Small but Mighty: New Benchmarks for Split and Rephrase
This work addresses benchmark reliability issues for researchers in natural language processing, but it is incremental as it focuses on improving evaluation rather than advancing methods.
The authors identified that the existing benchmark for the Split and Rephrase text simplification task contains exploitable syntactic cues, allowing a simple rule-based model to match state-of-the-art performance; they introduced two new crowdsourced datasets with more diverse syntax and higher quality, which are more challenging for models.
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.