NLP Policy Severity Scoring for User Utterance Violations
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Solution Overview
Problem
Existing electronic devices struggle to effectively identify and manage content-policy violations in user utterances, particularly in voice user interfaces, leading to the output of inappropriate or offensive language that may violate content policies.
Innovation Solution
A system comprising a phrase-management component, phrase evaluator, and permissive deviation component to analyze user utterances for content-policy violations, determining the severity of these violations based on factors like pronouns, proper nouns, and anaphoras, and adjusting scores accordingly, followed by application-management actions to address these violations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system analyzes user utterances for content-policy violations using multiple factors (pronouns, proper nouns, anaphoras), then the measurement precision of violation detection is improved, but the device complexity increases
Solution Approach 1:
The system segments the violation detection process into distinct components: a phrase-management component that identifies potential violations, a phrase evaluator that assesses severity using multiple factors (pronouns, proper nouns, anaphoras), and a permissive deviation component that applies contextual rules. This segmentation allows each component to specialize in specific detection tasks, improving overall measurement precision while managing complexity through modular design.
Solution Approach 2:
The phrase evaluator acts as an intermediary between the phrase-management component and the application-management component. It receives raw phrase data, evaluates it against multiple factors including pronouns, proper nouns, and anaphoras, and produces a standardized severity score. This intermediary layer enables precise multi-factor analysis without directly increasing the complexity of the overall system architecture.
2Reliability
If the system implements comprehensive content-policy violation analysis with severity scoring, then the reliability of content filtering is improved, but the processing time increases
Solution Approach 1:
The phrase-management component performs preliminary action by identifying and flagging potential policy violations before they reach the evaluation stage. It pre-processes user utterances to extract candidate phrases that may violate content policies, filtering out obvious safe content early in the pipeline. This preliminary filtering reduces the number of phrases requiring full severity evaluation, thereby maintaining high reliability while reducing overall processing time.
Solution Approach 2:
The system applies partial action by evaluating only the necessary factors for each phrase based on its context. The phrase evaluator selectively analyzes pronouns, proper nouns, and anaphoras only when relevant to the specific phrase being evaluated, rather than uniformly applying all factors to every phrase. This selective evaluation maintains filtering reliability while minimizing processing time by avoiding unnecessary analysis.
3Adaptability or versatility
If the system adjusts severity scores based on contextual factors (pronouns, proper nouns, anaphoras), then the adaptability of policy enforcement is improved, but the difficulty of detecting and measuring violations increases
Solution Approach 1:
The system implements parameter changes by dynamically adjusting the severity score based on contextual parameters such as the presence and type of pronouns, proper nouns, and anaphoras. The phrase evaluator modifies the base severity score according to these linguistic parameters, allowing the same policy violation to receive different severity ratings depending on context. This parameter-based approach improves adaptability while keeping the detection process systematic and measurable through defined adjustment rules.
Data Source
AI summary
Systems and methods for natural language processing policies are disclosed. For example, content such as a response to a user command and/or user input such as a customer review, may be analyzed to determine if the content deviates from one or more content policies and to determine a severity of the content-policy deviation(s). A system may determine a number of content-policy deviations associated with the content and whether the content includes an indicator of a person, such as a pronoun and/or a proper noun. Content that includes multiple content-policy deviations and/or that includes an indicator of a person may be determined to be more severe than other deviated content. Applications associated with deviated content may be ranked and corrective action may be taken based on the ranking.