Papers with Neural Module Networks
Structure Learning for Neural Module Networks (D19-64)
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| Challenge: | Neural Module Networks are a class of neural networks that involve human-specified neural modules . current models only learn the parameters of the modules and/or the order of their execution . |
| Approach: | They propose to learn internal structure and sequence without extra supervisory signals . they use dynamically composable modules which are then assembled into a layout . |
| Outcome: | The proposed model performs comparable to models using hand-designed modules. |
Teaching Neural Module Networks to Do Arithmetic (2022.coling-1)
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| Challenge: | Neural Module Networks (NMNs) have limited reasoning abilities and lack numerical reasoning capability. |
| Approach: | They propose to integrate the original question in the interpreter and introduce addition and subtraction modules that perform numerical reasoning over numbers. |
| Outcome: | The proposed methods outperform previous state-of-the-art models on a subset of DROP and achieve competitive reasoning performance. |
Improving Numerical Reasoning Skills in the Modular Approach for Complex Question Answering on Text (2021.findings-emnlp)
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| Challenge: | Neural Module Networks (NMNs) is an end-to-end differentiable model in the programmer-interpreter paradigm. |
| Approach: | They propose to make the interpreter question-aware and capture the relationship between entities and numbers in both questions and paragraphs. |
| Outcome: | The proposed models outperform the original models on the DROP dataset and are interpertable by nature. |