Autoregressive Transformer Dialogue Generation With Random Padding
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Solution Overview
Problem
Conventional systems for generating responses in multi-turn dialogs often produce irrelevant or non-useful responses due to exposure bias and limited diversity, failing to capture long-term temporal dependencies and suffering from syntactic redundancy, which leads to generic and repetitive outputs.
Innovation Solution
Transformer-based machine classifiers that model the joint distribution of context and response, use subword tokenization, and append random paddings to reduce syntactic redundancy, thereby improving response relevance and diversity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional neural network architectures are used for response generation, then the system can generate responses, but the responses are limited in diversity and not particularly relevant to the topic
Solution Approach 1:
The patent changes the fundamental parameters of the neural network architecture by transitioning from recurrent neural networks to transformer-based models with self-attention mechanisms. This architectural parameter change enables the system to capture long-term dependencies and generate more diverse yet relevant responses, resolving the contradiction between response diversity and relevance
Solution Approach 2:
The patent introduces a new dimension in processing by appending random paddings to input sequences. This dimensional augmentation to the input data structure helps reduce syntactic redundancy and exposes the model to varied input patterns, thereby improving both response diversity and relevance simultaneously
2Loss of information
If recurrent neural network-based architectures are used, then the system can process dialogue data, but it fails to capture long-term temporal dependencies
Solution Approach 1:
The patent replaces the sequential processing mechanism of recurrent neural networks with the parallel self-attention mechanism of transformers. This substitution eliminates the inherent limitation of RNNs in capturing long-term dependencies while maintaining architectural feasibility through the attention-based information gathering mechanism
Solution Approach 2:
The patent performs preliminary action by pre-processing input data with random paddings before feeding it to the transformer model. This preliminary data augmentation prepares the input sequences to better capture temporal relationships and reduces syntactic redundancy, improving temporal dependency capture without significantly increasing model complexity
3Adaptability or versatility
If sequence-to-sequence frameworks are used, then the system can generate responses, but it models only the conditional distribution of response given context, limiting diversity
Solution Approach 1:
The patent introduces dynamics by employing beam search decoding with dynamic beam width adjustment. This dynamic decoding strategy allows the system to explore multiple possible responses and select the most diverse yet high-quality outputs, simultaneously improving response diversity and maintaining quality standards
4Reliability
If conventional training methods are used, then the system can be trained, but exposure bias and syntactic redundancy lead to generic and repetitive outputs
Solution Approach 1:
The patent applies preliminary action by augmenting training data with random paddings appended to input sequences. This preliminary data preparation reduces syntactic redundancy in the training corpus and helps the model generalize better, reducing exposure bias and producing more diverse outputs without sacrificing quality
Solution Approach 2:
The patent implements feedback mechanisms through structured training approaches that provide diverse feedback signals during model training. This feedback strategy helps the model learn to avoid generic responses and produces more varied yet reliable outputs by continuously guiding the learning process with diverse training examples
Data Source
AI summary
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.