Summary of Knowcomp Pokemon Team at Dialam-2024: a Two-stage Pipeline For Detecting Relations in Dialogical Argument Mining, by Zihao Zheng et al.
KNOWCOMP POKEMON Team at DialAM-2024: A Two-Stage Pipeline for Detecting Relations in Dialogical Argument Mining
by Zihao Zheng, Zhaowei Wang, Qing Zong, Yangqiu Song
First submitted to arxiv on: 29 Jul 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 proposed DialAM-2024 shared task focuses on dialogical argument mining, which involves identifying relations between proposition nodes, locution nodes, and illocutionary relations. To tackle this challenge, a two-stage pipeline is designed, comprising the Two-Step S-Node Prediction Model in Stage 1 and the YA-Node Prediction Model in Stage 2. The pipeline is further augmented with training data and contextual information in Stage 2. The approach leads to good results, with the team “Pokemon” achieving top rankings in both ARI Focused and Global Focused scores. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to analyze conversations and identify important points being discussed. It’s like trying to understand what people are really saying when they’re talking. To do this, the researchers developed a special system that looks at different parts of a conversation and how they relate to each other. They used this system to try and understand some conversations better, and it seemed to work pretty well. |
