Regional NLP Explanations Through Topic Modeling and Perturbation
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Solution Overview
Problem
Existing NLP ML models face challenges in providing efficient and accurate global explanations due to the unstructured nature of text data, leading to issues with coverage, fidelity, and stability, as well as token ambiguity, which complicates the interpretation of model behavior across different regions of the dataset.
Innovation Solution
A novel regional explanation technique called regional text perturbation importance (RTPI) that identifies influential text tokens and discovers significant topics in NLP models, using a systematic approach to perturb documents and evaluate the quality of detected topics, thereby providing a comprehensive and interpretable explanation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If global explanation techniques are used for NLP models, then coverage of model behavior is improved, but fidelity and stability deteriorate due to token ambiguity and unstructured text data
Solution Approach 1:
The patent segments the text corpus into multiple topics using topic modeling, where each topic represents a distinct region with coherent semantics. This segmentation allows the explanation system to focus on specific topics rather than treating all text uniformly, thereby improving fidelity and stability within each topic region while maintaining comprehensive coverage across all topics.
Solution Approach 2:
The patent applies different explanation strategies to different topic regions based on their specific characteristics. By identifying influential tokens within each topic region and applying perturbation methods locally, the system achieves high fidelity explanations for each region while collectively covering the entire model behavior space.
2Ease of operation
If topic modeling is used to identify influential tokens, then interpretability is improved, but computational complexity increases
Solution Approach 1:
The patent extracts only the most influential tokens within each topic region using perturbation-based importance measurement, rather than analyzing all tokens in the corpus. This extraction approach maintains high interpretability by focusing on key tokens while reducing computational complexity compared to comprehensive analysis.
Solution Approach 2:
The patent performs topic modeling as a preliminary step to identify topic regions and their representative tokens before conducting perturbation analysis. This preliminary action organizes the unstructured text data into meaningful regions, making subsequent explanation tasks more efficient and interpretable.
3Measurement precision
If perturbation-based methods are applied to evaluate topic quality, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent applies perturbation-based evaluation selectively to evaluate the quality of detected topics and identify influential tokens, rather than exhaustively evaluating all possible token combinations. This partial action approach achieves sufficient measurement precision for topic quality while significantly reducing the time loss compared to exhaustive evaluation.
Data Source
AI summary
Herein are techniques for topic modeling and content perturbation that provide machine learning (ML) explainability (MLX) for natural language processing (NLP). A computer hosts an ML model that infers an original inference for each of many text documents that contain many distinct terms. To each text document (TD) is assigned, based on terms in the TD, a topic that contains a subset of the distinct terms. In a perturbed copy of each TD, a perturbed subset of the distinct terms is replaced. For the perturbed copy of each TD, the ML model infers a perturbed inference. For TDs of a topic, the computer detects that a difference between original inferences of the TDs of the topic and perturbed inferences of the TDs of the topic exceeds a threshold. Based on terms in the TDs of the topic, the topic is replaced with multiple, finer-grained new topics. After sufficient topic modeling, a regional explanation of the ML model is generated.