Flexible Instance-Specific Rationalization of NLP Models

TL;DR

This paper introduces a flexible, instance-specific method for selecting feature scoring, length, and type of rationales in NLP models, improving interpretability without requiring ad-hoc choices.

Contribution

It proposes a simple, effective approach that dynamically chooses the best rationale parameters per instance, enhancing explanation faithfulness and comprehensiveness.

Findings

Outperforms fixed rationale methods in faithfulness and sufficiency

Provides more comprehensive explanations across datasets

Eliminates need for ad-hoc parameter choices

Abstract

Recent research on model interpretability in natural language processing extensively uses feature scoring methods for identifying which parts of the input are the most important for a model to make a prediction (i.e. explanation or rationale). However, previous research has shown that there is no clear best scoring method across various text classification tasks while practitioners typically have to make several other ad-hoc choices regarding the length and the type of the rationale (e.g. short or long, contiguous or not). Inspired by this, we propose a simple yet effective and flexible method that allows selecting optimally for each data instance: (1) a feature scoring method; (2) the length; and (3) the type of the rationale. Our method is inspired by input erasure approaches to interpretability which assume that the most faithful rationale for a prediction should be the one with the highest difference between the model's output distribution using the full text and the text after removing the rationale as input respectively. Evaluation on four standard text classification datasets shows that our proposed method provides more faithful, comprehensive and highly sufficient explanations compared to using a fixed feature scoring method, rationale length and type. More importantly, we demonstrate that a practitioner is not required to make any ad-hoc choices in order to extract faithful rationales using our approach.