Fictional Character Response Generation with Context Retrieval
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Solution Overview
Problem
Existing methods face challenges in defining and training chatbots to mimic the conversational styles of fictional characters due to difficulties in character definition, discrete style representation, and insufficient dialogue data.
Innovation Solution
Utilizing a retrieval model to select pseudo-contexts based on bi-encoder embeddings and leveraging large-scale language models through few-shot learning to generate responses that reflect the style and personality of fictional characters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If typical methods are used to mimic fictional characters, then the chatbot can generate responses, but it is difficult to define the fictional character with just a few sentences
Solution Approach 1:
The system pre-collects and stores multiple contexts related to fictional characters in a database before actual use. When a user wants to mimic a character, the system retrieves pre-prepared contexts that match the character's personality and speech patterns, eliminating the need to define characters from scratch and ensuring high definition accuracy with minimal user input.
Solution Approach 2:
The system copies and stores actual dialogue contexts from source materials (books, movies, etc.) into the database. These copied contexts serve as templates that the chatbot can reference to mimic character speech patterns accurately, allowing faithful reproduction of character personalities without requiring extensive manual definition.
2Adaptability or versatility
If discrete styles (happy, sad, nervous) are used to define conversation style, then the character can be categorized, but it becomes difficult to capture nuanced conversational patterns
Solution Approach 1:
The system segments conversational contexts into multiple dimensional attributes including emotion, topic, speech pattern, and contextual situation. Instead of using single discrete style labels, the system breaks down character speech into various components stored in the context database, allowing nuanced and accurate reconstruction of conversational patterns through combination of these segmented elements.
3Reliability
If a large amount of dialogue data is collected for training, then the chatbot can learn character styles, but it becomes difficult to obtain sufficient dialogue data for fictional characters
Solution Approach 1:
The system creates a universal context database that serves multiple functions: it stores dialogue data for training, provides retrieval examples for few-shot learning, and acts as a knowledge base for character information. This multi-functional database eliminates the need to separately collect and process training data, reducing data collection difficulty while ensuring sufficient training material.
4Ease of manufacture
If few-shot learning is used with language models, then the chatbot can generate character-like responses with minimal data, but the system complexity increases due to retrieval model and context matching
Solution Approach 1:
The system performs preliminary organization of character contexts and speech patterns into a structured database with clear indexing and categorization before the chatbot needs to generate responses. This pre-processing work, though requiring initial system setup, simplifies the real-time generation process by providing ready-to-use templates and examples that the language model can directly reference during few-shot learning.
Data Source
AI summary
Systems and techniques to generate imitative responses are illustrated. response generation method performed in an electronic apparatus of the present disclosure includes acquiring at least one piece of utterance data, acquiring a first context corresponding to the utterance data from a context candidate set, generating one or more dialogue sets including the first context and the utterance data, receiving a second context from a user, and acquiring a response corresponding to the second context using a language model based on the one or more dialogue sets.