Housam Khalifa Bashier


2021

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DISK-CSV: Distilling Interpretable Semantic Knowledge with a Class Semantic Vector
Housam Khalifa Bashier | Mi-Young Kim | Randy Goebel
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Neural networks (NN) applied to natural language processing (NLP) are becoming deeper and more complex, making them increasingly difficult to understand and interpret. Even in applications of limited scope on fixed data, the creation of these complex “black-boxes” creates substantial challenges for debugging, understanding, and generalization. But rapid development in this field has now lead to building more straightforward and interpretable models. We propose a new technique (DISK-CSV) to distill knowledge concurrently from any neural network architecture for text classification, captured as a lightweight interpretable/explainable classifier. Across multiple datasets, our approach achieves better performance than the target black-box. In addition, our approach provides better explanations than existing techniques.

2020

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RANCC: Rationalizing Neural Networks via Concept Clustering
Housam Khalifa Bashier | Mi-Young Kim | Randy Goebel
Proceedings of the 28th International Conference on Computational Linguistics

We propose a new self-explainable model for Natural Language Processing (NLP) text classification tasks. Our approach constructs explanations concurrently with the formulation of classification predictions. To do so, we extract a rationale from the text, then use it to predict a concept of interest as the final prediction. We provide three types of explanations: 1) rationale extraction, 2) a measure of feature importance, and 3) clustering of concepts. In addition, we show how our model can be compressed without applying complicated compression techniques. We experimentally demonstrate our explainability approach on a number of well-known text classification datasets.