Interpreting Deep Learning Models in Natural Language Processing: A Review

Understand

Neural network models have achieved state-of-the-art performances in a wide range of natural language processing (NLP) tasks.

• However, a long-standing criticism against neural network models is the lack of interpretability, which not only reduces the reliability of neural NLP systems but also limits the scope of their applications in areas where interpretability is essential (e.g., health care applications).
• In response, the increasing interest in interpreting neural NLP models has spurred a diverse array of interpretation methods over recent years.
• In this survey, we provide a comprehensive review of various interpretation methods for neural models in NLP.