Sentence Phrase Generation via Semantic Triplet Assembly
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Solution Overview
Problem
Current systems for generating contextual answer responses to user questions and passage inputs are inefficient, inaccurate, and not scalable, particularly in providing qualitative sentence responses that include natural language semantics, failing to adequately process questions requiring information about people, places, ideas, or things.
Innovation Solution
A sentence phrasing system that uses a Bi-Directional Attention Flow (BiDAF) deep learning model to generate phrasal responses by determining subject, object, and predicate components from input passages, enabling the creation of semantic triplets and natural language phrases that form grammatically correct answers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional MRC models with BiDAF are used for generating contextual answer responses, then the system can process question-passage pairs efficiently, but the system fails to provide qualitative sentence responses with natural language semantics
Solution Approach 1:
The patent segments the answer generation process into distinct components: extracting subject, object, and predicate from the passage, forming semantic triplets, and then assembling them into complete sentence responses. This segmentation allows each component to be optimized independently, improving both accuracy of semantics and efficiency of generation.
Solution Approach 2:
The patent introduces semantic triplets as an intermediary structure between the passage and the final answer sentence. These triplets serve as a bridge that captures the semantic relationships (subject-predicate-object) in a structured format, enabling more accurate natural language generation while maintaining processing efficiency.
2Productivity
If keyword search and answer ranking methods are deployed, then the system can quickly retrieve answer information, but the system cannot generate comprehensive sentence responses including information about people, places, ideas, or things
Solution Approach 1:
The patent performs preliminary extraction of subject, object, and predicate components from the passage before assembling the final answer sentence. This preliminary action ensures that all necessary qualitative information (people, places, ideas, things) is captured and structured, preventing information loss while maintaining quick retrieval through the structured triplet format.
3Loss of time
If abbreviated answer responses are generated for question-passage pairs, then the system can provide quick responses, but the system lacks phrasal responses with grammatical connections
Solution Approach 1:
The patent changes the structural parameters of the answer by systematically assembling semantic triplets into complete sentences with proper grammatical connections (noun-verb relationships). This parameter change transforms abbreviated responses into natural language sentences without significantly increasing response time, as the triplet structure is already prepared during extraction.
Data Source
AI summary
Examples of a sentence phrasing system are provided. The system may obtain a user question from a user. The system may obtain question entailment data from a plurality of data sources. The system may implement an artificial intelligence component to identify a word index from the question entailment data and to identify a question premise from the user question. The system may implement a first cognitive learning operation to determine an answer premise corresponding to the question premise comprising a second-word data set. The system may determine a subject component corresponding to the question premise. The system may generate an object component and a predicate component from the second-word data set corresponding to the subject component. The system may generate an integrated answer relevant for resolving the user question and comprising the subject component, the object component, and the predicate component concatenated to form an answer sentence.