Attention-Based Question Generation for Complex Query Handling
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Solution Overview
Problem
Existing question answering systems are inefficient in handling complex queries and fail to accurately retrieve relevant information due to users' varied expressive abilities, leading to unsatisfactory performance in generating answers to questions related to multiple topics.
Innovation Solution
A deep learning-based question generation system using a novel attention-based recurrent neural network encoder-decoder architecture with control gates, which processes textual content and factual statements to generate concise and relevant questions by considering context and question types, incorporating convolutional neural networks for phrase embeddings and a language model for natural language generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing search engines are used to handle queries, then basic information retrieval is possible, but complex queries cannot be handled well and accurate answers are not returned
Solution Approach 1:
The patent transforms the query processing approach by changing parameters from traditional keyword matching to deep learning-based semantic understanding. The system uses neural networks to transform queries and documents into vector representations, enabling the system to handle complex queries with varied expressions while maintaining high accuracy in retrieving relevant information.
Solution Approach 2:
The patent replaces traditional mechanical search engine operations (keyword matching, boolean logic) with a neural network-based system. The mechanical process of exact matching is substituted with semantic similarity computation using vector embeddings and attention mechanisms, allowing the system to understand user intent behind complex queries.
2Reliability
If QA systems require concise and well-described questions as input, then best possible answers can be generated, but users with varied expressive abilities cannot effectively communicate their information needs
Solution Approach 1:
The patent introduces an intermediary neural network layer that translates between user's natural language expressions (varying in conciseness and quality) and the standardized format required for accurate answer generation. The attention-based encoder-decoder model acts as a mediator, understanding diverse user expressions and transforming them into meaningful query representations that maintain high answer quality.
Solution Approach 2:
The system changes the parameter of query representation from requiring concise well-structured questions to accepting any natural language expression. The neural network transforms varied input formats into unified vector representations, preserving the semantic meaning regardless of expression quality, thus maintaining ease of operation while ensuring reliable answer generation.
3Adaptability or versatility
If traditional QA systems are used, then simple questions can be answered, but questions related to many topics of interest and unrelated topics cannot be handled well
Solution Approach 1:
The patent segments the query and document processing into distinct neural network components: query embedding layer, document embedding layer, attention mechanism, and pooling layer. This segmentation allows the system to independently process different parts of multi-topic queries, identify relevant segments, and retrieve accurate answers for each topic while maintaining overall query coherence.
Solution Approach 2:
The patent creates a universal neural network architecture that can handle various types of questions across multiple topics. The attention-based encoder-decoder model serves multiple functions: understanding single-topic and multi-topic queries, identifying relevant document segments, and generating answers for diverse question types, thus achieving both versatility and reliability.
Data Source
AI summary
Methods and systems for generating a question from free text. The system is trained on a corpus of data and receives a tuple consisting of a paragraph (free text), a focused fact, and a question type. The system implements a language model to find the most optimal combination of words to return a question for the paragraph about the focused fact.