18377570. MULTI-TURN DIALOGUE RESPONSE GENERATION WITH AUTOREGRESSIVE TRANSFORMER MODELS simplified abstract (Capital One Services, LLC)
Contents
- 1 MULTI-TURN DIALOGUE RESPONSE GENERATION WITH AUTOREGRESSIVE TRANSFORMER MODELS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MULTI-TURN DIALOGUE RESPONSE GENERATION WITH AUTOREGRESSIVE TRANSFORMER MODELS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 How does this technology compare to other machine learning approaches in dialogue data analysis?
- 1.11 What are the potential limitations or challenges of implementing this technology in real-world applications?
- 1.12 Original Abstract Submitted
MULTI-TURN DIALOGUE RESPONSE GENERATION WITH AUTOREGRESSIVE TRANSFORMER MODELS
Organization Name
Inventor(s)
Oluwatobi Olabiyi of Arlington VA (US)
Erik T. Mueller of Chevy Chase MD (US)
MULTI-TURN DIALOGUE RESPONSE GENERATION WITH AUTOREGRESSIVE TRANSFORMER MODELS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18377570 titled 'MULTI-TURN DIALOGUE RESPONSE GENERATION WITH AUTOREGRESSIVE TRANSFORMER MODELS
Simplified Explanation
Machine classifiers in this patent application improve the capture of long-term temporal dependencies in dialogue data compared to existing RNN-based architectures. They also model the joint distribution of context and response, rather than just the conditional distribution of the response given the context. Random paddings are added before and/or after the input data to reduce syntactic redundancy and improve performance for dialogue-related tasks. The random padding also provides regularization during training and reduces exposure bias, with input data encoded based on subword tokenization.
- Improved capture of long-term temporal dependencies in dialogue data
- Modeling of joint distribution of context and response
- Addition of random paddings to reduce syntactic redundancy and improve performance
- Regularization and reduction of exposure bias during training
- Input data encoded based on subword tokenization
Potential Applications
The technology described in this patent application could be applied in various fields such as natural language processing, chatbots, customer service automation, and sentiment analysis.
Problems Solved
This technology addresses the challenges of capturing long-term temporal dependencies in dialogue data, modeling the joint distribution of context and response, reducing syntactic redundancy, and improving performance for dialogue-related tasks.
Benefits
The benefits of this technology include improved accuracy in dialogue data analysis, enhanced performance in dialogue-related tasks, and more efficient modeling of context and response interactions.
Potential Commercial Applications
Potential commercial applications of this technology include chatbot development, customer service automation tools, sentiment analysis software, and other natural language processing applications.
Possible Prior Art
One possible prior art in this field is the use of recurrent neural networks (RNNs) for sequence-to-sequence frameworks in natural language processing tasks. However, the approach described in this patent application offers improvements in capturing long-term dependencies and modeling joint distributions in dialogue data.
Unanswered Questions
How does this technology compare to other machine learning approaches in dialogue data analysis?
The article does not provide a direct comparison with other machine learning approaches commonly used in dialogue data analysis, such as LSTM networks or transformer models.
What are the potential limitations or challenges of implementing this technology in real-world applications?
The article does not address the potential limitations or challenges that may arise when implementing this technology in practical, real-world applications, such as computational resources required or scalability issues.
Original Abstract Submitted
Machine classifiers in accordance with embodiments of the invention capture long-term temporal dependencies in the dialogue data better than the existing RNN-based architectures. Additionally, machine classifiers may model the joint distribution of the context and response as opposed to the conditional distribution of the response given the context as employed in sequence-to-sequence frameworks. Machine classifiers in accordance with embodiments further append random paddings before and/or after the input data to reduce the syntactic redundancy in the input data, thereby improving the performance of the machine classifiers for a variety of dialogue-related tasks. The random padding of the input data may further provide regularization during the training of the machine classifier and/or reduce exposure bias. In a variety of embodiments, the input data may be encoded based on subword tokenization.
