Multitask Learning Question Answering Architecture
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Solution Overview
Problem
Current natural language processing systems face challenges in generalizing across different types of tasks, such as language translation, question answering, and classification, due to limited parameter sharing and task-specific optimizations, which hinders their ability to handle diverse natural language processing tasks effectively.
Innovation Solution
A unified system and method for multitask learning is proposed, where all tasks are framed as question answering tasks, using a computing device with a processor and memory that implements a question answering module. This module employs word encoding, bidirectional long short-term memory networks, coattention layers, and multi-layer self-attention-based transformers to generate answers, and employs a hybrid training strategy to mitigate catastrophic forgetting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If task-specific optimizations are used for different NLP tasks, then performance on each specific task is improved, but the system's ability to generalize across different task types deteriorates
Solution Approach 1:
The patent applies universality by framing all NLP tasks (translation, question answering, classification) as question answering tasks with a unified architecture. The system uses a single set of parameters and a common processing pipeline that can handle multiple task types, eliminating the need for separate task-specific models while maintaining performance across different tasks.
Solution Approach 2:
The patent employs parameter changes by dynamically adjusting the question answering module's behavior based on task type through prompting strategies. The same underlying parameters are reused across tasks, but the way questions are formulated and answers are generated is adapted to match the specific task requirements, allowing one parameter set to serve multiple functions effectively.
2Adaptability or versatility
If a unified architecture is used for all NLP tasks, then cross-task generalization is improved, but performance on specific tasks may deteriorate due to lack of task-specific optimization
Solution Approach 1:
The patent applies local quality by customizing the prompting strategy and answer generation approach for each specific task while using the same underlying architecture. For example, translation tasks use prompts that expect translated text, while question answering tasks use prompts that expect direct answers. This allows the unified system to maintain task-specific performance characteristics through localized prompt engineering rather than requiring task-specific models.
3Adaptability or versatility
If multiple task types are handled simultaneously, then system versatility is improved, but the complexity of the system increases
Solution Approach 1:
The patent applies merging by combining multiple NLP tasks into a single unified question answering framework. Instead of having separate models for translation, question answering, and classification, the system merges all these functions into one architecture that processes inputs through a common pipeline. This reduces overall system complexity by eliminating redundant components while maintaining multi-task capability.
Data Source
AI summary
Approaches for multitask learning as question answering include an input layer for encoding a context and a question, a self-attention based transformer including an encoder and a decoder, a first bi-directional long-term short-term memory (biLSTM) for further encoding an output of the encoder, a long-term short-term memory (LSTM) for generating a context-adjusted hidden state from the output of the decoder and a hidden state, an attention network for generating first attention weights based on an output of the first biLSTM and an output of the LSTM, a vocabulary layer for generating a distribution over a vocabulary, a context layer for generating a distribution over the context, and a switch for generating a weighting between the distributions over the vocabulary and the context, generating a composite distribution based on the weighting, and selecting a word of an answer using the composite distribution.