TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification

Francesco Barbieri, Jose Camacho-Collados, Luis Espinosa Anke, Leonardo Neves

Abstract
The experimental landscape in natural language processing for social media is too fragmented. Each year, new shared tasks and datasets are proposed, ranging from classics like sentiment analysis to irony detection or emoji prediction. Therefore, it is unclear what the current state of the art is, as there is no standardized evaluation protocol, neither a strong set of baselines trained on such domain-specific data. In this paper, we propose a new evaluation framework (TweetEval) consisting of seven heterogeneous Twitter-specific classification tasks. We also provide a strong set of baselines as starting point, and compare different language modeling pre-training strategies. Our initial experiments show the effectiveness of starting off with existing pre-trained generic language models, and continue training them on Twitter corpora.
Anthology ID:
2020.findings-emnlp.148
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Editors:
Trevor Cohn, Yulan He, Yang Liu
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1644–1650
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.148
DOI:
10.18653/v1/2020.findings-emnlp.148
Bibkey:
Cite (ACL):
Francesco Barbieri, Jose Camacho-Collados, Luis Espinosa Anke, and Leonardo Neves. 2020. TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1644–1650, Online. Association for Computational Linguistics.
Cite (Informal):
TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification (Barbieri et al., Findings 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/naacl24-info/2020.findings-emnlp.148.pdf
Code
 cardiffnlp/tweeteval +  additional community code
Data
TweetEval, GLUE, SuperGLUE
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