Attentive Matching for FAQ Retrieval Accuracy
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Solution Overview
Problem
Conventional FAQ retrieval techniques rely on time-consuming manual feature engineering to identify similarities between queries and question-answer pairs, often failing to effectively match queries with relevant FAQs due to limited comparison to both questions and answers.
Innovation Solution
The use of deep learning models such as deep matching networks, multi-hop attention networks, and Symmetric Bilateral Multi-Perspective Matching models for attention matching, which generate multi-dimensional representations of queries and question-answer pairs to determine match scores without the need for manual feature engineering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual feature engineering techniques are used to identify similarities between queries and questions, then semantic and syntactic structures can be discovered, but the process becomes time-consuming and challenging
Solution Approach 1:
The patent replaces manual mechanical feature engineering with automated deep learning models (attention mechanisms, bidirectional LSTMs, convolutional neural networks) that automatically extract semantic and syntactic features from queries and questions, eliminating time-consuming manual analysis while maintaining or improving similarity detection accuracy
Solution Approach 2:
The system enables self-service feature extraction where the deep learning models automatically learn and extract relevant features from the data without human intervention. The attention mechanisms and neural networks autonomously identify important semantic and syntactic patterns, making the feature engineering process self-sufficient and eliminating manual labor
2Measurement precision
If conventional FAQ retrieval techniques compare queries only to questions, then the retrieval process is simpler, but the matching accuracy decreases because relevant information in answers is ignored
Solution Approach 1:
The patent merges the comparison process to simultaneously evaluate both questions and answers against the query. The deep learning model processes query-question pairs and query-answer pairs together, combining their respective contributions to determine overall relevance. This merging ensures that relevant information in answers is not ignored while maintaining a unified retrieval framework
Solution Approach 2:
The patent adds another dimension to the retrieval process by incorporating answer-text analysis alongside question matching. Instead of single-dimensional query-question comparison, the system evaluates multiple dimensions (query-question similarity, query-answer similarity) and integrates them through attention mechanisms and neural networks to produce comprehensive match scores
3Measurement precision
If extensive manual feature engineering is performed to improve matching accuracy, then more relevant features can be identified, but the system complexity and processing time increase
Solution Approach 1:
The patent replaces complex manual feature engineering mechanisms with automated deep learning models that learn features directly from data. Attention mechanisms, bidirectional LSTMs, and convolutional neural networks automatically identify and extract relevant semantic and syntactic features without requiring manual feature design, reducing system complexity while improving feature identification accuracy
Solution Approach 2:
The patent changes the approach from manual feature parameter selection to automated parameter learning through deep learning. The models learn optimal feature representations and attention weights during training, dynamically adjusting parameters based on data patterns rather than relying on pre-defined manual features, thereby reducing system complexity while enhancing accuracy
Data Source
AI summary
Aspects of the present disclosure provide techniques for FAQ retrieval. Embodiments include receiving, via a user interface of a computing application, a query related to a subject. Embodiments include generating a first multi-dimensional representation of the query. Embodiments include obtaining a plurality of question and answer pairs related to the subject and, for a given question and answer pair comprising a given question and a given answer, generating a second multi-dimensional representation of the given question and a third multi-dimensional representation of the given answer. Embodiments include providing input to a model based on the first multi-dimensional representation, the second multi-dimensional representation, and the third multi-dimensional representation and determining a match score for the query and the given question and answer pair based on an output of the model. Embodiments include providing, via the user interface of the computing application, the question and answer pair based on the match score.