On the Explainability of Natural Language Processing Deep Models

TL;DR

This paper surveys explainability methods for NLP deep models, categorizing approaches by what they explain and discussing evaluation practices, aiming to advance understanding and development of interpretability in NLP.

Contribution

It provides a comprehensive framework for classifying and evaluating explainability methods in NLP, including a case-study and future research directions.

Findings

Categorizes explainability methods into input, processing, and output levels.

Highlights the lack of standardized evaluation practices in NLP explainability.

Provides a case-study on neural machine translation models.

Abstract

While there has been a recent explosion of work on ExplainableAI ExAI on deep models that operate on imagery and tabular data, textual datasets present new challenges to the ExAI community. Such challenges can be attributed to the lack of input structure in textual data, the use of word embeddings that add to the opacity of the models and the difficulty of the visualization of the inner workings of deep models when they are trained on textual data. Lately, methods have been developed to address the aforementioned challenges and present satisfactory explanations on Natural Language Processing (NLP) models. However, such methods are yet to be studied in a comprehensive framework where common challenges are properly stated and rigorous evaluation practices and metrics are proposed. Motivated to democratize ExAI methods in the NLP field, we present in this work a survey that studies model-agnostic as well as model-specific explainability methods on NLP models. Such methods can either develop inherently interpretable NLP models or operate on pre-trained models in a post-hoc manner. We make this distinction and we further decompose the methods into three categories according to what they explain: (1) word embeddings (input-level), (2) inner workings of NLP models (processing-level) and (3) models' decisions (output-level). We also detail the different evaluation approaches interpretability methods in the NLP field. Finally, we present a case-study on the well-known neural machine translation in an appendix and we propose promising future research directions for ExAI in the NLP field.