Interpreting Convolutional Networks Trained on Textual Data

Reza Marzban, Christopher Crick

Abstract

There have been many advances in the artificial intelligence field due to the emergence of deep learning. In almost all sub-fields, artificial neural networks have reached or exceeded human-level performance. However, most of the models are not interpretable. As a result, it is hard to trust their decisions, especially in life and death scenarios. In recent years, there has been a movement toward creating explainable artificial intelligence, but most work to date has concentrated on image processing models, as it is easier for humans to perceive visual patterns. There has been little work in other fields like natural language processing. In this paper, we train a convolutional model on textual data and analyze the global logic of the model by studying its filter values. In the end, we find the most important words in our corpus to our model’s logic and remove the rest (95%). New models trained on just the 5% most important words can achieve the same performance as the original model while reducing training time by more than half. Approaches such as this will help us to understand NLP models, explain their decisions according to their word choices, and improve them by finding blind spots and biases.

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Paper Citation


in Harvard Style

Marzban R. and Crick C. (2021). Interpreting Convolutional Networks Trained on Textual Data.In Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-486-2, pages 196-203. DOI: 10.5220/0010205901960203


in Bibtex Style

@conference{icpram21,
author={Reza Marzban and Christopher Crick},
title={Interpreting Convolutional Networks Trained on Textual Data},
booktitle={Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2021},
pages={196-203},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010205901960203},
isbn={978-989-758-486-2},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Interpreting Convolutional Networks Trained on Textual Data
SN - 978-989-758-486-2
AU - Marzban R.
AU - Crick C.
PY - 2021
SP - 196
EP - 203
DO - 10.5220/0010205901960203