Learning to Describe Unknown Phrases with Local and Global Contexts
Learning to Describe Unknown Phrases with Local and Global Contexts
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DOI：
10.18653/v1/n19-1350
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发表时间：
2019-06
期刊：
影响因子：
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通讯作者：
Shonosuke Ishiwatari;Hiroaki Hayashi;Naoki Yoshinaga;Graham Neubig;Shoetsu Sato;Masashi Toyoda;M. Kitsuregawa
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文献类型：
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作者：
Shonosuke Ishiwatari;Hiroaki Hayashi;Naoki Yoshinaga;Graham Neubig;Shoetsu Sato;Masashi Toyoda;M. Kitsuregawa
在阅读文本时，经常会被不熟悉的单词和短语卡住，比如具有新奇含义的多义词、很少使用的习语、网络俚语或新兴实体。如果我们人类不能从直接的本地上下文中找出这些表达式的含义，我们就会查阅字典来查找定义，或者搜索文档或网络来查找其他全局上下文来帮助解释。机器能帮我们做这项工作吗？哪种类型的上下文对机器解决问题更重要？为了回答这些问题，我们承担了一项任务，即基于局部和全局上下文用自然语言描述给定短语。为了解决这个问题，我们提出了一个由两个上下文编码器和一个描述解码器组成的神经描述模型。与现有的非标准英语解释方法[Ni+ 2017]和定义生成方法[Noraset+ 2017；Gadetsky+ 2018]，我们的模型适当地从本地和全球背景中获取重要线索。在三个现有数据集（包括WordNet、Oxford和Urban Dictionaries）和一个新创建的维基百科数据集上的实验结果表明，我们的方法比以前的工作更有效。
When reading a text, it is common to become stuck on unfamiliar words and phrases, such as polysemous words with novel senses, rarely used idioms, internet slang, or emerging entities. If we humans cannot figure out the meaning of those expressions from the immediate local context, we consult dictionaries for definitions or search documents or the web to find other global context to help in interpretation. Can machines help us do this work? Which type of context is more important for machines to solve the problem? To answer these questions, we undertake a task of describing a given phrase in natural language based on its local and global contexts. To solve this task, we propose a neural description model that consists of two context encoders and a description decoder. In contrast to the existing methods for non-standard English explanation [Ni+ 2017] and definition generation [Noraset+ 2017; Gadetsky+ 2018], our model appropriately takes important clues from both local and global contexts. Experimental results on three existing datasets (including WordNet, Oxford and Urban Dictionaries) and a dataset newly created from Wikipedia demonstrate the effectiveness of our method over previous work.