Revisiting non-English Text Simplification: A Unified Multilingual
  Benchmark
Michael J. Ryan Tarek Naous Wei Xu
Recent advancements in high-quality, large-scale English resources have
pushed the frontier of English Automatic Text Simplification (ATS) research.
However, less work has been done on multilingual text simplification due to the
lack of a diverse evaluation benchmark that covers complex-simple sentence
pairs in many languages. This paper introduces the MultiSim benchmark, a
collection of 27 resources in 12 distinct languages containing over 1.7 million
complex-simple sentence pairs. This benchmark will encourage research in
developing more effective multilingual text simplification models and
evaluation metrics. Our experiments using MultiSim with pre-trained
multilingual language models reveal exciting performance improvements from
multilingual training in non-English settings. We observe strong performance
from Russian in zero-shot cross-lingual transfer to low-resource languages. We
further show that few-shot prompting with BLOOM-176b achieves comparable
quality to reference simplifications outperforming fine-tuned models in most
languages. We validate these findings through human evaluation.