Exploiting Correlations Between Contexts and Definitions with Multiple
  Definition Modeling
Linhan Zhang Qian Chen Wen Wang Yuxin Jiang Bing Li Wei Wang Xin Cao
Definition modeling is an important task in advanced natural language
applications such as understanding and conversation. Since its introduction, it
focus on generating one definition for a target word or phrase in a given
context, which we refer to as Single Definition Modeling (SDM). However, this
approach does not adequately model the correlations and patterns among
different contexts and definitions of words. In addition, the creation of a
training dataset for SDM requires significant human expertise and effort. In
this paper, we carefully design a new task called Multiple Definition Modeling
(MDM) that pool together all contexts and definition of target words. We
demonstrate the ease of creating a model as well as multiple training sets
automatically. % In the experiments, we demonstrate and analyze the benefits of
MDM, including improving SDM's performance by using MDM as the pretraining task
and its comparable performance in the zero-shot setting.