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.
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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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