Switchboard NLP Clustering via Zero-Shot Confidence Scoring
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Solution Overview
Problem
Existing NLP models for clustering questions face challenges such as reliance on time-consuming and potentially biased training data, difficulty in real-time updates, and limited data sources, which can lead to inaccurate or outdated information, especially for breaking news and dynamic events.
Innovation Solution
The Switchboard system employs a dynamic approach using zero-shot learning to cluster and answer questions, allowing for modular data sourcing, human-in-the-loop tuning, and customizable similarity definitions, enabling real-time processing and flexible data usage across various domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional NLP models use training data sets to learn clustering, then the model can identify semantically similar questions, but the training process is time-consuming and may introduce biases
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing question embeddings in a database before runtime. During operation, the system retrieves pre-computed embeddings and compares them with new questions, eliminating the need for time-consuming training at deployment while maintaining clustering accuracy through预先 prepared semantic representations
Solution Approach 2:
The patent uses copying by creating vector representations (embeddings) of questions that capture semantic meaning. These embedding copies allow the system to compare new questions against a database of existing question embeddings without retraining, preserving clustering precision while avoiding repeated training time costs
2Stability of the object's composition
If traditional NLP models are trained on fixed data, then the model structure is stable, but the model cannot be updated in real-time for breaking news and developing events
Solution Approach 1:
The patent implements dynamics by enabling the system to continuously ingest new questions and answers, compute their embeddings, and add them to the database in real-time. This allows the question-answering system to adapt to breaking news and developing events while maintaining its core clustering functionality through dynamic updates to the embedding database
Solution Approach 2:
The patent applies segmentation by separating the model's core clustering logic (which remains stable) from the question-answer database (which is dynamically updated). This allows the stable embedding computation algorithm to work with continuously evolving data, achieving both stability and adaptability through functional decomposition
3Device complexity
If traditional models are limited to trained data sources, then the model structure is simple, but the model cannot access multiple data sources for comprehensive answers
Solution Approach 1:
The patent applies universality by designing an embedding-based system that can process and store questions from multiple diverse data sources using the same computational approach. The universal embedding representation allows the system to ingest data from various sources without requiring source-specific processing logic, achieving data source flexibility while maintaining relatively simple system architecture
Data Source
AI summary
According to one embodiment, a computer-implemented method for clustering and answering questions is provided. The method includes obtaining an input from a user device, wherein the input comprises a text. The method includes transforming, using a first natural language processing model, the text into a first embedding vector representing a location in an embedding graph, wherein the embedding graph comprises a plurality of prior question embedding vectors representing respective locations in the embedding graph and each prior question embedding vector is associated with at least one answer text. The method includes selecting a set of one or more prior question embedding vectors based on a distance in the embedding graph between the location of the first embedding vector and the respective locations of the plurality of prior question embedding vectors. The method includes, for each respective prior question embedding vector in the selected set of one or more prior question embedding vectors, generating, using a zero-shot confidence scoring model, a respective confidence score value for the respective prior question embedding vector, wherein the respective confidence score value corresponds to a degree of similarity between the first embedding vector and the respective prior question embedding vector. The method includes selecting a first prior question embedding vector from the selected set of one or more prior question embedding vectors based on the generated respective confidence score value of the first prior question embedding vector. The method includes obtaining an answer text associated with the first prior question embedding vector. The method includes generating a response comprising the identified answer text.