Summary of Dmon: a Simple Yet Effective Approach For Argument Structure Learning, by Wei Sun et al.
DMON: A Simple yet Effective Approach for Argument Structure Learning
by Wei Sun, Mingxiao Li, Jingyuan Sun, Jesse Davis, Marie-Francine Moens
First submitted to arxiv on: 2 May 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 approach called Dual-tower Multi-scale cOnvolution neural Network (DMON) for Argument Structure Learning (ASL), which predicts relations between arguments to facilitate document understanding. ASL is widely applied in medical, commercial, and scientific domains, but remains a challenging task due to the complexity of relationships between sentences. The DMON framework organizes arguments into a relationship matrix and captures contextual relations using argument embeddings. Experimental results on three datasets demonstrate that DMON outperforms state-of-the-art models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to better analyze documents by recognizing relationships between ideas. It’s like trying to figure out what a story is about by looking at the sentences. The researchers created a new way to do this called DMON, which works really well on different kinds of texts. This could be useful in many areas, such as medicine or science. |
Keywords
» Artificial intelligence » Neural network
