论文标题
LSBERT：简单的简单框架
LSBert: A Simple Framework for Lexical Simplification
论文作者
论文摘要
词汇简化（LS）旨在用给定句子中的复杂单词替换为简单的等效含义的替代方案，以简化句子。最近，无监督的词汇简化方法仅依赖于复杂的单词本身，而不论给定的句子如何产生候选替代，这将不可避免地产生大量的虚假候选人。 In this paper, we propose a lexical simplification framework LSBert based on pretrained representation model Bert, that is capable of (1) making use of the wider context when both detecting the words in need of simplification and generating substitue candidates, and (2) taking five high-quality features into account for ranking candidates, including Bert prediction order, Bert-based language model, and the paraphrase database PPDB, in addition to the word frequency和其他LS方法中常用的单词相似性。我们表明，我们的系统输出语法上正确且在语义上适当的词汇简化，并且与这些基准相比，在三个众所周知的基准上的精度优于29.8精确点。
Lexical simplification (LS) aims to replace complex words in a given sentence with their simpler alternatives of equivalent meaning, to simplify the sentence. Recently unsupervised lexical simplification approaches only rely on the complex word itself regardless of the given sentence to generate candidate substitutions, which will inevitably produce a large number of spurious candidates. In this paper, we propose a lexical simplification framework LSBert based on pretrained representation model Bert, that is capable of (1) making use of the wider context when both detecting the words in need of simplification and generating substitue candidates, and (2) taking five high-quality features into account for ranking candidates, including Bert prediction order, Bert-based language model, and the paraphrase database PPDB, in addition to the word frequency and word similarity commonly used in other LS methods. We show that our system outputs lexical simplifications that are grammatically correct and semantically appropriate, and obtains obvious improvement compared with these baselines, outperforming the state-of-the-art by 29.8 Accuracy points on three well-known benchmarks.
