ERASER: A Benchmark to Evaluate Rationalized NLP Models

TL;DR

ERASER is a comprehensive benchmark dataset collection designed to evaluate the quality and faithfulness of rationales in interpretable NLP models, aiming to standardize progress in explainable AI.

Contribution

The paper introduces ERASER, a unified benchmark with datasets, metrics, and annotations for assessing rationales in NLP models, facilitating consistent evaluation of interpretability methods.

Findings

Multiple datasets with human-annotated rationales

Metrics for rationale alignment and faithfulness

Benchmark implementation available online

Abstract

State-of-the-art models in NLP are now predominantly based on deep neural networks that are opaque in terms of how they come to make predictions. This limitation has increased interest in designing more interpretable deep models for NLP that reveal the `reasoning' behind model outputs. But work in this direction has been conducted on different datasets and tasks with correspondingly unique aims and metrics; this makes it difficult to track progress. We propose the Evaluating Rationales And Simple English Reasoning (ERASER) benchmark to advance research on interpretable models in NLP. This benchmark comprises multiple datasets and tasks for which human annotations of "rationales" (supporting evidence) have been collected. We propose several metrics that aim to capture how well the rationales provided by models align with human rationales, and also how faithful these rationales are (i.e., the degree to which provided rationales influenced the corresponding predictions). Our hope is that releasing this benchmark facilitates progress on designing more interpretable NLP systems. The benchmark, code, and documentation are available at https://www.eraserbenchmark.com/