Summary of Symbolic Autoencoding For Self-supervised Sequence Learning, by Mohammad Hossein Amani et al.
Symbolic Autoencoding for Self-Supervised Sequence Learning
by Mohammad Hossein Amani, Nicolas Mario Baldwin, Amin Mansouri, Martin Josifoski, Maxime Peyrard, Robert West
First submitted to arxiv on: 16 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 The paper introduces symbolic autoencoding (ΣAE), a self-supervised framework that leverages abundant unparallel data alongside limited parallel data to improve performance in transduction tasks between distinct symbolic systems. ΣAE connects two generative models via a discrete bottleneck layer and optimizes end-to-end by minimizing reconstruction loss, simultaneously with supervised loss for the parallel data. This approach enables efficient sequence learning despite the discreteness of the bottleneck, leading to significant enhancements in transduction task performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding ways for computers to understand language better. It’s hard for them to switch between different languages or formats, even when they have lots of information. The new method, called symbolic autoencoding (ΣAE), helps computers learn from both similar and dissimilar data. This makes it easier for them to translate text from one language to another, which is important for many tasks like machine translation and language understanding. |
Keywords
* Artificial intelligence * Language understanding * Self supervised * Supervised * Translation
