Papers with Neural module networks
VGNMN: Video-grounded Neural Module Networks for Video-Grounded Dialogue Systems (2022.naacl-main)
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| Challenge: | Neural module networks (NMN) have been used in image-grounded tasks such as Visual Question Answering (VQA) however, very limited work on NMN has been studied in the video-ground dialogue tasks. |
| Approach: | They propose to use video as the grounding feature in video-grounded dialogues to model the information retrieval process in videogrounded language tasks as a pipeline of neural modules. |
| Outcome: | The proposed model can achieve promising performance on video-grounded dialogue and QA benchmarks. |
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. |
Mind the Context: The Impact of Contextualization in Neural Module Networks for Grounding Visual Referring Expressions (2021.emnlp-main)
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| Challenge: | Prior implementations of NMN use pre-defined and fixed textual inputs in their module instantiation. |
| Approach: | They propose to parameterize the module arguments to reduce the number of modules in NMN by up to 75% without any loss in performance. |
| Outcome: | The proposed model outperforms the state-of-the-art model on CLEVR-Ref+ dataset with +8.1% improvement in accuracy and +4.3% on full test set. |