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The standard definition generation task requires to automatically producemono-lingual definitions (e.g., English definitions for English words), butignores that the generated definitions may also consist of unfamiliar words forlanguage learners. In this work, we propose a novel task of Trans-LingualDefinition Generation (TLDG), which aims to generate definitions in anotherlanguage, i.e., the native speaker's language. Initially, we explore theunsupervised manner of this task and build up a simple implementation offine-tuning the multi-lingual machine translation model. Then, we develop twonovel methods, Prompt Combination and Contrastive Prompt Learning, for furtherenhancing the quality of the generation. Our methods are evaluated against thebaseline Pipeline method in both rich- and low-resource settings, and weempirically establish its superiority in generating higher-qualitytrans-lingual definitions.

Assisting Language Learners: Automated Trans-Lingual Definition Generation via Contrastive Prompt Learning