- The paper introduces a dynamic neural module network that composes tailored networks for diverse QA tasks across images and knowledge bases.
- It leverages reinforcement learning to jointly optimize module parameters and network layouts based on natural language inputs.
- The approach achieves state-of-the-art performance with 59.4% accuracy on VQA and 54.3% on GeoQA benchmarks.
Learning to Compose Neural Networks for Question Answering
This paper presents a compositional, attentional model designed to address the question answering (QA) task across diverse data modalities, specifically images and structured knowledge bases (KBs). The approach involves dynamically assembling neural networks using natural language inputs to form composable modules, a method termed the Dynamic Neural Module Network (D-NMN). The parameters for these modules, along with the network assembly parameters, are optimized through reinforcement learning. The training relies solely on (world, question, answer) triples, bypassing the need for explicit network layout supervision.
Model Overview
The proposed model integrates two main components: neural modules tailored for specific tasks, and a network layout predictor that determines the assembly of these modules into a complete network for each question. Unlike previous models that depended on manually specified structures, this paper introduces a fully differentiable approach that extends modular reasoning to structured data sets.
Key features of the approach include:
- Composable Modules: Modular neural network components that can be flexibly assembled to handle multiple question types.
- Network Layout Predictor: Learns to organize modules based on the syntactic analysis of input questions.
- Reinforcement Learning: Utilized to jointly train both module and layout components, improving adaptability to both image-based and knowledge-base QA tasks.
Numerical Results and Claims
The model demonstrates state-of-the-art results on benchmark datasets across both visual and structured query types. Specifically, it shows competitive performance on the VQA dataset, achieving an accuracy of 59.4% on the test-standard set, outperforming fixed-structure models. Additionally, it achieves a 54.3% accuracy on GeoQA, a significant improvement over traditional logical and learned predicate models.
Implications and Future Directions
The ability of the D-NMN to simultaneously learn and execute instance-specific network structures represents an advancement in bridging the gap between structured logical approaches and the flexibility of neural networks. By leveraging continuous representations, the model simplifies the historically complex task of semantic parsing, enhancing generalization and efficiency.
Looking forward, the principles established in this work have implications beyond QA, potentially influencing areas such as instruction following and game playing. The dynamic assembly feature offers a path toward more adaptive AI systems capable of complex reasoning over varied input representations. Future research could explore the extension of this approach to other tasks demanding dynamic computational structuring, further enhancing the versatility and intelligence of neural network models.
