Argument Convincingness Ranking Model for Balanced Passage Search
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Solution Overview
Problem
Existing search engines fail to accurately rank text passages based on convincingness, particularly in topics with multiple perspectives, leading to biased results and echo chambers due to the subjective nature of determining argument convincingness.
Innovation Solution
A neural network model is trained to identify and rank text passages based on their convincingness by analyzing pairs of passages with the same stance, using a directed graph to filter and update the model with labeled data, ensuring accurate and unbiased ranking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional search engines rank passages based on relevancy to queries, then search results can be obtained efficiently, but the results are biased and fail to provide diverse perspectives due to the subjective nature of determining argument convincingness
Solution Approach 1:
The patent introduces an intermediary component - a trained neural network model that acts as a mediator between the query and passage ranking process. This model objectively evaluates passage convincingness by analyzing pairs of arguments with the same stance, thereby eliminating human subjectivity while maintaining search efficiency. The model serves as an automated judge that ranks passages based on learned convincingness patterns rather than simple relevancy matching.
2Reliability
If a neural network model is trained to determine passage convincingness, then accurate and unbiased ranking can be achieved, but the system complexity increases due to the need for training data collection, processing, and model maintenance
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network model offline using collected and processed passage pairs before deployment. All the complex work of data collection, labeling, and model training is performed in advance, so that during actual search operations, the system only needs to query the pre-trained model for convincingness rankings. This shifts the complexity burden from runtime operations to offline preparation, maintaining simple and fast online search performance.
3Measurement precision
If pairs of passages with the same stance are used for training, then the model can learn to distinguish convincingness objectively, but the data collection and processing time increases
Solution Approach 1:
The patent performs data collection and processing as a preliminary offline action. Passage pairs with the same stance are collected, labeled, and processed before model training begins. This allows the system to invest time in creating a comprehensive and high-quality training dataset once, rather than continuously processing data during search operations. The pre-processed data then enables fast and precise convincingness evaluation during actual use.
Data Source
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AI summary
Aspects of the present disclosure relate to systems and methods for identifying and providing a passage that is highly convincing in terms of one or more stances for a given topic. A query may relate to a topic position. A convincingness ranking model trainer identifies passage pairs that relate to the query, and labels the passage pairs according to relative levels of convincingness of the position between the two passages. The system ranks passages from the passage pairs based on the labels and filter out specific passages when the rank forms a cyclic relationship in a directed graph. A convincingness ranking model trainer uses the remaining message pair and the labels to train the convincingness ranking model.