DAMDID 2023. S11T2. Alisher Rogov. Interpretability Methods Performance in Text Categorization Task
SESSION 11. Information extraction from text I
Alisher Rogov and Natalia Loukachevitch
Evaluating the Performance of Interpretability Methods in Text Categorization Task
Neural networks are progressively assuming a larger role in individuals daily routines, as their complexity continues to grow. While the model demonstrates satisfactory performance when evaluated on the test data, it often yields unforeseen outcomes in real-world scenarios. To diagnose the source of these errors, understanding the decision-making process employed by the model becomes crucial. In this paper, we consider various methods of interpreting the BERT model in classification tasks, and also consider methods for evaluating interpretation methods using vector representations fastText, GloVe and Sentence-BERT.