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Tweeteval: Unified Benchmark And Comparative Evaluation For Tweet Classification

Francesco Barbieri, Jose Camacho-Collados, Leonardo Neves, Luis Espinosa-Anke . Findings of the Association for Computational Linguistics: EMNLP 2020 2020 – 499 citations

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ACL Affective Computing Compositional Generalization Content Enrichment Datasets EMNLP Evaluation Image Text Integration Interactive Environments Interdisciplinary Approaches Multimodal Semantic Representation Neural Machine Translation Productivity Enhancement Question Answering Tools Training Techniques Visual Contextualization

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.

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