Summary of Enhancing Natural Language Inference Performance with Knowledge Graph For Covid-19 Automated Fact-checking in Indonesian Language, by Arief Purnama Muharram and Ayu Purwarianti
Enhancing Natural Language Inference Performance with Knowledge Graph for COVID-19 Automated Fact-Checking in Indonesian Language
by Arief Purnama Muharram, Ayu Purwarianti
First submitted to arxiv on: 22 Aug 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 In this paper, researchers propose using Knowledge Graph (KG) as external knowledge to enhance Natural Language Inference (NLI) performance for automated COVID-19 fact-checking in the Indonesian language. The proposed model architecture consists of three modules: a fact module, an NLI module, and a classifier module. The study demonstrates that incorporating KGs can significantly improve NLI performance in fact-checking, achieving the best accuracy of 0.8616. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Automated fact-checking is important for fighting COVID-19 misinformation online. This paper shows how using special knowledge called a Knowledge Graph can help machines better understand if information is true or not. The researchers created a new model that uses this knowledge to improve its ability to check facts about COVID-19 in Indonesian. Their results show that this approach works well, with an accuracy of 86.16%. |
Keywords
» Artificial intelligence » Inference » Knowledge graph
