Google llc (20240289552). CHARACTER-LEVEL ATTENTION NEURAL NETWORKS simplified abstract

CHARACTER-LEVEL ATTENTION NEURAL NETWORKS

Organization Name

google llc

Inventor(s)

Yi Tay of Singapore (SG)

Dara Bahri of Lafayette CA (US)

Donald Arthur Metzler, Jr. of Marina del Rey CA (US)

Hyung Won Chung of New York NY (US)

Jai Prakash Gupta of Fremont CA (US)

Sebastian Nikolas Ruder of London (GB)

Simon Baumgartner of Brooklyn NY (US)

Vinh Quoc Tran of New York NY (US)

Zhen Qin of Mountain View CA (US)

CHARACTER-LEVEL ATTENTION NEURAL NETWORKS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240289552 titled 'CHARACTER-LEVEL ATTENTION NEURAL NETWORKS

Simplified Explanation: This patent application describes methods, systems, and apparatus for performing a machine learning task on a sequence of characters using a neural network with a gradient-based sub-word tokenizer and an output neural network.

• The system includes a neural network with a gradient-based sub-word tokenizer and an output neural network.
• The gradient-based sub-word tokenizer applies a learned sub-word tokenization strategy to the input sequence to generate latent sub-word representations.
• The output neural network processes the latent sub-word representations to generate the network output for the machine learning task.

Key Features and Innovation:

• Utilizes a gradient-based sub-word tokenizer for character sequence processing.
• Employs a learned sub-word tokenization strategy for generating latent sub-word representations.
• Integrates an output neural network for processing the latent sub-word representations.

Potential Applications:

• Natural language processing tasks.
• Text classification and sentiment analysis.
• Speech recognition systems.

Problems Solved:

• Efficient processing of character sequences.
• Improved accuracy in machine learning tasks.
• Enhanced performance in natural language processing.

Benefits:

• Higher accuracy in text analysis.
• Faster processing of character sequences.
• Improved performance in machine learning tasks.

Commercial Applications: The technology can be applied in various industries such as:

• E-commerce for sentiment analysis of customer reviews.
• Healthcare for processing medical records.
• Finance for fraud detection in transactions.

Prior Art: Readers can explore prior research on neural networks, sub-word tokenization, and natural language processing techniques.

Frequently Updated Research: Stay updated on advancements in neural network architectures, sub-word tokenization algorithms, and machine learning applications.

Questions about Machine Learning with Sub-word Tokenization: 1. How does the gradient-based sub-word tokenizer improve the processing of character sequences? 2. What are the potential limitations of using sub-word tokenization in machine learning tasks?

Original Abstract Submitted

methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing a machine learning task on an input sequence of characters that has a respective character at each of a plurality of character positions to generate a network output. one of the systems includes a neural network configured to perform the machine learning task, the neural network comprising a gradient-based sub-word tokenizer and an output neural network. the gradient-based sub-word tokenizer is configured to apply a learned, i.e., flexible, sub-word tokenization strategy to the input sequence of characters to generate a sequence of latent sub-word representations. the output neural network is configured to process the latent sub-word representation to generate the network output for the task.