Multi-Task Learning Improves Performance In Deep Argument Mining Models
Amirhossein Farzam Shashank Shekhar Isaac Mehlhaff Marco Morucci
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.