SuperTweetEval: A Challenging, Unified and Heterogeneous Benchmark for Social Media NLP Research

Dimosthenis Antypas, Asahi Ushio, Francesco Barbieri, Leonardo Neves, Kiamehr Rezaee, Luis Espinosa-Anke, Jiaxin Pei, Jose Camacho-Collados

Abstract
Despite its relevance, the maturity of NLP for social media pales in comparison with general-purpose models, metrics and benchmarks. This fragmented landscape makes it hard for the community to know, for instance, given a task, which is the best performing model and how it compares with others. To alleviate this issue, we introduce a unified benchmark for NLP evaluation in social media, SuperTweetEval, which includes a heterogeneous set of tasks and datasets combined, adapted and constructed from scratch. We benchmarked the performance of a wide range of models on SuperTweetEval and our results suggest that, despite the recent advances in language modelling, social media remains challenging.
Anthology ID:
2023.findings-emnlp.838
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2023
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
12590–12607
Language:
URL:
https://aclanthology.org/2023.findings-emnlp.838
DOI:
10.18653/v1/2023.findings-emnlp.838
Bibkey:
Cite (ACL):
Dimosthenis Antypas, Asahi Ushio, Francesco Barbieri, Leonardo Neves, Kiamehr Rezaee, Luis Espinosa-Anke, Jiaxin Pei, and Jose Camacho-Collados. 2023. SuperTweetEval: A Challenging, Unified and Heterogeneous Benchmark for Social Media NLP Research. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 12590–12607, Singapore. Association for Computational Linguistics.
Cite (Informal):
SuperTweetEval: A Challenging, Unified and Heterogeneous Benchmark for Social Media NLP Research (Antypas et al., Findings 2023)
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PDF:
https://preview.aclanthology.org/nschneid-patch-5/2023.findings-emnlp.838.pdf
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