Spatial-Directed Clustering for Active Question Selection
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Solution Overview
Problem
Existing question answering systems face challenges in obtaining high-quality and diversified question-answer pairs for training, as current active learning frameworks operate in a general feature space, neglecting spatial distribution and often include outliers or noise in the training data.
Innovation Solution
An active learning framework that uses a spatial-directed clustering algorithm to partition the question-answer space, ranks questions based on their contribution to the decision boundary and statistical model, and performs re-sampling based on distance measures to ensure high-quality training data selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If current active learning frameworks are used to select training data, then the labeling cost is reduced, but the training data quality deteriorates due to inclusion of outliers and noise
Solution Approach 1:
The patent segments the question-answer space into multiple clusters using spatial-directed clustering, then selects representative questions from each cluster. This segmentation allows the system to focus on diverse, representative examples rather than processing all unlabeled data, thereby maintaining high training data quality while reducing labeling costs.
Solution Approach 2:
The patent extracts and removes outliers and noise from the training data through a multi-step process that includes clustering, representative selection, and re-sampling. By taking out problematic data points, the system maintains high training data quality while still benefiting from automated selection to reduce labeling costs.
2Adaptability or versatility
If more questions are selected for training, then the training data diversity improves, but the computational resources required increase
Solution Approach 1:
The patent divides the question-answer space into clusters and selects a representative number of questions from each cluster. This segmentation approach ensures diverse training data is obtained efficiently, avoiding the need to process all available questions while maintaining computational efficiency.
Solution Approach 2:
The patent performs partial action by selecting only the necessary number of representative questions from each cluster rather than processing all questions. The re-sampling step ensures sufficient diversity is achieved with a manageable subset, balancing training data diversity with computational resource consumption.
3Reliability
If spatial distribution of training data is considered, then the training data quality improves, but the algorithm complexity increases
Solution Approach 1:
The patent replaces complex manual selection processes with automated spatial-directed clustering algorithms that operate on the geometric structure of the data. This substitution maintains high training data quality through spatial awareness while using computationally efficient algorithms rather than complex manual procedures.
Data Source
AI summary
An active learning framework is operative to identify informative questions that should be added to existing question-answer (Q&A) pairs that comprise a training dataset for a learning model. In this approach, the question-answer pairs (to be labeled as “true” or “false”) are automatically selected from a larger pool of unlabeled data. A spatial-directed clustering algorithm partitions the relevant question-answer space of unlabeled data. A margin-induced loss function is then used to rank a question. For each question selected, a label is then obtained, preferably by assigning a prediction for each associated question-answer pair using a current model that has been trained on labeled question-answer pairs. After the questions are labeled, an additional re-sampling is performed to assure high quality of the training data. Preferably, and with respect to a particular question, this additional re-sampling is based on a distance measure between correct and incorrect answers.