Context-Based Intelligent Agent for Domain-Specific Question Answering
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Solution Overview
Problem
Existing question answering systems struggle to scale to different domains and handle domain-specific questions effectively, relying on labor-intensive and biased handcrafted datasets, which limits their accuracy and diversity in responding to user queries.
Innovation Solution
An automatic question answering system that utilizes a machine comprehension model trained on domain-specific documents, employing unsupervised multi-lingual keyword extraction and a Seq2Seq model with the Bandanau attention mechanism to generate relevant questions and identify answer spans within specific contexts, allowing for the creation of domain-specific datasets and improving the system's ability to handle diverse user queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If handcrafted datasets are used for training QA systems, then domain-specific accuracy can be improved, but labor intensity and bias increase
Solution Approach 1:
The system automatically generates domain-specific training datasets by extracting questions and answers from domain documents using NLP techniques, eliminating the need for manual dataset creation. The machine comprehension model self-trains on generated data, making the system self-sufficient in creating its own training materials.
Solution Approach 2:
An automatic question generation module acts as an intermediary between domain documents and the QA training data. This module extracts questions and candidate answers from unstructured domain texts, transforming them into structured training datasets without requiring manual intervention.
2Measurement precision
If handcrafted datasets are used for training QA systems, then domain-specific accuracy can be improved, but bias in responses increases
Solution Approach 1:
The system automatically generates diverse training examples from domain documents using algorithmic question generation, reducing human bias. The machine comprehension model learns from automatically generated diverse questions and answers, minimizing the bias present in manually crafted datasets.
3Ease of operation
If generic QA models are used, then ease of deployment is improved, but ability to handle domain-specific questions deteriorates
Solution Approach 1:
The system dynamically adapts to different domains by automatically generating domain-specific training data from provided domain documents. The machine comprehension model is fine-tuned on domain-specific generated datasets, enabling it to adapt its capabilities to various domains while maintaining the same underlying architecture.
Solution Approach 2:
The system separates the domain adaptation process into distinct stages: domain document processing, automatic question generation, model training, and evaluation. This segmentation allows the generic model to be systematically adapted to specific domains through automated pipeline processing.
4Measurement precision
If more domain-specific training data is created manually, then QA accuracy improves, but time consumption increases
Solution Approach 1:
The system automatically generates extensive domain-specific training data from provided domain documents using NLP techniques. This self-service approach creates large volumes of training examples without manual intervention, significantly reducing the time required compared to manual dataset creation while maintaining high QA accuracy.
Solution Approach 2:
The system performs preliminary automatic question generation and answer extraction from domain documents before model training. This preliminary action creates ready-to-use training datasets, eliminating the need for time-consuming manual data preparation and annotation processes.
Data Source
AI summary
An automatic question answering system serves to respond to questions pertaining to a user-selected domain of a plurality of domains. A plurality of documents pertaining to the domain are received and processed to identify candidate answers. Questions corresponding to the candidate answers are automatically generated. The candidate answers and the questions are used to train a machine comprehension (MC) model. A user interface allows the selection of one of the plurality of domains for querying by a user. When a user query pertaining to the selected domain is received, the user query is analyzed to identify a relevant context from the plurality of documents. An answer span to the user query identified by the MC model from the relevant context is used to frame a complete response to the user query.