Small but Mighty: New Benchmarks for Split and Rephrase

TL;DR

This paper critiques existing benchmarks for the Split and Rephrase task, revealing their exploitable cues, and introduces new, more diverse datasets with manual evaluation to better assess model performance.

Contribution

It identifies limitations in current benchmarks and provides two new crowdsourced datasets with improved diversity and quality for more accurate evaluation.

Findings

Existing benchmarks contain exploitable syntactic cues.

Simple rule-based models can match state-of-the-art performance on flawed benchmarks.

New datasets are more diverse and challenging, with manual evaluation confirming their quality.

Abstract

Split and Rephrase is a text simplification task of rewriting a complex sentence into simpler ones. As a relatively new task, it is paramount to ensure the soundness of its evaluation benchmark and metric. We find that the widely used benchmark dataset universally contains easily exploitable syntactic cues caused by its automatic generation process. Taking advantage of such cues, we show that even a simple rule-based model can perform on par with the state-of-the-art model. To remedy such limitations, we collect and release two crowdsourced benchmark datasets. We not only make sure that they contain significantly more diverse syntax, but also carefully control for their quality according to a well-defined set of criteria. While no satisfactory automatic metric exists, we apply fine-grained manual evaluation based on these criteria using crowdsourcing, showing that our datasets better represent the task and are significantly more challenging for the models.