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Rationalization for Explainable NLP: A Survey by Gurrapu, Sai; Kulkarni, Ajay; Huang, Lifu; Lourentzou, Ismini; Freeman, Laura; Batarseh, Feras A. is a scholarly article available to read on EtoBox.

What is Rationalization for Explainable NLP: A Survey about?

Recent advances in deep learning have improved the performance of many Natural Language Processing (NLP) tasks such as translation, question-answering, and text classification. However, this improvement comes at the expense of model explainability. Black-box models make it difficult to understand the internals of a system and the process it takes to arrive at an output. Numerical (LIME, Shapley) and visualization (saliency heatmap) explainability techniques are helpful; however, they are insufficient because they require specialized knowledge. These factors led rationalization to emerge as a more accessible explainable technique in NLP. Rationalization justifies a model's output by providing a natural language explanation (rationale). Recent improvements in natural language generation have made rationalization an attractive technique because it is intuitive, human-comprehensible, and accessible to non-technical users. Since rationalization is a relatively new field, it is disorganized. As the first survey, rationalization literature in NLP from 2007-2022 is analyzed. This survey presents available methods, explainable evaluations, code, and datasets used across various NLP tasks th

Author

Gurrapu, Sai; Kulkarni, Ajay; Huang, Lifu; Lourentzou, Ismini; Freeman, Laura; Batarseh, Feras A.

Published

2023

Language

EN