Can machines know what twin prime is? From the composition of this phrase,
machines may guess twin prime is a certain kind of prime, but it is still
difficult to deduce exactly what twin stands for without additional knowledge.
Here, twin prime is a jargon - a specialized term used by experts in a
particular field. Explaining jargon is challenging since it usually requires
domain knowledge to understand. Recently, there is an increasing interest in
extracting and generating definitions of words automatically. However, existing
approaches, either extraction or generation, perform poorly on jargon. In this
paper, we propose to combine extraction and generation for jargon definition
modeling: first extract self- and correlative definitional information of
target jargon from the Web and then generate the final definitions by
incorporating the extracted definitional information. Our framework is
remarkably simple but effective: experiments demonstrate our method can
generate high-quality definitions for jargon and outperform state-of-the-art
models significantly, e.g., BLEU score from 8.76 to 22.66 and human-annotated
score from 2.34 to 4.04.

本文提出了一种将提取和生成相结合的术语定义建模框架，通过从网络中提取自我和相关的定义信息，然后结合这些信息生成最终定义。实验表明，我们的方法可以为术语生成高质量的定义，BLEU 得分从 8.76 到 22.66，人工标注得分从 2.34 到 4.04，且优于现有的最先进的模型。