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- W4378499114 abstract "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." @default.
- W4378499114 created "2023-05-27" @default.
- W4378499114 creator A5037533418 @default.
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- W4378499114 date "2023-05-24" @default.
- W4378499114 modified "2023-10-16" @default.
- W4378499114 title "Revisiting non-English Text Simplification: A Unified Multilingual Benchmark" @default.
- W4378499114 doi "https://doi.org/10.48550/arxiv.2305.15678" @default.
- W4378499114 hasPublicationYear "2023" @default.
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