Encoder-Decoder Neural Network for Digital Assistant Query Mapping
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Solution Overview
Problem
Current digital assistants lack robustness in handling noisy and ambiguous queries, often failing to provide human-level responsiveness due to their inability to cope with real-world unpredictability and errors in speech and text input.
Innovation Solution
A method of training a machine learning system that involves obtaining query data samples, converting them into vector representations, clustering these representations to determine canonical queries, and using an encoder-decoder neural network architecture to map ill-formed queries to their canonical forms, thereby improving query processing and response accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional natural language processing pipelines are used, then the system structure is simple and easy to implement, but the system lacks robustness in handling noisy and ambiguous queries
Solution Approach 1:
The patent introduces an intermediary representation layer (vector embeddings) between the raw query input and the processing pipeline. This intermediary transforms noisy text queries into continuous vector representations that capture semantic meaning, allowing the system to handle ambiguity and noise more effectively while maintaining a relatively simple overall architecture.
Solution Approach 2:
The patent changes the parameter representation from discrete text tokens to continuous vector embeddings. This parameter transformation allows the system to capture nuanced semantic differences and handle noisy inputs by operating in a continuous semantic space rather than discrete token space, improving robustness without significantly increasing system complexity.
2Measurement precision
If complex processing pipelines are implemented to improve query understanding, then the accuracy of query interpretation improves, but the system becomes more vulnerable to errors at any stage
Solution Approach 1:
The vector embedding layer serves as a robust intermediary that smooths out errors from noisy input. By transforming discrete tokens into continuous vectors, small errors in tokenization or spelling result in small perturbations in vector space that can be corrected by the nearest neighbor or clustering mechanisms, rather than causing complete failure.
Solution Approach 2:
The patent applies beforehand cushioning by using vector embeddings that inherently provide error tolerance. The continuous vector representation creates a buffer zone where small errors in input don't lead to large errors in interpretation, as similar vectors represent semantically similar meanings even if the original text differed.
3Adaptability or versatility
If the system attempts to process all variations of user queries, then the coverage of query types increases, but the difficulty of training and processing increases
Solution Approach 1:
The patent merges numerous query variations into unified vector representations. By transforming diverse text queries into vector space, semantically similar queries naturally cluster together, allowing the system to handle unlimited query variations through a single unified processing mechanism rather than requiring separate handling for each query type.
Solution Approach 2:
The patent transitions from processing queries in the discrete text dimension to a continuous vector dimension. This dimensionality change allows the system to capture semantic relationships and handle query variations more efficiently, as vector operations can generalize across infinite query variations rather than requiring explicit programming for each case.
Data Source
AI summary
A machine learning system for a digital assistant is described, together with a method of training such a system. The machine learning system is based on an encoder-decoder sequence-to-sequence neural network architecture trained to map input sequence data to output sequence data, where the input sequence data relates to an initial query and the output sequence data represents canonical data representation for the query. The method of training involves generating a training dataset for the machine learning system. The method involves clustering vector representations of the query data samples to generate canonical-query original-query pairs in training the machine learning system.