Background Knowledge Transformer for Why-Question Answer Identification
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Solution Overview
Problem
Existing why-type question-answering systems struggle to accurately determine whether a passage is an answer to a question, as they do not effectively utilize background knowledge, leading to inaccuracies in answer identification.
Innovation Solution
A text classifier is developed, incorporating a language representation model, a knowledge integration transformer, and a background knowledge representation generator trained using Generative Adversarial Networks (GAN) to enhance the utilization of background knowledge, specifically causal and tool-goal relations, for accurate answer identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional question-answering systems use basic text matching and simple causal relation patterns, then the system complexity remains low, but the answer identification accuracy is insufficient
Solution Approach 1:
The system performs preliminary extraction of causal relation representations from web archives before the actual question-answering process. This pre-processing step creates a knowledge base of causal relationships that can be efficiently queried during answer identification, improving accuracy without adding complexity to the core question-answering mechanism
Solution Approach 2:
The system introduces causal relation representations as an intermediary layer between the question and answer passages. These representations act as mediators that capture the semantic relationship between questions and answers, enabling more accurate identification without requiring direct complex analysis of all text pairs
2Measurement precision
If the system extracts and utilizes background knowledge from web archives, then the answer determination accuracy improves, but the information processing time and computational load increase
Solution Approach 1:
The system extracts causal relation representations from web archives in advance, before actual question-answering operations. This pre-extraction creates a ready-to-use knowledge base that reduces processing time during actual queries, as the system can directly query pre-processed causal relationships rather than analyzing raw text in real-time
Solution Approach 2:
The system extracts only the essential causal relation representations from web archives, separating this critical background knowledge from the full text corpus. This selective extraction focuses computational resources on the most relevant information for answer determination, reducing overall processing time while maintaining accuracy
3Measurement precision
If the system uses detailed causal relation patterns and clue terms, then the ability to identify correct answers improves, but the complexity of knowledge representation increases
Solution Approach 1:
The system extracts specific causal relation patterns and clue terms from the text, separating these critical relationship indicators from the full text content. This extraction creates a simplified representation that captures essential causal relationships without requiring the system to process all textual details, maintaining recognition accuracy while reducing representation complexity
Solution Approach 2:
The system transforms unstructured text into structured causal relation representations with defined parameters and formats. This parameterization standardizes the knowledge representation, making it more manageable and computationally efficient while preserving the semantic richness needed for accurate causal relation recognition
Data Source
AI summary
A text classifier 90 for answer identification is capable of highly accurate identification of an answer candidate to a question, by effectively using background knowledge related to the question, in order to extract an answer candidate to the question, the text classifier including: a BERT (Bidirectional Encoder Representation from Transformers) receiving a question and an answer candidate as inputs; a knowledge integration transformer receiving the output of BERT as an input; a background knowledge representation generator receiving a question and an answer as inputs and generating a group of background knowledge representation vectors for the question; and a vector converter respectively converting the question and the answer candidate to embedded vectors and inputting the same to the background knowledge representation generator. The knowledge integration transformer receives the group of background knowledge representation vectors as attention and outputs a label indicating whether the answer candidate includes the correct answer to the question.