Question Answering with Sentence Selection for Minimal Context
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional neural question answering models face inefficiencies when learning full context from large documents and are vulnerable to adversarial inputs, leading to slow performance and incorrect answers.
Innovation Solution
A scalable question answering system that uses a sentence selector to identify a minimum set of sentences with a configurable threshold score, allowing it to generate answers without considering the entire document, and incorporates a shared encoder and decoder architecture to determine question-aware sentence embeddings and scores for robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional neural QA models learn full context from large documents, then answer accuracy is improved, but processing time and computational complexity increase significantly
Solution Approach 1:
The patent segments the full document into individual sentences and processes them separately through the encoder-decoder architecture. The system encodes each sentence independently and uses attention mechanisms to selectively combine relevant sentence representations, avoiding the need to process the entire document as a single unit. This segmentation reduces computational complexity while maintaining answer accuracy by focusing only on relevant context.
Solution Approach 2:
The patent extracts and processes only the most relevant sentences for answering a given question, rather than learning from the full document context. The decoder uses attention mechanisms to identify and extract key information from encoded sentence representations, discarding irrelevant portions. This extraction approach maintains answer accuracy while significantly reducing processing time and computational resources.
2Reliability
If conventional neural QA models process full document context, then comprehensive understanding is achieved, but scalability to large corpora becomes intractable
Solution Approach 1:
The patent divides the document into discrete sentence units that can be independently encoded and processed. This segmentation enables the system to handle large corpora by processing sentences in manageable batches, improving scalability while maintaining comprehensive understanding through selective attention mechanisms that identify relevant sentences across the entire corpus.
Solution Approach 2:
The patent applies partial action by processing only the subset of sentences necessary to answer each question, rather than learning from all document content. The attention mechanism selectively attends to relevant sentences, performing sufficient processing to achieve comprehensive understanding without the excessive computational cost of processing the entire document, thereby enabling scalability to large corpora.
3Speed
If conventional neural models focus on wrong subsets of context due to adversarial inputs, then processing speed is maintained, but answer correctness deteriorates
Solution Approach 1:
The patent incorporates feedback mechanisms through the attention decoder that continuously evaluates the relevance of each sentence to the question being answered. The attention mechanism provides feedback signals that adjust the weighting of different sentence representations, allowing the system to correct focus errors and maintain answer correctness even when faced with adversarial inputs, while preserving processing speed through efficient selective attention.
Data Source
AI summary
A natural language processing system that includes a sentence selector and a question answering module. The sentence selector receives a question and sentences that are associated with a context. For a question and each sentence, the sentence selector determines a score. A score represents whether the question is answerable with the sentence. Sentence selector then generates a minimum set of sentences from the scores associated with the question and sentences. The question answering module generates an answer for the question from the minimum set of sentences.