Summary of Guiding Word Equation Solving Using Graph Neural Networks (extended Technical Report), by Parosh Aziz Abdulla et al.
Guiding Word Equation Solving using Graph Neural Networks (Extended Technical Report)
by Parosh Aziz Abdulla, Mohamed Faouzi Atig, Julie Cailler, Chencheng Liang, Philipp Rümmer
First submitted to arxiv on: 19 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Logic in Computer Science (cs.LO)
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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 Graph Neural Network-guided algorithm for solving word equations is proposed, leveraging the Nielsen transformation to split equations into a tree-like search space. The algorithm iterates by rewriting first terms on each side of an equation, with Graph Neural Networks (GNNs) aiding efficient decision-making at each split point. Five graph representations are introduced to encode structural information for GNNs. The solver, DragonLi, is implemented and evaluated on artificial and real-world benchmarks, outperforming well-established string solvers on single word equations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to solve word puzzles using a special kind of neural network called a Graph Neural Network (GNN). It helps make decisions about how to split the puzzle into smaller parts. The researchers created five different ways to represent these puzzles, and then tested their algorithm on both made-up and real-world puzzles. They found that their method is really good at solving simple word puzzles, but it’s still not as good as other methods for more complicated puzzles. |
Keywords
» Artificial intelligence » Gnn » Graph neural network » Neural network
