| Challenge: | Social media data exhibits severe redundancy due to its noisy nature, leading to increased training time and model bias in its processing. |
| Approach: | They propose a new framework for deduplication of social media data by removing semantically duplicate data from the model and add time-dimensional Gaussian noise to reduce training complexity. |
| Outcome: | The proposed framework can reduce training samples while improving performance over baselines. |
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| Challenge: | Social media data exhibits distinctive characteristics such as rapid and continual topic evolution. |
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SuperTweetEval: A Challenging, Unified and Heterogeneous Benchmark for Social Media NLP Research (2023.findings-emnlp)
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Dimosthenis Antypas, Asahi Ushio, Francesco Barbieri, Leonardo Neves, Kiamehr Rezaee, Luis Espinosa-Anke, Jiaxin Pei, Jose Camacho-Collados
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| Challenge: | Existing methods to encourage lexical diversity for language generation tasks produce repetitive outputs, but this often comes at a cost to the perceived fluency and adequacy of the output. |
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