Reader-Retriver Question Answering for Open-Domain Accuracy
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Solution Overview
Problem
Current question answering systems, particularly open-domain question answering (OpenQA), face challenges in achieving high accuracy and efficiency due to the efficiency-accuracy trade-off in retriever-reader models and limitations in knowledge graph-based systems, which restrict the power of the reader and result in unsuitable delays and limited question coverage.
Innovation Solution
A reader-retriever approach is employed, where the reader processes a corpus of text to generate question and answer spaces, allowing the retriever to retrieve answers from pre-constructed spaces, enabling offline generation of candidate question and answer pairs for improved accuracy and coverage, and combining this with retriever-reader approaches for enhanced performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If a retriever is designed to be computationally efficient, then the processing speed is improved, but the reliability of retrieved results deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing answer representations from the corpus before query processing. The reader component generates and stores answer representations in advance, so that during query processing, the system only needs to retrieve and compare representations rather than performing full text analysis, thus achieving both speed and reliability
Solution Approach 2:
The patent segments the question answering process into distinct components: the reader that processes the corpus offline to generate answer representations, and the retriever that handles online queries by comparing query representations with pre-generated answer representations. This segmentation allows each component to be optimized independently for its specific function
2Reliability
If a retriever is made highly complex to provide reliable retrieved results, then the reliability of retrieved results is improved, but the processing time increases unacceptably
Solution Approach 1:
The system performs the computationally intensive work of generating answer representations in advance during an offline phase. The reader processes the entire corpus beforehand, creating a repository of answer representations that can be quickly retrieved and matched during online query processing, thus avoiding time-consuming operations during actual query handling
Solution Approach 2:
The patent creates copies of answer representations from the original corpus text. Instead of processing the full text during query retrieval, the system works with pre-generated answer representations that capture the essential information, enabling fast comparison and matching while maintaining accuracy
3Productivity
If knowledge graph-based systems are used to restrict the reader's processing, then the processing efficiency is improved, but the question coverage is limited
Solution Approach 1:
The patent creates a universal answer representation system that can handle diverse question types without being constrained by predefined knowledge graph schemas. The reader generates answer representations for various types of questions from the corpus, and the retriever can match different query formats against these representations, providing broad question coverage while maintaining efficiency
Data Source
AI summary
Techniques and systems are provided for predicting answers in response to one or more input queries. For instance, text from a corpus of text can be processed by a reader to generate one or multiple question and answer spaces. A question and answer space can include answerable questions and the answers associated with the questions (referred to as “question and answer pairs”). A query defining a question can be received (e.g., from a user input device) and processed by a retriever portion of the system. The retriever portion of the system can retrieve an answer to the question from the one or more pre-constructed question and answer spaces, and/or can determine an answer by comparing one or more answers retrieved from the one or more pre-constructed question and answer spaces to an answer generated by a retriever-reader system.