@inproceedings{chen-etal-2021-explaining,
title = "Explaining Neural Network Predictions on Sentence Pairs via Learning Word-Group Masks",
author = "Chen, Hanjie and
Feng, Song and
Ganhotra, Jatin and
Wan, Hui and
Gunasekara, Chulaka and
Joshi, Sachindra and
Ji, Yangfeng",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.306",
doi = "10.18653/v1/2021.naacl-main.306",
pages = "3917--3930",
abstract = "Explaining neural network models is important for increasing their trustworthiness in real-world applications. Most existing methods generate post-hoc explanations for neural network models by identifying individual feature attributions or detecting interactions between adjacent features. However, for models with text pairs as inputs (e.g., paraphrase identification), existing methods are not sufficient to capture feature interactions between two texts and their simple extension of computing all word-pair interactions between two texts is computationally inefficient. In this work, we propose the Group Mask (GMASK) method to implicitly detect word correlations by grouping correlated words from the input text pair together and measure their contribution to the corresponding NLP tasks as a whole. The proposed method is evaluated with two different model architectures (decomposable attention model and BERT) across four datasets, including natural language inference and paraphrase identification tasks. Experiments show the effectiveness of GMASK in providing faithful explanations to these models.",
}
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<abstract>Explaining neural network models is important for increasing their trustworthiness in real-world applications. Most existing methods generate post-hoc explanations for neural network models by identifying individual feature attributions or detecting interactions between adjacent features. However, for models with text pairs as inputs (e.g., paraphrase identification), existing methods are not sufficient to capture feature interactions between two texts and their simple extension of computing all word-pair interactions between two texts is computationally inefficient. In this work, we propose the Group Mask (GMASK) method to implicitly detect word correlations by grouping correlated words from the input text pair together and measure their contribution to the corresponding NLP tasks as a whole. The proposed method is evaluated with two different model architectures (decomposable attention model and BERT) across four datasets, including natural language inference and paraphrase identification tasks. Experiments show the effectiveness of GMASK in providing faithful explanations to these models.</abstract>
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%0 Conference Proceedings
%T Explaining Neural Network Predictions on Sentence Pairs via Learning Word-Group Masks
%A Chen, Hanjie
%A Feng, Song
%A Ganhotra, Jatin
%A Wan, Hui
%A Gunasekara, Chulaka
%A Joshi, Sachindra
%A Ji, Yangfeng
%S Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2021
%8 jun
%I Association for Computational Linguistics
%C Online
%F chen-etal-2021-explaining
%X Explaining neural network models is important for increasing their trustworthiness in real-world applications. Most existing methods generate post-hoc explanations for neural network models by identifying individual feature attributions or detecting interactions between adjacent features. However, for models with text pairs as inputs (e.g., paraphrase identification), existing methods are not sufficient to capture feature interactions between two texts and their simple extension of computing all word-pair interactions between two texts is computationally inefficient. In this work, we propose the Group Mask (GMASK) method to implicitly detect word correlations by grouping correlated words from the input text pair together and measure their contribution to the corresponding NLP tasks as a whole. The proposed method is evaluated with two different model architectures (decomposable attention model and BERT) across four datasets, including natural language inference and paraphrase identification tasks. Experiments show the effectiveness of GMASK in providing faithful explanations to these models.
%R 10.18653/v1/2021.naacl-main.306
%U https://aclanthology.org/2021.naacl-main.306
%U https://doi.org/10.18653/v1/2021.naacl-main.306
%P 3917-3930
Markdown (Informal)
[Explaining Neural Network Predictions on Sentence Pairs via Learning Word-Group Masks](https://aclanthology.org/2021.naacl-main.306) (Chen et al., NAACL 2021)
ACL