Episodic Memory Reader for Streaming Data QA
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Solution Overview
Problem
Existing question-answering (QA) models face memory limitations when dealing with large-scale datasets, such as TriviaQA and video QA, leading to scalability issues and the need for document truncation, as they struggle to handle long documents and streaming data contexts that exceed system memory capacity.
Innovation Solution
A memory-based reinforcement learning method and system that uses an episodic memory reader (EMR) to encode streaming data into memory vector representations, compute relative importance, and dynamically replace less important data with semantic information, allowing for efficient storage and retrieval of relevant context in an external memory using a trained reinforcement learning model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing QA models use full context documents for answering questions, then answer accuracy is improved, but memory consumption exceeds hardware capacity
Solution Approach 1:
The patent extracts only the most relevant semantic information from full context documents using an episodic memory reader and external memory system. The reinforcement learning model selectively identifies and stores key semantic elements rather than retaining entire documents, thereby reducing memory consumption while preserving answer accuracy.
Solution Approach 2:
The patent transforms full context documents into compressed semantic representations by changing the parameter of information density. The external memory stores condensed semantic information that captures essential meaning while occupying significantly less memory space than original documents.
2Quantity of substance
If document truncation is applied to reduce memory usage, then memory consumption is reduced, but answer accuracy deteriorates
Solution Approach 1:
The patent employs a reinforcement learning model that uses feedback from answer performance to continuously optimize what semantic information to store in external memory. The model learns from past QA interactions to identify which truncated or partial information is sufficient for accurate answering, thereby maintaining accuracy while reducing memory usage.
Solution Approach 2:
The episodic memory reader performs preliminary extraction of semantic information from documents before the QA task occurs. This advance processing prepares condensed representations that are ready for rapid retrieval, eliminating the need to load full documents during actual questioning.
3Adaptability or versatility
If reinforcement learning is used to select minimal context, then scalability is improved, but computational complexity increases
Solution Approach 1:
The patent segments the complex RL problem into distinct components: an episodic memory reader for input processing, an external memory system for storage, and a RL model for selection. This segmentation allows each component to be optimized independently and processed in parallel, reducing overall computational complexity while maintaining scalability.
4Ease of manufacture
If rule-based memory replacement is used (e.g., LRU), then implementation simplicity is improved, but task performance is reduced
Solution Approach 1:
The patent implements a self-service memory management system where the reinforcement learning model autonomously decides what information to retain or replace in external memory based on task performance feedback. The system self-optimizes its memory usage patterns without requiring manual configuration or simple rule-based approaches, thereby achieving high task performance while maintaining reasonable implementation complexity.
Data Source
AI summary
Provided is a memory-based reinforcement learning method and system capable of storing optional information in streaming data. A question-answering (QA) method using memory-based reinforcement learning method includes receiving, in an episodic memory reader (EMR), streaming data about an input context that is input from a user; analyzing, in the EMR, the received streaming data and storing preset semantic information used for QA in an external memory; and, in response to an input of a question front the user, determining, in a pretrained QA model, an answer to the input question based on semantic information stored in the external memory.