Chatbot Response Styling via Domain Word-Graphs
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Solution Overview
Problem
Conventional chatbots fail to generate responses that mimic specific speaking styles or personas, limiting their ability to engage users effectively in domain-specific conversations.
Innovation Solution
A computerized framework that constructs domain-specific word-graphs using tweets from specific domains to transform regular chatbot responses, incorporating stylized word-patterns that mimic native styles, while maintaining factual accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional chatbots use standard response generation, then factual accuracy is maintained, but domain-specific speaking styles cannot be mimicked
Solution Approach 1:
The response generation process is segmented into two independent modules: a factual response generation module that ensures accuracy, and a style transformation module that applies domain-specific speaking patterns. This segmentation allows each module to optimize for its specific function without compromising the other.
Solution Approach 2:
A style transformation layer acts as an intermediary between the factual chatbot response and the final output. This intermediary takes the accurate factual response and transforms it by injecting domain-specific linguistic patterns, slang, and speaking styles without altering the underlying factual content.
2Ease of operation
If chatbots transform responses to mimic native styles, then user engagement is enhanced, but response accuracy may be compromised
Solution Approach 1:
The transformation is applied locally to specific linguistic features (word choices, sentence structures, slang terms) rather than the entire response content. This allows the factual core to remain precise while only the stylistic elements are modified to enhance engagement.
Solution Approach 2:
The system changes linguistic parameters (tone, vocabulary, sentence complexity) of the response without changing the factual parameters. This allows the response to adapt its style to match domain experts while maintaining the same informational content and accuracy.
3Adaptability or versatility
If chatbots use general human-like behavior modeling, then conversational capability is achieved, but specific persona styles cannot be replicated
Solution Approach 1:
The system copies linguistic patterns, word choices, and stylistic features from example texts of domain experts rather than attempting to model their complete cognitive processes. This copying approach captures the essence of specific personas with relatively simple transformation rules.
Solution Approach 2:
The style transformation module is designed to be universal and can adapt to multiple different domain styles by loading different style profiles. A single system architecture serves multiple functions by transforming responses to match politicians, scientists, artists, or other domain experts based on the selected style profile.
Data Source
AI summary
Disclosed are systems and methods for improving interactions with and between computers in content searching, generating, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The disclosure provides a computerized framework for automatically generating chatbot responses to produce domain-specific responses that mimic native styles unique to particular domains. The disclosed systems and methods construct domain-specific word-graphs based on account activity from specific domains and generate word-patterns. New words obtained from the patterns in the graph are introduced to transform the regular response. The graph is then pruned using data-driven thresholds in order to avoid spurious transformations, and paragraph vectors are also utilized to assign relevance scores to generated patterns such that only the patterns that are contextually similar to the original response (generic/regular response) are used. As result, the regular chatbot response is rewritten using an optimized set of patterns.