Multi-Task Learning Improves Performance In Deep Argument Mining Models

• URL: http://arxiv.org/abs/2307.01401v1
• Date: Mon, 3 Jul 2023 23:42:29 GMT
• Title: Multi-Task Learning Improves Performance In Deep Argument Mining Models
• Authors: Amirhossein Farzam, Shashank Shekhar, Isaac Mehlhaff, Marco Morucci
• Abstract summary: We show that different argument mining tasks share common semantic and logical structure by implementing a multi-task approach to argument mining. 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.
• Score: 2.2312474084968024
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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