Dialog Agent Reinforcement Learning for Natural Language Database Access
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Solution Overview
Problem
Conventional task-oriented dialog systems require users to have knowledge of the underlying database schema and query language, making it difficult for users to retrieve relevant data, and they lack a strong feedback mechanism for training dialog management policies, resulting in indirect, coarse, and delayed rewards.
Innovation Solution
A dialog handler that utilizes a reinforced learning process with a reward function measured on a per-turn basis, allowing for direct, fine-grained, and timely feedback to train a dialog management policy, enabling users to interact naturally with a database via metadata without needing to know the database schema or query language.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional task-oriented dialog systems use database schema and query language, then information retrieval capability is improved, but user accessibility deteriorates
Solution Approach 1:
The patent introduces a dialog management policy as an intermediary layer between the user and the database system. This policy translates natural language user intents into database queries without requiring users to know the underlying schema or query language, while maintaining precise information retrieval capabilities through structured query generation.
Solution Approach 2:
The system creates a simplified representation or copy of the database schema through metadata that exposes only necessary information to users. This allows users to interact with a user-friendly version of the database structure without needing to understand the complex underlying schema, thus improving accessibility while preserving retrieval accuracy.
2Stability of the object's composition
If conventional dialog systems use indirect feedback mechanisms, then system stability is improved, but training effectiveness deteriorates
Solution Approach 1:
The patent implements a per-turn reward function that provides immediate, fine-grained feedback during dialog interactions. This feedback mechanism evaluates system performance at each turn rather than providing delayed, coarse feedback, enabling more effective training of the dialog management policy through reinforcement learning while maintaining system stability through controlled reward structures.
Data Source
AI summary
Embodiments of the present invention provide a system for implementing multi-turn dialogs. The system performs a method that includes receiving a series of user utterances, generating a series of responsive system utterances, and labeling the series of responsive system utterances to generate training data for training a dialog management policy. The labeling includes executing a reward function at each turn of a dialog, in which for each turn of the dialog the reward function is configured to output a reward value that is based at least in part on an accuracy of the responsive system utterance of the turn and on number of dialog turns elapsed.