Interpretability Analysis of Artificial Intelligence - A Case Study in Natural Language Processing
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Interpretability in machine learning and deep learning refers to the extent to which model predictions can be explained in a way that is understandable and straightforward to the audience. In recent years, deep learning has achieved successful applications in natural language processing, significantly improving the performance of various tasks. However, due to its inherent complexity, the comprehensibility and interpretability are not satisfactory, which also hinders the further promotion of deep learning methods. This talk first introduces what interpretability analysis is, what interpretability analysis tasks exist in natural language processing, and the purpose of interpretability analysis. Then, it introduces the current development status of interpretability analysis in the field of natural language processing from three aspects: understanding the functional attributes of model components, explaining the behavior of model predictions, and model diagnosis. Finally, it discusses future research trends.