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Sequence-to-Sequence Models for Automated Text Simplification

机译：自动文本简化的序列到序列模型

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摘要

A key writing skill is the capability to clearly convey desired meaning using available linguistic knowledge. Consequently, writers must select from a large array of idioms, vocabulary terms that are semantically equivalent, and discourse features that simultaneously reflect content and allow readers to grasp meaning. In many cases, a simplified version of a text is needed to ensure comprehension on the part of a targeted audience (e.g., second language learners). To address this need, we propose an automated method to simplify texts based on paraphrasing. Specifically, we explore the potential for a deep learning model, previously used for machine translation, to learn a simplified version of the English language within the context of short phrases. The best model, based on an Universal Transformer architecture, achieved a BLEU score of 66.01. We also evaluated this model's capability to perform similar transformation to texts that were simplified by human experts at different levels.
机译：一项关键的写作技能是能够使用可用的语言知识清楚地传达所需含义的能力。因此，作者必须从大量的成语，语义上等价的词汇以及能同时反映内容并允许读者掌握含义的语篇特征中进行选择。在许多情况下，需要文本的简化版本以确保目标受众（例如第二语言学习者）的理解。为了满足这一需求，我们提出了一种自动方法，可以根据释义来简化文本。具体来说，我们探索了以前用于机器翻译的深度学习模型在短短语语境中学习英语简化版本的潜力。基于通用变压器体系结构的最佳模型的BLEU得分为66.01。我们还评估了该模型对文本进行类似转换的能力，这些文本已被不同级别的人类专家简化。

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