Virtual Assistant Humor Management Through Contextual Pun Generation
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Solution Overview
Problem
Existing humor recognition and generation systems for virtual assistants lack robust datasets and effective models to understand and generate humorous text, particularly puns, due to sparse supervision signals and coarse-grained annotations, limiting their ability to interact naturally with users.
Innovation Solution
A comprehensive humor dataset and machine learning models, including a pun explanation model and a pun generation model, are developed to recognize and generate humorous text based on contextual keywords, enabling systems to detect and inject humor appropriately during user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If existing humor recognition and generation systems use sparse supervision signals and coarse-grained annotations, then the system complexity is reduced, but the measurement precision of humor detection deteriorates
Solution Approach 1:
The humor detection task is segmented into multiple sub-tasks: pun detection, humor type classification, and context relevance assessment. Each sub-task has its own specialized model and annotation scheme, allowing precise measurement of humor detection while managing overall system complexity through modular architecture.
Solution Approach 2:
The patent introduces a new dimension of analysis by annotating not only whether text is humorous but also the specific type of humor (puns, wordplay, sarcasm), the contextual relevance, and the user engagement potential. This multi-dimensional annotation scheme enables precise measurement without requiring a complete rewrite of the entire system.
2Productivity
If virtual assistants generate humorous content without contextual awareness, then the generation speed increases, but the adaptability to user context deteriorates
Solution Approach 1:
The system performs preliminary context analysis and keyword extraction before humor generation. By pre-processing the conversation history, identifying key entities, and determining the appropriate humor type in advance, the system can quickly generate contextually relevant humor without sacrificing adaptability.
Solution Approach 2:
An intermediary layer of context-aware selection is introduced between the humor templates and the user interaction. This intermediary component matches generated humor to the current conversation context using extracted keywords and semantic similarity, ensuring adaptability while maintaining efficient generation speeds.
3Measurement precision
If the system uses comprehensive humor datasets with fine-grained annotations, then the measurement precision of humor understanding improves, but the loss of time for data collection and processing increases
Solution Approach 1:
The comprehensive annotation task is segmented into multiple phases: initial coarse annotation of humor presence, followed by selective fine-grained annotation of humor types and contextual relevance only for samples that require deeper analysis. This segmentation enables high measurement precision for critical cases while reducing overall time investment.
Solution Approach 2:
Instead of annotating every possible aspect of every humorous example, the system applies partial annotation to the most critical dimensions (humor type, contextual relevance, user engagement) while leaving less critical aspects unannotated. This partial action approach achieves sufficient measurement precision without the time cost of complete annotation.
Data Source
AI summary
In accordance with one disclosed method, first data representing one or more first keywords may be processed by at least one first component to determine a first pair of words suitable for generating a pun. The first pair of words may then be processed using a first machine learning model configured to generate an output pun based at least in part on one or more input keywords and an input word pair such that the output pun is contextually related to the one or more input keywords. A first pun that is contextually related to the one or more first keywords may be received from the first machine learning model, and a first device may be caused to output a representation of the first pun.