Locally Aggregated Feature Attribution on Natural Language Model Understanding
This work addresses the problem of model interpretability for NLP practitioners by providing a more robust feature attribution method, though it is incremental as it adapts existing gradient-based techniques to NLP.
The paper tackles the challenge of applying gradient-based feature attribution methods to NLP models by proposing Locally Aggregated Feature Attribution (LAFA), which smooths gradients using similar reference texts from language model embeddings, and demonstrates superior performance on tasks like Entity Recognition and Sentiment Analysis.
With the growing popularity of deep-learning models, model understanding becomes more important. Much effort has been devoted to demystify deep neural networks for better interpretability. Some feature attribution methods have shown promising results in computer vision, especially the gradient-based methods where effectively smoothing the gradients with reference data is key to a robust and faithful result. However, direct application of these gradient-based methods to NLP tasks is not trivial due to the fact that the input consists of discrete tokens and the "reference" tokens are not explicitly defined. In this work, we propose Locally Aggregated Feature Attribution (LAFA), a novel gradient-based feature attribution method for NLP models. Instead of relying on obscure reference tokens, it smooths gradients by aggregating similar reference texts derived from language model embeddings. For evaluation purpose, we also design experiments on different NLP tasks including Entity Recognition and Sentiment Analysis on public datasets as well as key feature detection on a constructed Amazon catalogue dataset. The superior performance of the proposed method is demonstrated through experiments.