Lexically Constrained Decoding with Edit Operation Prediction for Controllable Text Simplification

Tatsuya Zetsu, Tomoyuki Kajiwara, Yuki Arase
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引用次数: 2

Abstract

Controllable text simplification assists language learners by automatically rewriting complex sentences into simpler forms of a target level. However, existing methods tend to perform conservative edits that keep complex words intact. To address this problem, we employ lexically constrained decoding to encourage rewriting. Specifically, the proposed method predicts edit operations conditioned to a target level and creates positive/negative constraints for words that should/should not appear in an output sentence. The experimental results confirm that our method significantly outperforms previous methods and demonstrates a new state-of-the-art performance.
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基于编辑操作预测的可控文本简化的词法约束解码
可控文本简化通过自动将复杂句子改写成目标水平的更简单形式来帮助语言学习者。然而,现有的方法倾向于执行保守的编辑,以保持复杂的单词完整。为了解决这个问题,我们使用词法约束解码来鼓励重写。具体来说,建议的方法预测目标级别的编辑操作,并为输出句子中应该/不应该出现的单词创建积极/消极约束。实验结果证实,我们的方法明显优于以前的方法,并展示了新的最先进的性能。
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