Summary of Position Paper: Generalized Grammar Rules and Structure-based Generalization Beyond Classical Equivariance For Lexical Tasks and Transduction, by Mircea Petrache et al.
Position Paper: Generalized grammar rules and structure-based generalization beyond classical equivariance for lexical tasks and transduction
by Mircea Petrache, Shubhendu Trivedi
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG); Machine Learning (stat.ML)
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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 Machine learning models have long struggled with compositional generalization, a property that allows humans to apply learned concepts to novel combinations of words or phrases. Our proposed framework for building such models uses Generalized Grammar Rules (GGRs), a type of symmetry-based constraint inspired by physics. We show how GGRs can be used to formalize notions of symmetry in language transduction tasks, and highlight their potential connections to reinforcement learning and other areas of research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Language is hard for computers because they can’t understand words in new combinations. Our idea is to use rules that help machines learn from patterns in language. These “Grammar Rules” are like secret codes that tell the machine how to combine words correctly. We think this could work really well and be useful in lots of different areas, not just language. |
Keywords
* Artificial intelligence * Generalization * Machine learning * Reinforcement learning
