无监督简单定义生成的多任务框架
Multitasking Framework for Unsupervised Simple Definition Generation
摘要：
定义生成任务可以通过为不熟悉的单词提供解释来帮助语言学习者。近年来，这项任务引起了广泛关注。我们提出了一个新的任务简单定义生成（SDG），以帮助语言学习者和低识字读者。这项任务的一个重大挑战是缺乏许多语言的学习词典，因此缺乏监督训练的数据。我们探索这个任务，并提出了一个多任务框架的定义，只需要一个标准的字典与复杂的定义和语料库包含任意简单的文本。我们解开的复杂性因素，从文本仔细设计两个解码器之间的参数共享计划。通过联合训练这些组件，框架可以同时生成复杂和简单的定义。我们证明，该框架可以生成相关的，简单的目标词的定义，通过自动和手动评估英语和中文数据集。我们的方法在英文数据集上的SARI得分比基线模型高出1.77，并且将汉语定义中的低级（HSK 1 - 3级）词的比例提高了3.87%。
英文摘要：
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%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
