论文标题
通过变压器网络在社交媒体中的投诉标识
Complaint Identification in Social Media with Transformer Networks
论文作者
论文摘要
抱怨是人类广泛使用的言论行为，以传达现实与期望之间的负面不一致。以前关于自动识别社交媒体投诉的工作重点是使用基于功能和特定任务的神经网络模型。调整最先进的预训练的神经语言模型及其与来自主题或情感的其他语言信息的组合以进行投诉预测。在本文中，我们评估了由变压器网络支撑的一系列神经模型，随后我们将其与语言信息结合在一起。对公开数据集的实验表明，我们的模型通过大幅度实现高达87的宏F1的优势优于先前的最先进方法。
Complaining is a speech act extensively used by humans to communicate a negative inconsistency between reality and expectations. Previous work on automatically identifying complaints in social media has focused on using feature-based and task-specific neural network models. Adapting state-of-the-art pre-trained neural language models and their combinations with other linguistic information from topics or sentiment for complaint prediction has yet to be explored. In this paper, we evaluate a battery of neural models underpinned by transformer networks which we subsequently combine with linguistic information. Experiments on a publicly available data set of complaints demonstrate that our models outperform previous state-of-the-art methods by a large margin achieving a macro F1 up to 87.
