Hashing it Out：预测推特上的不健康对话
Hashing it Out: Predicting Unhealthy Conversations on Twitter
AI总结：
本文提出基于注意力的BERT模型，在推特语料上预训练并微调，用于预测对话脱轨，性能优于LSTM，且通过合成过采样在小数据集上有效。
摘要：
在社交媒体对话的背景下，人身攻击往往导致对话快速脱轨，进而引发更多有害的交流。用于检测此类对话脱轨的最先进系统通常采用深度学习方法进行预测。在本文中，我们展示了一种基于注意力的BERT架构，该架构在大型推特语料库上进行预训练，并在我们的任务上进行微调，能够高效且有效地进行此类预测。该模型在性能上明显优于我们用作基线的现有LSTM模型。此外，我们表明，通过在一个相对较小且新颖的数据集上进行微调，特别是在通过合成过采样技术缓解过拟合问题之后，可以达到这种令人印象深刻的性能。通过引入第一个基于Transformer的模型来预测推特上的对话事件，这项工作为在最具普遍性的社交媒体平台之一上鼓励更好互动提供了实用工具的基础。
英文摘要：
Personal attacks in the context of social media conversations often lead to fast-paced derailment, leading to even more harmful exchanges being made. State-of-the-art systems for the detection of such conversational derailment often make use of deep learning approaches for prediction purposes. In this paper, we show that an Attention-based BERT architecture, pre-trained on a large Twitter corpus and fine-tuned on our task, is efficient and effective in making such predictions. This model shows clear advantages in performance to the existing LSTM model we use as a baseline. Additionally, we show that this impressive performance can be attained through fine-tuning on a relatively small, novel dataset, particularly after mitigating overfitting issues through synthetic oversampling techniques. By introducing the first transformer based model for forecasting conversational events on Twitter, this work lays the foundation for a practical tool to encourage better interactions on one of the most ubiquitous social media platforms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
