pre-trained embeddings to SemEval tasks. We evaluate our models on macro F1-score, precision, accuracy, and recall on the datasets. We intend to show that hyper-parameter tuning and data pre-processing steps help a lot in improving the scores. In our experiments, we are able to achieve 0.886 F1-Macro on OffenEval Greek language subtask post-evaluation, whereas the highest is 0.852 during the Evaluation Period. We stood third in Spanglish competition with our best F1-score of 0.756. Codalab username is asking28.
Anthology ID:
2020.semeval-1.180
Volume:
Proceedings of the Fourteenth Workshop on Semantic Evaluation
Month:
December
Year:
2020
Address:
Barcelona (online)
Venues:
*SEMEVAL | COLING
SIG:
SIGLEX
Publisher:
International Committee for Computational Linguistics
Note:
Pages:
1331–1341
Language:
URL:
https://www.aclweb.org/anthology/2020.semeval-1.180
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.semeval-1.180.pdf