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: The patent application describes methods, systems, and apparatus for performing a machine learning task on a sequence of characters to generate a network output 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 task.

Key Features and Innovation:

• Utilizes a neural network with a gradient-based sub-word tokenizer for machine learning tasks.
• Applies a learned sub-word tokenization strategy to input sequences of characters.
• Generates latent sub-word representations for processing by the output neural network.

Potential Applications:

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

Problems Solved:

• Efficiently process input sequences of characters for machine learning tasks.
• Improve accuracy and performance of neural networks in handling text data.

Benefits:

• Enhanced accuracy in text processing tasks.
• Increased efficiency in machine learning tasks involving character sequences.

Commercial Applications:

• Natural language processing software for businesses.
• Text analytics tools for data analysis companies.
• Speech recognition systems for communication technology firms.

Prior Art: Prior art related to this technology may include research papers on neural networks for text processing tasks, sub-word tokenization strategies, and machine learning algorithms for character sequence analysis.

Frequently Updated Research: Researchers are constantly exploring new sub-word tokenization techniques, improving neural network architectures for text processing, and enhancing machine learning algorithms for character sequence analysis.

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

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.