View source for Google LLC (20240289552). CHARACTER-LEVEL ATTENTION NEURAL NETWORKS simplified abstract

=CHARACTER-LEVEL ATTENTION NEURAL NETWORKS=


==Organization Name==

[[:Category:Google LLC|Google LLC]]


[[Category:Google LLC]]

==Inventor(s)==

[[:Category:Yi Tay of Singapore (SG)|Yi Tay of Singapore (SG)]][[Category:Yi Tay of Singapore (SG)]]

[[:Category:Dara Bahri of Lafayette CA (US)|Dara Bahri of Lafayette CA (US)]][[Category:Dara Bahri of Lafayette CA (US)]]

[[:Category:Donald Arthur Metzler, Jr. of Marina del Rey CA (US)|Donald Arthur Metzler, Jr. of Marina del Rey CA (US)]][[Category:Donald Arthur Metzler, Jr. of Marina del Rey CA (US)]]

[[:Category:Hyung Won Chung of New York NY (US)|Hyung Won Chung of New York NY (US)]][[Category:Hyung Won Chung of New York NY (US)]]

[[:Category:Jai Prakash Gupta of Fremont CA (US)|Jai Prakash Gupta of Fremont CA (US)]][[Category:Jai Prakash Gupta of Fremont CA (US)]]

[[:Category:Sebastian Nikolas Ruder of London (GB)|Sebastian Nikolas Ruder of London (GB)]][[Category:Sebastian Nikolas Ruder of London (GB)]]

[[:Category:Simon Baumgartner of Brooklyn NY (US)|Simon Baumgartner of Brooklyn NY (US)]][[Category:Simon Baumgartner of Brooklyn NY (US)]]

[[:Category:Vinh Quoc Tran of New York NY (US)|Vinh Quoc Tran of New York NY (US)]][[Category:Vinh Quoc Tran of New York NY (US)]]

[[:Category:Zhen Qin of Mountain View CA (US)|Zhen Qin of Mountain View CA (US)]][[Category: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.



[[Category:G06F40/284]]
[[Category:CPC_G06F40/284]]