Neural IR Index for Virtual Assistant Annotation
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Solution Overview
Problem
Current methods for building virtual assistants fall short as they do not effectively utilize domain knowledge from content authors to facilitate conversational annotation, leading to inefficient creation of conversation models.
Innovation Solution
A cooperative build and content annotation system that formulates a build context, generates content queries, searches a neural Information Retrieval index, and provides recommendations for enhancing virtual assistant models through joint and cooperative methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automatic annotation of documented content using known conversational meta-models is used, then annotation efficiency is improved, but annotation quality and relevance to build context deteriorate
Solution Approach 1:
The system introduces an intermediary neural Information Retrieval index that acts as a bridge between the build context and the documented content. This intermediary translates build context into semantic queries and retrieves relevant content annotations, thereby maintaining both efficiency and quality. The intermediary layer enables automatic annotation while preserving relevance to the specific build context.
Solution Approach 2:
The system changes the parameter of content representation from traditional text-search indexing to neural Information Retrieval indexing. This parameter change enables the system to understand semantic meaning and context relevance, thereby improving annotation quality while maintaining automatic annotation efficiency. The neural index transforms how content is stored and retrieved, allowing for more precise matching with build context.
2Ease of manufacture
If content authors produce enterprise content without focusing on conversational structure, then content creation ease is improved, but conversational annotation utility deteriorates
Solution Approach 1:
The system creates a universal annotation framework that serves multiple functions: it annotates content for conversational use while preserving the original content structure. The neural Information Retrieval index enables the same annotated content to serve both as reference material for content authors and as training data for conversational models, thereby achieving multi-functionality without restricting content authors.
Solution Approach 2:
The system creates a semantic copy of the content through neural embeddings in the Information Retrieval index. This copy captures the meaning and context of the original content without requiring authors to change their writing style or structure. The semantic copy can be queried and matched with build context, providing conversational annotation utility while leaving the original content creation process unchanged.
3Device complexity
If traditional text-search index is used for content retrieval, then system complexity is reduced, but content retrieval accuracy for build context deteriorates
Solution Approach 1:
The system creates a composite indexing structure that combines traditional text-search indexing with neural Information Retrieval indexing. This composite approach leverages the simplicity and speed of traditional indexing while incorporating the semantic understanding capabilities of neural methods. The hybrid structure retrieves content with higher accuracy for build context while maintaining reasonable system complexity through modular integration.
Data Source
AI summary
In an approach for a cooperative build and content annotation system for conversational design of virtual assistants, a processor formulates a build context based on a build activity of a user. A processor formulates one or more content queries based on the build context. A processor builds a content index by augmenting a text-search index with a neural Information Retrieval (IR) index. A processor searches the content index using the one or more content queries to identify content relevant to the build context. A processor determines at least one recommendation for the user based on heuristic rules applied to the build context and the identified content, wherein each recommendation is a build suggestion or a content annotation suggestion.