Assisting Language Learners: Automated Trans-Lingual Definition Generation via Contrastive Prompt Learning (2023)

Abstract: 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.

## introduction a significant area of research within intelligent computer-assisted language learning (icall) is devoted to supporting language learners in understanding words @xcite . this research is primarily motivated by two main issues: (1) language learners often struggle to accurately identify the meaning of words with multiple definitions, as the cognitive process of differentiating each meaning can be challenging @xcite ; (2) on another note, lexicographers are responsible for manually updating predefined word-definition inventories for language learners, a process that may be time-consuming and not always able to keep up with the constantly evolving nature of language usage. to address these issues, researchers aim to benefit both language learners and lexicographers by automatically generating the definition for a given word based on its corresponding local context @xcite @xcite . previous works on definition generation mainly focus on mono-lingual generation scenarios, primarily due to the availability of parallel training and evaluation data @xcite @xcite . however, these works rarely notice a real-occurring problem that the generated definitions may also consist of unfamiliar words for language learners @xcite . in other words, it is more applicable to generate definitions in the native language of foreign learners. as depicted in figure 1 , if a chinese native speaker wants to know an english word's meaning, the definition in chinese is easier to capture. to this end, we propose a novel task called trans-lingual definition generation (tldg). the tldg task is challenging because there are no trans-lingual parallel datasets, e.g., the word and context are in chinese, and the definition is to mitigate the problems, we develop two novel learning methods. for the ignore-task error, we get inspired from task-oriented prompt learning @xcite , and design prompt combination method to force the models focus on generating trans-lingual definition rather than mere translation. in addition, we propose contrastive prompt learning method based on an contrastive loss @xcite , which separates language information from the task prompt and in turn acquires a better task prompt representation for definition generation. due to the scarcity of definition generation data in numerous languages, we carry out extensive experiments in both rich-and low-resource situations. we demonstrate that the contrastive prompt learning method is effective in addressing the two errors and capable of yielding higher-quality definitions when compared to the baselines in both scenarios. in general, our contributions are as follows: ## method one straightforward approach to generating translingual definitions is to develop a pipeline that initially produces mono-lingual definitions and then translates them into the desired language. this intuitive approach serves as one naive baseline, which we elaborate in the experiment section (section 4.2). besides, in this section, we introduce 3 methods to better fit our task: (1) a simple implementation of fine-tuning on multi-lingual translation model; (2) prompt combination method; and (3) contrastive prompt learning method. ## experiments in this section, we conduct extensive experiments and analyze the proposed methods carefully. ## conclusions in this work, we propose a novel and challenging task tldg that generates the trans-lingual definition in an unsupervised manner. to tackle the task, we leverage multi-lingual translation models and propose an effective method of contrastive prompt learning for the task. through extensive experiments, we validate the method is capable of addressing typical errors and promising in both rich-and low-resource scenarios. in the future, we will develop more strategies to improve the quality of trans-lingual definitions.