Virtual Assistant Pun Recognition With Humor Explanation Models
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Solution Overview
Problem
Existing humor recognition and generation systems for virtual assistants lack robust datasets and effective methods 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 is formulated to train models that recognize whether and why text is funny, and generate humorous text based on contextual keywords, incorporating techniques like T5 and BERT models for pun explanation and classification, along with a dialog management system to inject humor appropriately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing humor recognition and generation systems are used, then basic humor functionality is provided, but the ability to understand and generate humorous text particularly puns is limited due to sparse supervision signals and coarse-grained annotations
Solution Approach 1:
The humor annotation task is segmented into multiple levels of granularity. Coarse-grained annotations identify whether text is humorous, while fine-grained annotations specifically identify puns, jokes, and their components (setup, punchline, delivery). This multi-level segmentation allows the system to learn from abundant data while maintaining precise supervision signals for different humor types.
Solution Approach 2:
The patent introduces a new dimension to humor annotation by adding contextual information and semantic relationships between words. Instead of only annotating whether text is humorous, the system annotates the semantic structure, word relationships, and contextual factors that make text humorous, thereby enriching the supervision signals without increasing data volume.
2Adaptability or versatility
If comprehensive humor datasets and advanced models are implemented, then contextually relevant humor generation is improved, but system complexity increases
Solution Approach 1:
The humor management system is designed as a multi-functional module that can both recognize existing humor in user input and generate appropriate humorous responses. The same annotated data and processing framework serve dual purposes: analyzing user humor and generating system humor, thereby reducing overall system complexity while enhancing versatility.
Solution Approach 2:
The patent introduces an intermediary layer of humor annotations and processed data that mediates between raw text input and the AI models. This intermediary layer pre-processes and structures the data, making it easier for the models to handle and reducing the complexity of direct model-training requirements.
3Ease of operation
If humor recognition and generation capabilities are added to virtual assistants, then user interaction quality is improved, but the ability to interact naturally with users is limited without robust datasets
Solution Approach 1:
The patent performs preliminary annotation and processing of humor data before it is used for training models. By pre-annotating datasets with detailed humor metadata, semantic relationships, and contextual information, the system prepares the data in advance, reducing the volume of raw data needed while maintaining high training quality and improving natural interaction capabilities.
Data Source
AI summary
In accordance with one disclosed method, first data representing first text may be processed using a first machine learning model configured to generate second data representing an explanation as to why the first text is humorous. The second data may be processed, together with the first data, by a second machine learning model configured to generate a value indicating that the first data and the second data correspond to a pun. A device may be caused to take at least a first action based at least in part on the value.