Multi-Task Learning Improves Performance In Deep Argument Mining Models
This improves argument mining for political and market analysis by showing task similarities, but it is incremental as it builds on existing deep learning methods.
The paper tackled the problem of separate models for different argument mining tasks by implementing a multi-task learning approach, achieving better performance than state-of-the-art methods.
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.