Clarification Question Generation for Conversational Search
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Solution Overview
Problem
Users face difficulties in obtaining relevant information from abundant textual sources due to the lack of effective mechanisms for processing complex information searching tasks, leading to unsuccessful results in traditional search methods.
Innovation Solution
A computer-implemented method and system that utilizes machine learning language models to generate clarification questions by analyzing dialogs between users and agents, retrieving relevant text passages, and presenting them to users to facilitate conversational search, thereby guiding the information distillation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional search methods are used to retrieve information from abundant textual sources, then the search engine can provide initial responses quickly, but the users cannot obtain relevant information effectively due to lack of mechanisms for processing complex information searching tasks
Solution Approach 1:
The system dynamically adapts the search process by generating clarification questions based on user answers to previous questions, allowing the search strategy to evolve and refine itself iteratively rather than following a static query path
Solution Approach 2:
The system implements feedback loops where user answers to clarification questions are fed back into the search engine to refine subsequent queries, creating a closed-loop system that continuously improves information retrieval based on user responses
2Reliability
If users perform multiple rounds of searching to distill relevant information, then the users may eventually find relevant information, but the process becomes time-consuming and inefficient
Solution Approach 1:
The system performs preliminary actions by proactively generating and presenting clarification questions that anticipate the user's information needs, rather than waiting for users to iteratively refine their own queries through multiple search rounds
Solution Approach 2:
The clarification question generation system acts as an intermediary between the user and the search engine, translating user information needs into refined search queries through generated clarification questions, thereby reducing the time users would otherwise spend formulating multiple search iterations
3Reliability
If conversational search system presents clarification questions to guide users through information distillation, then the system can improve information retrieval accuracy, but the system complexity increases due to need for generating and processing multiple clarification questions
Solution Approach 1:
The system serves itself by automatically generating clarification questions based on analysis of historical dialog data and user answers, without requiring manual intervention or complex external systems to formulate the questions
Solution Approach 2:
The system learns from copying patterns in historical dialog data between users and agents, using machine learning models to replicate effective clarification question generation strategies without requiring complex rule-based systems
4Extent of automation
If machine learning language models are trained on historical dialogs to generate clarification questions, then the system can automatically generate relevant questions, but the training data processing and model training require significant computational resources
Solution Approach 1:
The system performs preliminary training actions by training the machine learning model on historical dialog data in advance, so that during actual operation the model can automatically generate clarification questions without requiring real-time computational resources for training
Solution Approach 2:
The system uses machine learning models that have copied effective clarification question patterns from historical dialog data, allowing the model to generate questions automatically during operation without requiring continuous access to training data or computational resources
Data Source
AI summary
Training a machine learning language model to generate clarification questions for use in conversational search, including: Obtaining multiple dialogs between users and agents, each dialog including messages exchanged between a user and an agent, wherein one of the messages of each dialog includes a reference to a solution document provided by the agent. For each of the dialogs, operating a search engine to retrieve a text passage, relevant to at least one of the messages of the respective dialog, from the respective solution document. Training a machine learning language model to generate a new clarification question given at least one new message and multiple new text passages, wherein the training is based on a training set which comprises, for each of the dialogs: said at least one of the messages of the respective dialog, and the text passage retrieved for the respective dialog.