Sequence-to-Sequence Attention for Query Scoring
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing information retrieval systems face challenges in processing natural language queries due to their fuzziness and implicitness, leading to inefficient document selection, as they often rely on distance/similarity-based scoring methods that may return irrelevant documents.
Innovation Solution
A method using a multilayer sequence-to-sequence converter with an attention mechanism to score queries against documents by generating probabilistic vectors for each word, multiplying their probabilities to determine the overall relevance of the query to the document, and returning documents with a probability score above a threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If distance/similarity based scoring methods are used, then the scoring process is simple and fast, but the retrieval accuracy and relevance of returned documents deteriorate
Solution Approach 1:
The patent changes the scoring parameter from distance-based metrics to probability-based metrics. The sequence-to-sequence model with attention mechanism outputs probability distributions over vocabulary, and the final score is computed as the product of probabilities for each query word given the document context. This fundamental parameter change enables more accurate relevance assessment while maintaining computational efficiency through the probabilistic framework.
Solution Approach 2:
The patent replaces the mechanical distance-based scoring system with a probabilistic semantic understanding system. Instead of computing geometric distances in vector spaces, the system uses a neural sequence-to-sequence model that generates probability distributions and computes relevance as a product of conditional probabilities. This substitution enables the system to capture semantic relationships and contextual meaning that distance metrics cannot represent.
2Measurement precision
If probabilistic scoring with sequence-to-sequence converter is used, then retrieval accuracy and relevance improve, but system complexity increases
Solution Approach 1:
The patent segments the complex scoring task into distinct functional components: the encoder processes the document to generate contextual representations, the attention mechanism selectively focuses on relevant parts of the document for each query word, and the decoder generates probability distributions over vocabulary. This segmentation of the sequence-to-sequence model into modular components makes the complex probabilistic scoring system more manageable and interpretable while maintaining high retrieval accuracy.
Solution Approach 2:
The patent introduces an attention mechanism as an intermediary between the encoder and decoder. This attention layer acts as a mediator that selectively weights different parts of the document based on their relevance to each query word, producing a focused contextual representation. The attention mechanism simplifies the overall system by automatically identifying and focusing on relevant information, reducing the need for complex manual feature engineering while improving retrieval accuracy.
3Productivity
If distance based scoring is used, then the system returns documents with best scores quickly, but the documents may not provide meaningful responses to the query
Solution Approach 1:
The patent changes the scoring parameter from distance-based metrics to probability-based metrics. The sequence-to-sequence model with attention mechanism outputs probability distributions over vocabulary, and the final score is computed as the product of probabilities for each query word given the document context. This fundamental parameter change enables more accurate relevance assessment while maintaining computational efficiency through the probabilistic framework.
Solution Approach 2:
The patent replaces the mechanical distance-based scoring system with a probabilistic semantic understanding system. Instead of computing geometric distances in vector spaces, the system uses a neural sequence-to-sequence model that generates probability distributions and computes relevance as a product of conditional probabilities. This substitution enables the system to capture semantic relationships and contextual meaning that distance metrics cannot represent.
Data Source
AI summary
A technique of scoring a query against a document using sequence to sequence neural networks. The technique comprises: receiving a query comprising a plurality of words from a user; performing a search for a document comprising words based on the query; feeding the words of the document as the input of an encoder of a multilayer sequence to sequence converter; generating a plurality of vectors at a decoder of the multilayer sequence to sequence converter, each vector comprising a probability associated with a respective word in the query; looking up in the respective vector each word's probability of being associated with the document; multiplying every word's probability together to determine an overall probability of the query being associated with the document; and returning the document to the user if the overall probability of the query being associated with the document is greater than a threshold value.