We explore the use of residual networks and neural attention for multiple argument mining tasks. We propose a residual architecture that exploits attention, multi-task learning, and makes use of ensemble, without any assumption on document or argument structure. We present an extensive experimental evaluation on five different corpora of user-generated comments, scientific publications, and persuasive essays. Our results show that our approach is a strong competitor against state-of-the-art architectures with a higher computational footprint or corpus-specific design, representing an interesting compromise between generality, performance accuracy and reduced model size.

翻译：我们探索了残差网络与神经注意力机制在多个论点挖掘任务中的应用。提出了一种残差架构，该架构融合了注意力机制、多任务学习，并采用集成方法，且不对文档或论点结构作任何假设。我们在五个不同语料库上进行了广泛的实验评估，这些语料涵盖用户生成评论、科学出版物以及说服性论文。结果表明，我们的方法在与计算开销更高或针对特定语料设计的先进架构的竞争中表现出强劲实力，在通用性、性能准确性和模型精简之间实现了有意义的平衡。