Partially Supervised Numeric Reasoning Networks for Open-Ended QA
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Contemporary Neural Module Networks (NMNs) require exhaustive supervision for numerical reasoning, which is time-consuming and expensive, and fail to generalize effectively in open-ended settings without such supervision.
Innovation Solution
A partially supervised numeric reasoning module network using a weakly supervised neuro-symbolic module network (WNSMN) that learns to execute noisy heuristic programs through dependency parsing and reinforcement learning with answers as sole supervision, incorporating discrete reasoning and neural modules for end-to-end training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exhaustive supervision is used for numerical reasoning in Neural Module Networks, then reasoning accuracy is improved, but data annotation cost and time consumption increase
Solution Approach 1:
The patent applies partial supervision by providing only answer labels rather than complete reasoning traces for training data. The model learns to execute noisy heuristic programs through reinforcement learning, where only the final answer is supervised while the reasoning process is learned through exploration and feedback, significantly reducing annotation requirements while maintaining reasoning capability
Solution Approach 2:
The system performs self-training through reinforcement learning where the model generates its own training data by executing heuristic programs and receiving feedback based on answer correctness. This self-supervised mechanism eliminates the need for manual annotation of reasoning traces, allowing the model to learn from its own performance
2Reliability
If exhaustive supervision is provided for numerical reasoning, then model performance on closed-ended queries is improved, but generalization to open-ended settings deteriorates
Solution Approach 1:
The patent employs dynamic program execution where the model learns to execute noisy heuristic programs through reinforcement learning. This dynamic approach allows the model to adapt its reasoning strategy based on the specific query, enabling generalization to open-ended settings while maintaining performance on closed-ended queries through the same unified framework
Solution Approach 2:
The model achieves multi-functionality by handling both closed-ended and open-ended queries through a single unified architecture that executes heuristic programs. The same reinforcement learning framework trains the model to perform numerical reasoning across diverse question types, eliminating the need for separate specialized models for different query formats
Data Source
AI summary
Embodiments described herein provide systems and methods for a partially supervised training model for questioning answering tasks. Specifically, the partially supervised training model may include two modules—a query parsing module and a program execution module. The query parsing module parses queries into a grogram, and the program execution module execute the program to reach an answer through explicit reasoning and partial supervision. In this way, the partially supervised training model can be trained with answers as supervision, obviating the need for supervision by gold program operations and gold query-span attention at each step of the program.