Abstract
In this work, we proposed a new interpretability framework for convolutional neural networks trained for text classification. The objective is to discover the interpretability of the convolutional layers that composes the architecture. The methodology introduced explores the most relevant words for the classification and more generally look for the most relevant concepts learned in the internal representation of the CNN. Here, the concepts studied were the POS tags.
Furthermore, we have proposed an iterative algorithm to determine the most relevant filters or neurons for the task. The outcome of this algorithm is a threshold used to mask the least active neurons and focus the interpretability study only on the most relevant parts of the network.
The introduced framework has been validated for explaining the internal representation of a well-known sentiment analysis task. As a result of this study, we found evidence that certain POS tags, such as nouns and adjectives, are more relevant for the classification. Moreover, we found evidence of the redundancy among the filters from a convolutional layer.
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Notes
- 1.
The annotation classes considered are discussed in Sect. 4.
- 2.
References
Abadi, M., et al.: TensorFlow: large-scale machine learning on heterogeneous systems (2015). https://www.tensorflow.org/. Software available from tensorflow.org
Arrieta, A.B., et al.: Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 58, 82–115 (2020)
Bau, D., Zhou, B., Khosla, A., Oliva, A., Torralba, A.: Network dissection: quantifying interpretability of deep visual representations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6541–6549 (2017)
Bird, S., Klein, E., Loper, E.: Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit. O’Reilly Media, Inc. (2009)
Chollet, F., et al.: Keras (2015). https://github.com/fchollet/keras
Deng, L., Liu, Y.: Deep Learning in Natural Language Processing. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-5209-5
Gilpin, L.H., Bau, D., Yuan, B.Z., Bajwa, A., Specter, M., Kagal, L.: Explaining explanations: an overview of interpretability of machine learning. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 80–89. IEEE (2018)
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press (2016). http://www.deeplearningbook.org
Jacovi, A., Shalom, O.S., Goldberg, Y.: Understanding convolutional neural networks for text classification. In: Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, pp. 56–65 (2018)
Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1746–1751. Association for Computational Linguistics, Doha (2014). https://doi.org/10.3115/v1/D14-1181, https://aclanthology.org/D14-1181
Linzen, T.T., Chrupała, G., Alishahi, A.: Proceedings of the 2018 EMNLP workshop BlackboxNLP: analyzing and interpreting neural networks for NLP. In: Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP (2018)
Maas, A., Daly, R.E., Pham, P.T., Huang, D., Ng, A.Y., Potts, C.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 142–150 (2011)
Mikolov, T., Chen, K., Corrado, G.S., Dean, J.: Efficient estimation of word representations in vector space. In: ICLR (2013)
Yin, W., Kann, K., Yu, M., Schütze, H.: Comparative study of CNN and RNN for natural language processing. CoRR abs/1702.01923 (2017). http://dblp.uni-trier.de/db/journals/corr/corr1702.html#0001KYS17
Acknowledgments
We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research. This work is partially supported by the TAILOR project, a project funded by the EU Horizon 2020 research and innovation programme under GA No 952215.
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Giménez, M., Fabregat-Hernández, A., Fabra-Boluda, R., Palanca, J., Botti, V. (2022). A Fine-Grained Study of Interpretability of Convolutional Neural Networks for Text Classification. In: García Bringas, P., et al. Hybrid Artificial Intelligent Systems. HAIS 2022. Lecture Notes in Computer Science(), vol 13469. Springer, Cham. https://doi.org/10.1007/978-3-031-15471-3_23
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