@inproceedings{subramanian-etal-2020-obtaining,
title = "Obtaining Faithful Interpretations from Compositional Neural Networks",
author = "Subramanian, Sanjay and
Bogin, Ben and
Gupta, Nitish and
Wolfson, Tomer and
Singh, Sameer and
Berant, Jonathan and
Gardner, Matt",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.495",
doi = "10.18653/v1/2020.acl-main.495",
pages = "5594--5608",
abstract = "Neural module networks (NMNs) are a popular approach for modeling compositionality: they achieve high accuracy when applied to problems in language and vision, while reflecting the compositional structure of the problem in the network architecture. However, prior work implicitly assumed that the structure of the network modules, describing the abstract reasoning process, provides a faithful explanation of the model{'}s reasoning; that is, that all modules perform their intended behaviour. In this work, we propose and conduct a systematic evaluation of the intermediate outputs of NMNs on NLVR2 and DROP, two datasets which require composing multiple reasoning steps. We find that the intermediate outputs differ from the expected output, illustrating that the network structure does not provide a faithful explanation of model behaviour. To remedy that, we train the model with auxiliary supervision and propose particular choices for module architecture that yield much better faithfulness, at a minimal cost to accuracy.",
}
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<abstract>Neural module networks (NMNs) are a popular approach for modeling compositionality: they achieve high accuracy when applied to problems in language and vision, while reflecting the compositional structure of the problem in the network architecture. However, prior work implicitly assumed that the structure of the network modules, describing the abstract reasoning process, provides a faithful explanation of the model’s reasoning; that is, that all modules perform their intended behaviour. In this work, we propose and conduct a systematic evaluation of the intermediate outputs of NMNs on NLVR2 and DROP, two datasets which require composing multiple reasoning steps. We find that the intermediate outputs differ from the expected output, illustrating that the network structure does not provide a faithful explanation of model behaviour. To remedy that, we train the model with auxiliary supervision and propose particular choices for module architecture that yield much better faithfulness, at a minimal cost to accuracy.</abstract>
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%0 Conference Proceedings
%T Obtaining Faithful Interpretations from Compositional Neural Networks
%A Subramanian, Sanjay
%A Bogin, Ben
%A Gupta, Nitish
%A Wolfson, Tomer
%A Singh, Sameer
%A Berant, Jonathan
%A Gardner, Matt
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F subramanian-etal-2020-obtaining
%X Neural module networks (NMNs) are a popular approach for modeling compositionality: they achieve high accuracy when applied to problems in language and vision, while reflecting the compositional structure of the problem in the network architecture. However, prior work implicitly assumed that the structure of the network modules, describing the abstract reasoning process, provides a faithful explanation of the model’s reasoning; that is, that all modules perform their intended behaviour. In this work, we propose and conduct a systematic evaluation of the intermediate outputs of NMNs on NLVR2 and DROP, two datasets which require composing multiple reasoning steps. We find that the intermediate outputs differ from the expected output, illustrating that the network structure does not provide a faithful explanation of model behaviour. To remedy that, we train the model with auxiliary supervision and propose particular choices for module architecture that yield much better faithfulness, at a minimal cost to accuracy.
%R 10.18653/v1/2020.acl-main.495
%U https://aclanthology.org/2020.acl-main.495
%U https://doi.org/10.18653/v1/2020.acl-main.495
%P 5594-5608
Markdown (Informal)
[Obtaining Faithful Interpretations from Compositional Neural Networks](https://aclanthology.org/2020.acl-main.495) (Subramanian et al., ACL 2020)
ACL
- Sanjay Subramanian, Ben Bogin, Nitish Gupta, Tomer Wolfson, Sameer Singh, Jonathan Berant, and Matt Gardner. 2020. Obtaining Faithful Interpretations from Compositional Neural Networks. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5594–5608, Online. Association for Computational Linguistics.