  The standard definition generation task requires to automatically produce
mono-lingual definitions (e.g., English definitions for English words), but
ignores that the generated definitions may also consist of unfamiliar words for
language learners. In this work, we propose a novel task of Trans-Lingual
Definition Generation (TLDG), which aims to generate definitions in another
language, i.e., the native speaker's language. Initially, we explore the
unsupervised manner of this task and build up a simple implementation of
fine-tuning the multi-lingual machine translation model. Then, we develop two
novel methods, Prompt Combination and Contrastive Prompt Learning, for further
enhancing the quality of the generation. Our methods are evaluated against the
baseline Pipeline method in both rich- and low-resource settings, and we
empirically establish its superiority in generating higher-quality
trans-lingual definitions.
