The successful analysis of argumentative techniques from user-generated text is central to many downstream tasks such as political and market analysis. Recent argument mining tools use state-of-the-art deep learning methods to extract and annotate argumentative techniques from various online text corpora, however each task is treated as separate and different bespoke models are fine-tuned for each dataset. We show that different argument mining tasks share common semantic and logical structure by implementing a multi-task approach to argument mining that achieves better performance than state-of-the-art methods for the same problems. Our model builds a shared representation of the input text that is common to all tasks and exploits similarities between tasks in order to further boost performance via parameter-sharing. Our results are important for argument mining as they show that different tasks share substantial similarities and suggest a holistic approach to the extraction of argumentative techniques from text.

翻译：从用户生成文本中成功分析论证技术对于政治分析、市场分析等诸多下游任务至关重要。当前的论点挖掘工具采用最先进的深度学习方法从各类在线文本语料库中提取并标注论证技术，然而每个任务被独立处理，且需针对不同数据集分别微调定制模型。本文通过实现多任务论点挖掘方法，证明不同论证挖掘任务共享共同的语义与逻辑结构，且该方法在相同问题上优于现有先进技术。我们的模型构建了所有任务共有的输入文本共享表征，并通过参数共享利用任务间相似性进一步提升性能。该研究成果对论点挖掘领域具有重要意义，揭示了不同任务之间存在显著相似性，并为从文本中提取论证技术提供了整体性思路。