# ERASER: A Benchmark to Evaluate Rationalized NLP Models

Source: [ERASER: A Benchmark to Evaluate Rationalized NLP Models](<https://www.sota2.com/research/paper/68fa67721e209131cea1655b>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:15:13.389Z

Authors: Jay DeYoung, Sarthak Jain, Nazneen Fatema Rajani, Eric Lehman, Caiming Xiong, Richard Socher, Byron C. Wallace

Published: 2019-11-08T18:29:03.000Z

## 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/

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Rationale Extraction on MovieReview](<https://www.sota2.com/research/sota/rationale-extraction-on-moviereview.md>): Rationale Extraction; MovieReview; F1 Score: 32.2.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1911.03429>)

[PDF](<https://arxiv.org/pdf/1911.03429.pdf>)