Summary of A Generative Marker Enhanced End-to-end Framework For Argument Mining, by Nilmadhab Das et al.
A Generative Marker Enhanced End-to-End Framework for Argument Mining
by Nilmadhab Das, Vishal Choudhary, V. Vijaya Saradhi, Ashish Anand
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel generative paradigm-based framework called argTANL for Argument Mining (AM). This end-to-end framework extracts both Argumentative Components (ACs) and their corresponding Argumentative Relations (ARs) from given argumentative texts. The authors also explore the impact of Argumentative and Discourse markers on enhancing the model’s performance within this framework. Two distinct frameworks, Marker-Enhanced argTANL (ME-argTANL) and argTANL with specialized Marker-Based Fine-Tuning, are proposed to achieve this. Extensive experiments are conducted on three standard AM benchmarks to demonstrate the superior performance of ME-argTANL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to analyze text called Argument Mining (AM). It’s like searching for clues in an argument to understand what people are saying. The researchers developed a special framework, argTANL, that can find these clues and understand how they relate to each other. They also tried adding special markers to the framework to make it better at finding these clues. They tested their new approach on three different tests to see if it worked well. |
Keywords
» Artificial intelligence » Discourse » Fine tuning
