Seq2seq Dialog Model Hybrid RL SL Training
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Solution Overview
Problem
Current task-oriented dialog systems face challenges in generating fluent and coherent responses due to limitations in reinforcement learning (RL) and supervised learning (SL) approaches, which require expensive human annotations and predefined templates, and fail to optimize for future rewards, leading to suboptimal performance in multi-turn dialog systems.
Innovation Solution
A hybrid approach combining RL and SL techniques, using a sequence-to-sequence (seq2seq) model with a custom variant that learns embeddings for dialog history offline without human annotation, and incorporates a reward term to encourage responses that move the conversation towards the final goal, de-emphasizing cross-entropy loss and using nearest neighbor algorithms for generating coherent responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reinforcement learning is used to optimize future rewards in dialog systems, then long-term task completion improves, but the system requires expensive human annotations and predefined templates
Solution Approach 1:
The patent uses supervised learning to pre-train the model on annotated dialog data, creating a foundation model that copies effective patterns from training examples. This pre-trained model then serves as the basis for reinforcement learning, reducing the need for extensive annotations during the RL phase while maintaining task completion accuracy.
Solution Approach 2:
The patent merges supervised learning and reinforcement learning into a hybrid training framework. The SL component provides structured knowledge from annotated data, while the RL component optimizes for long-term task completion. This combination allows the system to benefit from both labeled data efficiency and future reward optimization without fully committing to the high cost of pure RL approaches.
2Ease of manufacture
If supervised learning is used with cross-entropy loss, then training is simpler, but the system fails to optimize for future rewards in multi-turn dialogs
Solution Approach 1:
The patent performs preliminary supervised learning training to establish a baseline model with reasonable performance. This preliminary training simplifies the subsequent reinforcement learning process, as the model already has basic dialog understanding before being fine-tuned for future reward optimization. The preliminary action reduces the complexity of the RL training phase.
Solution Approach 2:
The patent implements a dynamic training approach where the loss function transitions from primarily cross-entropy loss in early training stages to a greater emphasis on reward-based loss in later stages. This dynamic adjustment allows the model to first learn basic patterns simply, then progressively optimize for future rewards as training progresses.
3Reliability
If reinforcement learning optimizes for long-term rewards, then task completion improves, but the training process becomes more complex and resource-intensive
Solution Approach 1:
The patent applies partial reinforcement learning by combining it with supervised learning rather than using pure RL. This partial application of RL principles provides enough future reward optimization to improve task completion rates while avoiding the full computational burden and training complexity of complete RL approaches. The hybrid method achieves sufficient RL benefits with reduced resource requirements.
Data Source
AI summary
Techniques for intelligent task-oriented multi-turn dialog system automation are described. A seq2seq ML model can be trained using a corpus of training data and a loss function that is based at least in part on a distance to a goal. The seq2seq ML model can be provided a user utterance as an input, and a vector of a plurality of values output by a plurality of hidden units of a decoder of the seq2seq ML model can be used to select one or more candidate responses to the user utterance via a nearest neighbor algorithm. In some embodiments, the specially adapted seq2seq ML model can be trained using unsupervised learning, and can be adapted to select intelligent, coherent agent responses that move a task-oriented dialog toward its completion.