Summary of Fact Checking Beyond Training Set, by Payam Karisani et al.
Fact Checking Beyond Training Set
by Payam Karisani, Heng Ji
First submitted to arxiv on: 27 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 A machine learning-based solution is proposed to address the challenge of evaluating everyday claims in various domains. The traditional fact-checking pipeline, comprising a retriever and reader component, is shown to suffer from performance deterioration when trained on labeled data from one domain and applied to another. Novel algorithms are developed to overcome this limitation by making the retriever robust against distribution shift using an adversarial training approach. The reader component is also adapted to be insensitive to the order of claims and evidence documents. Empirical evaluations support the effectiveness of these modifications in achieving higher robustness against distribution shift. A multi-topic fact-checking dataset is proposed, constructed from two well-known datasets, and compared to strong baseline models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to check if information is true or false is being developed. Right now, it takes a lot of time and special knowledge to evaluate everyday claims. This new approach uses machine learning to make the process faster and more accurate. The problem is that this method doesn’t work well when applying it to different areas of expertise. To fix this issue, new algorithms are created to make the system more robust. The main idea is to train the system on labeled data from one area and then use it in another area without needing additional training. Another challenge is that the reader part of the system needs to be able to understand information presented in different orders. The proposed solution shows great results in addressing these challenges. |
Keywords
* Artificial intelligence * Machine learning
