Explainable Neural Computation via Stack Neural Module Networks (1807.08556v3)

Abstract: In complex inferential tasks like question answering, machine learning models must confront two challenges: the need to implement a compositional reasoning process, and, in many applications, the need for this reasoning process to be interpretable to assist users in both development and prediction. Existing models designed to produce interpretable traces of their decision-making process typically require these traces to be supervised at training time. In this paper, we present a novel neural modular approach that performs compositional reasoning by automatically inducing a desired sub-task decomposition without relying on strong supervision. Our model allows linking different reasoning tasks though shared modules that handle common routines across tasks. Experiments show that the model is more interpretable to human evaluators compared to other state-of-the-art models: users can better understand the model's underlying reasoning procedure and predict when it will succeed or fail based on observing its intermediate outputs.

• The paper introduces Stack Neural Module Networks (Stack-NMNs) for performing interpretable compositional reasoning in tasks like visual question answering without requiring explicit supervision of reasoning stages.
• A core innovation is a differentiable stack-based data structure that organizes reasoning tasks into modular sub-tasks, eliminating the need for costly expert layouts required by previous models.
• Evaluations on CLEVR show Stack-NMNs achieve high human interpretability and competitive accuracy, demonstrating potential for multi-task learning and applications requiring trusted, transparent AI systems.

Explainable Neural Computation via Stack Neural Module Networks

Stack Neural Module Networks (Stack-NMNs) illustrate a significant advancement in neural computation by addressing both compositional reasoning and interpretability in complex tasks such as visual question answering (VQA) and referential expression grounding (REF). The paper presents a novel neural modular approach designed to perform compositional reasoning processes without relying on explicit supervision of reasoning stages. Unlike prior models requiring strong supervision, Stack-NMNs induce a modular reasoning layout that remains interpretable to human evaluators.

The key innovation in this paper is the implementation of a differentiable stack-based data structure that organizes reasoning tasks into modular sub-tasks realized through shared modules. This novel design eliminates the necessity for expert layouts, which are necessary in previous models with comparable goals, such as N2NMN or PG+EE. This feature represents a considerable advancement in the usability and adaptability of neural models in real-world applications where generating expert layouts can be infeasible or expensive.

The evaluation on the CLEVR dataset reveals that Stack-NMNs achieve high interpretability and accuracy in VQA tasks. Tested both with and without expert supervision, the model exhibits superior performance in terms of human interpretability compared to state-of-the-art non-modular models such as MAC. The interpretability was evaluated through subjective understanding and forward prediction tasks, where human evaluators could discern the reasoning steps and accurately predict model outcomes. This alignment of automated reasoning with human cognitive processes is vital for developing potential applications where machine learning systems collaborate with human users.

Furthermore, Stack-NMNs have been shown to seamlessly integrate and optimize across multiple tasks by performing joint training on VQA and REF tasks. The paper's results indicate that shared routine modules across tasks enhance both the accuracy and the model’s ability to generalize across different tasks. This multi-task learning potential is critical for future models that aim to replicate the human ability to apply overlapping cognitive processes across diverse contexts.

Despite not achieving absolute dominance in raw test performance compared to top non-modular models, Stack-NMNs complement performance with enhanced transparency in their decision-making pathways. The trade-off between interpretability and raw accuracy surfaces as an enduring theme in AI research, with Stack-NMNs weightily contributing to this discourse by improving interpretability substantially while maintaining competitive accuracy.

The implications of this research extend to practical models requiring human trust and comprehension, including those deployed in clinical, legal, or autonomous systems where interpretability is paramount. As such, one can speculate that the future development of artificial intelligence may increasingly focus on the alignment between machine logic and human interpretability, as seen with Stack-NMNs. This necessitates that future advancements in AI be evaluated not only for numerical accuracy but also in terms of cognitive alignment with human reasoning processes.

In conclusion, "Explainable Neural Computation via Stack Neural Module Networks" demonstrates considerable progress toward creating machine learning models that are interpretable, efficient, and effective across varied tasks. The Stack-NMN presents a promising direction for developing models that balance complexity and interpretability, poised for extensive application across domains that require transparent and collaborative AI systems.