Papers with NMNs
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. |
Obtaining Faithful Interpretations from Compositional Neural Networks (2020.acl-main)
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Sanjay Subramanian, Ben Bogin, Nitish Gupta, Tomer Wolfson, Sameer Singh, Jonathan Berant, Matt Gardner
| Challenge: | Neural module networks (NMNs) are a popular approach for modeling compositionality but prior work implicitly assumed that the structure of the network modules provides a faithful explanation of the model’s reasoning. |
| Approach: | They propose to use auxiliary supervision to train a model with a structured model that can understand the reasoning process and make better choices for module architecture. |
| Outcome: | The proposed models on two datasets show that the proposed models do not provide a faithful explanation of model behaviour. |