Workflow Plans for Task-Oriented Dialogue Systems
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Solution Overview
Problem
Existing text-based dialogue systems, such as chatbots, struggle with understanding user intents, collecting necessary information, and generating accurate responses for complex, multi-step tasks, especially when encountering novel goals or external constraints, as they lack the ability to generalize to unseen workflows and actions.
Innovation Solution
The integration of a symbolic planning mechanism with pre-trained natural language processing models, such as T5 text2text models, to provide explicit workflow plans and metadata, allowing the models to generate sequences of actions and adapt to new tasks by conditioning on plans, ensuring correct and up-to-date execution of tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If pre-trained natural language processing models are used for task-oriented dialogues, then the models can generate fluent text and understand language well, but they cannot reliably perform new multi-step tasks or generalize to unseen workflows
Solution Approach 1:
The patent introduces workflow plans as an intermediary component that bridges the pre-trained language model and the task execution. These plans serve as a mediator that translates high-level task descriptions into concrete action sequences, allowing the model to handle unseen workflows without retraining. The workflow plans contain structured information about task steps, required actions, and expected outcomes, enabling the model to generalize to new tasks while maintaining language fluency.
Solution Approach 2:
The patent applies preliminary action by pre-defining workflow plans for various tasks before the model encounters them during deployment. These workflow plans are prepared in advance and stored as knowledge that the model can retrieve and execute. When faced with a new task, the model can match it to predefined workflow patterns and adapt them, rather than learning from scratch. This allows the system to handle unseen workflows by leveraging previously prepared action sequences.
2Loss of information
If only conversation history is provided as input to the model, then the model can maintain context, but it lacks the ability to understand task structure and execute multi-step workflows correctly
Solution Approach 1:
The patent merges two types of information into the model input: conversation history (for context retention) and workflow plan information (for task structure understanding). By combining these inputs, the model simultaneously maintains conversational context and understands the required task execution sequence. The workflow plan information includes structured data about current step, required actions, and task goals, which when merged with conversation history, enables both context awareness and precise task execution.
3Reliability
If the model is trained on specific tasks, then it can perform those tasks well, but it cannot generalize to novel goals or external constraints
Solution Approach 1:
The patent implements universality by creating a unified workflow plan structure that can represent multiple different tasks and domains. Instead of training separate models for each task, a single model uses a universal workflow representation that captures the essential structure of task execution across different domains. The workflow plans use standardized action types and structured formats that can be applied to various tasks, enabling the model to generalize from trained tasks to novel goals while maintaining reliable performance on known tasks.
Data Source
AI summary
A pre-trained natural language processing machine learning model is received. The pre-trained natural language processing machine learning model is tuned. A workflow to be implemented by a chatbot to complete a task-oriented dialog is received. A representation of one or more plan steps based on a current progression state of the workflow is dynamically determined. At least the representation of the one or more plan steps is provided as an input to the pre-trained natural language processing machine learning model to guide the pre-trained natural language processing machine learning model. An output action and/or an utterance for the task-oriented dialog is received from the pre-trained natural language processing machine learning model.