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BLEU is Not Suitable for the Evaluation of Text Simplification

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arxiv 1810.05995 v1 pith:UCEY7T52 submitted 2018-10-14 cs.CL

classification cs.CL
keywords bleusentencesimplificationsplittingstructuralcorrelationevaluationsuitable
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BLEU is widely considered to be an informative metric for text-to-text generation, including Text Simplification (TS). TS includes both lexical and structural aspects. In this paper we show that BLEU is not suitable for the evaluation of sentence splitting, the major structural simplification operation. We manually compiled a sentence splitting gold standard corpus containing multiple structural paraphrases, and performed a correlation analysis with human judgments. We find low or no correlation between BLEU and the grammaticality and meaning preservation parameters where sentence splitting is involved. Moreover, BLEU often negatively correlates with simplicity, essentially penalizing simpler sentences.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Building Large-Scale English-Romanian Literary Translation Resources with Open Models

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    LoRA-fine-tuning a 12B open model on 15,000 GPT-o3 fable translations yields rubric scores close to GPT-o3 (4.83 vs 4.92) for English-to-Romanian literary translation at roughly one percent of the API cost.

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  3. Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation

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    Human-written plain language summaries led to significantly better reader comprehension than LLM-generated ones, despite similar subjective ratings, and most automated metrics did not predict comprehension.

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