Retrieval-Aware Question Decontextualization for Open-Domain Retrieval
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Solution Overview
Problem
Conventional machine learning-based question generation systems fail to generate unambiguous questions that can uniquely retrieve a small subset of documents from a corpus, especially in open domain scenarios, leading to ineffective training and retrieval in question answering applications.
Innovation Solution
A retrieval aware natural language question generation system that decontextualizes questions by adding terms from the document context, using a Detect Document Identifier, Question decontextualizer, and Retriever to ensure the decontextualized question can retrieve a unique subset of documents without requiring the context, employing a Text-to-Text Transfer Transformer model for intelligent term insertion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional machine learning-based question generation is used, then questions can be generated for training, but the questions are ambiguous and cannot retrieve a unique subset of documents
Solution Approach 1:
The system segments the question generation process into multiple components: a question generator that creates initial questions, a decontextualizer that adds identifying terms, and a retriever that validates uniqueness. This segmentation allows each component to specialize in one aspect, improving overall question specificity while managing complexity through modular design.
Solution Approach 2:
The system performs preliminary actions by first generating questions with context, then decontextualizing them by adding identifying terms before final retrieval validation. This preliminary processing ensures that questions are properly formatted and contain necessary identifying information before being used for training, resolving the ambiguity issue.
2Reliability
If questions are made specific to retrieve unique documents, then retrieval accuracy improves, but the question generation process becomes more complex
Solution Approach 1:
The system implements feedback through the retriever component that validates whether decontextualized questions retrieve the intended unique document. This feedback loop allows the system to iteratively improve question specificity by identifying which decontextualized questions successfully retrieve unique documents and adjusting the decontextualization process accordingly, thereby improving reliability while managing complexity through automated validation.
3Adaptability or versatility
If decontextualized questions are generated without context, then open domain retrieval is enabled, but the questions may lose important contextual information
Solution Approach 1:
The system extracts only the essential identifying terms from the context that are necessary for unique document retrieval, rather than removing all contextual information. The decontextualizer selectively adds these key terms to the question while discarding redundant contextual elements, enabling open domain retrieval while preserving the minimum necessary information for accurate identification.
Solution Approach 2:
The system applies local quality by adding identifying terms only in specific locations within the question where they are most effective for retrieval. Rather than uniformly adding all context terms, the decontextualizer strategically places terms that uniquely identify the target document, preserving question naturalness while enabling open domain retrieval capability.
Data Source
AI summary
Retrieval aware natural language question generation for open domain document retrieval is provided. In one aspect, a system for retrieval aware question generation includes: a question decontextualizer configured to decontextualize a question generated from a context of a target document by adding terms from the context into the question itself to create a decontextualized question, where the decontextualized question alone enables open domain document retrieval without a need for also providing the context. The system can also include a detect document identifier configured to find the terms in the context; and a retriever configured to retrieve documents from the corpus of documents using the decontextualized question. A method for retrieval aware question generation using the present system is also provided.