Contextual Label Compression for Extreme Multi-Label Classification
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Solution Overview
Problem
Conventional methods face challenges in accurately predicting relevant search queries for content, especially when data is sparse or when dealing with a large number of potential keywords, leading to inaccurate predictions and increased resource intensity.
Innovation Solution
A contextual label compression framework using trained sequence-to-sequence models to generate semantic encodings from input sequences, such as textual information, which enables the prediction of relevant queries by transforming extreme multi-label classification tasks into a sequence-to-sequence formulation, reducing model size and improving inference time while maintaining competitive accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used to predict search queries for content, then the system can handle basic classification tasks, but the prediction accuracy deteriorates when data is sparse or when dealing with a large number of potential keywords
Solution Approach 1:
The patent segments the extreme multi-label classification problem into a sequence-to-sequence formulation, where the model processes input sequences (content features) and generates output sequences (predicted queries) step-by-step. This segmentation allows the model to handle sparse data and large keyword sets by breaking down the complex classification task into manageable sequential predictions, thereby improving both accuracy and adaptability
Solution Approach 2:
The patent transforms the traditional multi-label classification approach into a sequence-to-sequence framework, adding a temporal/sequential dimension to the problem. By representing both input content and output queries as sequences, the model can leverage sequential patterns and dependencies, enabling accurate predictions even with sparse data and extensive keyword vocabularies
2Measurement precision
If larger models are used to improve prediction accuracy for extreme multi-label classification, then the model size and resource requirements increase significantly
Solution Approach 1:
The patent employs a dynamic sequence-to-sequence model that adapts its processing based on input characteristics. The model dynamically generates query sequences of varying lengths based on the input content, allowing it to maintain high accuracy without requiring a excessively large fixed-size architecture. This dynamic approach enables the model to scale efficiently with problem complexity
Solution Approach 2:
The patent changes the fundamental parameters of the classification approach by using sequence-to-sequence modeling with learned embeddings and attention mechanisms. This parameter transformation allows the model to achieve high prediction accuracy through sophisticated sequential processing rather than relying solely on increasing model size, thereby reducing the complexity-scaling trade-off
3Productivity
If traditional classification methods are used, then the implementation is simpler, but the inference time increases and resource usage is higher for extreme multi-label tasks
Solution Approach 1:
The patent replaces traditional mechanical classification systems (rule-based or simple statistical methods) with a neural sequence-to-sequence model. This substitution enables faster inference by leveraging parallel processing capabilities of neural networks and efficient sequence generation, achieving high productivity despite increased implementation complexity through the use of modern deep learning frameworks
Data Source
AI summary
A contextual label compression framework is presented that uses trained sequence to sequence models. A set of training data including received queries and related content can be processed to generate sequences of semantic encodings. These sequences can be used to train the sequence to sequence models, in order to be able to predict queries for instances of content when the relevant information for those instances is processed by the model. When such information is received for an instance, that information can be processed to generate a semantic encoding sequence which can then be processed by the model. A resulting semantic sequence output by the model can be segmented and decoded to produce a set of relevant queries for the instance of content. This information can then be provided to an entity associated with the instance of content for purposes in managing aspects relating to that content.