Summary of Nlas-multi: a Multilingual Corpus Of Automatically Generated Natural Language Argumentation Schemes, by Ramon Ruiz-dolz et al.
NLAS-multi: A Multilingual Corpus of Automatically Generated Natural Language Argumentation Schemes
by Ramon Ruiz-Dolz, Joaquin Taverner, John Lawrence, Chris Reed
First submitted to arxiv on: 22 Feb 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 This paper presents an innovative approach to generating natural language arguments across various topics and languages. By developing an effective methodology for automatic argument generation, the authors aim to overcome existing limitations in annotating argumentatively rich data. Specifically, they introduce a corpus of the largest publicly available natural language argumentation schemes and fine-tuned models for identifying these schemes. This breakthrough has significant implications for advancing our understanding of argument mining, generation, and analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it possible to create arguments in different languages and topics using computers. Right now, it’s hard to make this happen because we don’t have enough data and the data we do have is tricky to label. The authors solve these problems by creating a new way to generate natural language arguments automatically. They also share a huge collection of argumentation schemes that can be used as a reference for other researchers. |
