Summary of To Be Continuous, or to Be Discrete, Those Are Bits Of Questions, by Yiran Wang and Masao Utiyama
To be Continuous, or to be Discrete, Those are Bits of Questions
by Yiran Wang, Masao Utiyama
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 Recently, binary representation has emerged as an innovative intermediate representation bridging the gap between continuous and discrete representations. This paper explores the possibility of introducing binary labels on the output side, aiming to enable models to output binary labels while preserving structural information. To achieve this, we extend the contrastive hashing method to structured contrastive hashing (SCH), upgrading CKY from label-level to bit-level, defining a novel similarity function with span marginal probabilities, and introducing a carefully designed instance selection strategy for the loss function. Our proposed model demonstrates competitive performance on various structured prediction tasks, highlighting the potential of binary representation as a novel intermediate representation that further connects deep learning’s continuous nature to natural languages’ discrete intrinsic property. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding new ways for computers to understand and generate text. Instead of using numbers or letters, it uses a special type of code called “binary” which is like a mix between writing words and drawing pictures. The researchers tried to make this binary code work better by adding extra steps to the way computers learn from data. They tested their new approach on different tasks that involve understanding language and found that it worked almost as well as other methods. This breakthrough could help computers get even better at understanding natural languages like human speech. |
Keywords
» Artificial intelligence » Deep learning » Loss function
