Context-Aware Answer Ranking for Conversational Query Response
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Solution Overview
Problem
Existing question and answer systems, particularly non-factoid-based systems, struggle to provide contextually and grammatically correct answers due to the inaccuracy of passage retrieval and nonsensical summaries, leading to inefficient user experience.
Innovation Solution
A system that analyzes the linguistic and syntactic structure of a question to generate contextually, grammatically, and conversationally correct answers by pairing the question with candidate answers, extracting features, scoring them using a ranking model, and providing ranked answers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If passage retrieval is used to answer non-factoid questions, then relevant text spans are identified, but the answers are inaccurate and do not consider the actual context of the question
Solution Approach 1:
The system segments the answer generation process into multiple independent components: context analysis module that extracts contextual information from the question, candidate answer generation module that creates multiple potential answers, and ranking module that evaluates candidates based on contextual relevance. This segmentation allows each component to specialize and improves overall answer accuracy while preserving context.
Solution Approach 2:
The patent introduces contextual information as an intermediary element between the question and candidate answers. The context analysis module extracts this intermediary representation, which then mediates the selection and ranking of candidate answers. This intermediary ensures that answers are evaluated based on their relevance to the specific contextual nuances of the question rather than just keyword matching.
2Productivity
If summarization is used to generate answers, then relevant bits of documents are extracted and combined, but the summaries are often nonsensical and not related to the actual context of the question
Solution Approach 1:
The system implements feedback mechanisms where candidate answers are evaluated against the original question context. The ranking module provides feedback by scoring each candidate based on how well it answers the question within its contextual framework. This feedback loop ensures that nonsensical summaries are filtered out and only coherent, contextually relevant answers are selected, improving reliability while maintaining productivity.
Solution Approach 2:
The patent changes the parameters of answer evaluation from simple keyword matching to multi-dimensional assessment including contextual relevance, grammatical correctness, and conversational appropriateness. By adjusting these evaluation parameters, the system can distinguish between nonsensical summaries and coherent answers, thereby improving answer reliability without sacrificing generation efficiency.
3Extent of automation
If natural language generation is used to create answers, then responses are generated automatically, but the results often look like word salad and are not sufficiently robust to generate contextually and grammatically correct answers
Solution Approach 1:
The system performs preliminary actions by analyzing the question's linguistic and syntactic structure before generating candidate answers. The context analysis module extracts contextual information and grammatical constraints in advance, which then guide the natural language generation process. This preliminary analysis ensures that automated answer generation produces contextually and grammatically correct responses rather than word salad, maintaining both automation and quality.
Solution Approach 2:
The patent changes the parameters of natural language generation from unconstrained text generation to constrained generation based on extracted contextual and grammatical parameters. By adjusting generation parameters to match the question's linguistic structure and contextual requirements, the system produces answers that are both automatically generated and of high quality, resolving the contradiction between automation extent and answer quality.
4Device complexity
If existing Q&A systems provide answers without considering linguistic and syntactic structure, then processing is simpler, but the answers lack contextual and conversational correctness
Solution Approach 1:
The system segments the complex task of contextual answer generation into manageable modules: linguistic structure analysis module, context extraction module, candidate generation module, and ranking module. Each module handles a specific aspect of the processing, making the overall complex system more tractable and maintainable while achieving high contextual accuracy through the coordinated work of specialized components.
Data Source
AI summary
The present disclosure relates to systems and methods for generating contextually, grammatically, and conversationally correct answers to input questions. Embodiments provide for linguistic and syntactic structure analysis of a submitted question in order to determine whether the submitted question may be answered by at least one headnote. The question is then further analyzed to determine more details about the intent and context of the question. A federated search process, based on the linguistic and syntactic structure analysis, and the additional analysis of the question is used to identify candidate question-answer pairs from a corpus of previously created headnotes. Machine learning models are used to analyze the candidate question-answer pairs, additional rules are applied to rank the candidate answers, and dynamic thresholds are applied to identify the best potential answers to provide to a user as a response to the submitted question.