Improving Sentiment Analysis over non-English Tweets using Multilingual Transformers and Automatic Translation for Data-Augmentation

Valentin Barriere, Alexandra Balahur


Abstract
Tweets are specific text data when compared to general text. Although sentiment analysis over tweets has become very popular in the last decade for English, it is still difficult to find huge annotated corpora for non-English languages. The recent rise of the transformer models in Natural Language Processing allows to achieve unparalleled performances in many tasks, but these models need a consequent quantity of text to adapt to the tweet domain. We propose the use of a multilingual transformer model, that we pre-train over English tweets on which we apply data-augmentation using automatic translation to adapt the model to non-English languages. Our experiments in French, Spanish, German and Italian suggest that the proposed technique is an efficient way to improve the results of the transformers over small corpora of tweets in a non-English language.
Anthology ID:
2020.coling-main.23
Original:
2020.coling-main.23v1
Version 2:
2020.coling-main.23v2
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Editors:
Donia Scott, Nuria Bel, Chengqing Zong
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
266–271
Language:
URL:
https://aclanthology.org/2020.coling-main.23
DOI:
10.18653/v1/2020.coling-main.23
Bibkey:
Cite (ACL):
Valentin Barriere and Alexandra Balahur. 2020. Improving Sentiment Analysis over non-English Tweets using Multilingual Transformers and Automatic Translation for Data-Augmentation. In Proceedings of the 28th International Conference on Computational Linguistics, pages 266–271, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
Improving Sentiment Analysis over non-English Tweets using Multilingual Transformers and Automatic Translation for Data-Augmentation (Barriere & Balahur, COLING 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.coling-main.23.pdf
Data
SB10k