Chatbot Clarifying Question Sequencing for Ambiguous Queries
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Solution Overview
Problem
Traditional dialog systems are unable to effectively handle ambiguous user queries, relying on brute force or query popularity, which can lead to inefficient and time-consuming interactions, as they lack the ability to use human intuition to clarify user intent.
Innovation Solution
The implementation of an Adaptive Stochastic Maximization Problem with Cardinality Constraints (ASMC) framework, utilizing a fast approximation algorithm under an adaptive submodularity framework, to determine clarifying questions that reduce query ambiguity by iteratively posing questions to users and utilizing a response model to optimize the sequence of clarifying questions within a defined budget.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional dialog systems use brute force to handle ambiguous queries by working through each choice one at a time, then the system can eventually identify the correct solution, but the interaction becomes time-consuming for the user
Solution Approach 1:
The system performs preliminary actions by proactively generating and presenting multiple clarifying questions simultaneously to the user before the conversation progresses further. This allows the user to provide multiple pieces of information in advance, enabling the system to narrow down ambiguous queries more efficiently without time-consuming sequential interactions
Solution Approach 2:
The system transitions from traditional single-turn sequential questioning to a multi-dimensional approach by presenting multiple clarifying questions across different dimensions simultaneously. This allows parallel information gathering along multiple paths, dramatically reducing the time required to resolve ambiguity while maintaining high accuracy
2Productivity
If traditional dialog systems rely on query popularity from historical conversations to guess user intent, then the system can quickly respond to common queries, but it fails to reduce ambiguity when users have non-common queries
Solution Approach 1:
The system incorporates feedback mechanisms by analyzing user responses to clarifying questions and iteratively refining its understanding of user intent. This feedback loop allows the system to adapt to both common and non-common queries, maintaining high response speed while effectively reducing ambiguity through dynamic adjustment based on user inputs
Solution Approach 2:
The system dynamically changes parameters such as the number and type of clarifying questions presented based on the detected ambiguity level and query patterns. For common queries, it maintains quick response with minimal questions, while for non-common queries, it adjusts by presenting more targeted clarifying questions to effectively reduce ambiguity
3Measurement precision
If traditional dialog systems ask clarifying questions for each choice in ambiguous queries, then the system can narrow down the solution space, but the number of questions becomes large and frustrates users
Solution Approach 1:
The system segments the clarifying questions into multiple categories or dimensions (e.g., temporal, contextual, specific details) and presents them in an organized manner. This segmentation allows the user to systematically provide information across different aspects of the query, achieving high precision in intent understanding while keeping the interaction structured and manageable
Solution Approach 2:
The system designs clarifying questions with multi-functionality, where each question serves multiple purposes: it clarifies specific ambiguities, gathers additional contextual information, and helps narrow down the solution space simultaneously. This universal approach reduces the total number of questions needed while maintaining high measurement precision
Data Source
AI summary
Dialog systems, such as chatbots, enable a user to query for information of a data management system. However, a user's query may be ambiguous resulting in a reply of too much information that is incomprehensible to a user. Clarifying questions are posed to a user to remove ambiguity from a user's query, and based on a user's answers, the volume of replying information is reduced to an amount that a user can comprehend. Clarifying questions are efficiently sequenced to reduce the ambiguity of a user's query, such that the number of clarifying questions does not result in a user abandoning the user's query.