Summary of Bridging Textual and Tabular Worlds For Fact Verification: a Lightweight, Attention-based Model, by Shirin Dabbaghi Varnosfaderani et al.
Bridging Textual and Tabular Worlds for Fact Verification: A Lightweight, Attention-Based Model
by Shirin Dabbaghi Varnosfaderani, Canasai Kruengkrai, Ramin Yahyapour, Junichi Yamagishi
First submitted to arxiv on: 26 Mar 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 introduce a new model for fact extraction and verification tasks involving unstructured text and structured tabular data. The model, which doesn’t require extensive preprocessing or rule-based transformations, leverages pre-trained models and attention mechanisms to efficiently analyze diverse datasets. By preserving the original evidence’s context and incorporating multi-modal information, the approach exhibits competitive performance on the FEVEROUS benchmark. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers’ goal is to improve fact extraction and verification by developing a model that can handle different types of data without losing important context. Their new approach uses pre-trained models and attention mechanisms to analyze text and table data together, which helps it make more accurate predictions about whether certain information is true or false. |
Keywords
» Artificial intelligence » Attention » Multi modal
