  The definition generation task can help language learners by providing
explanations for unfamiliar words. This task has attracted much attention in
recent years. We propose a novel task of Simple Definition Generation (SDG) to
help language learners and low literacy readers. A significant challenge of
this task is the lack of learner's dictionaries in many languages, and
therefore the lack of data for supervised training. We explore this task and
propose a multitasking framework SimpDefiner that only requires a standard
dictionary with complex definitions and a corpus containing arbitrary simple
texts. We disentangle the complexity factors from the text by carefully
designing a parameter sharing scheme between two decoders. By jointly training
these components, the framework can generate both complex and simple
definitions simultaneously. We demonstrate that the framework can generate
relevant, simple definitions for the target words through automatic and manual
evaluations on English and Chinese datasets. Our method outperforms the
baseline model by a 1.77 SARI score on the English dataset, and raises the
proportion of the low level (HSK level 1-3) words in Chinese definitions by
3.87%.
