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首页> 外文期刊>ACM transactions on Asian and low-resource language information processing >Outline Extraction with Question-Specific Memory Cells
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Outline Extraction with Question-Specific Memory Cells

机译：用问题特定的存储器单元提取提取

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摘要

Outline extraction has been widely applied in online consultation to help experts quickly understand individual cases. Given a specific case described as unstructured plain text, outline extraction aims to make a summary for this case by answering a set of questions, which in fact is a new type of machine reading comprehension task. Inspired by a recently popular memory network, we propose a novel question-specific memory cell network (QSMCN) to extract information related to multiple questions on-the-fly as it reads texts. QSMCN constructs a specific memory cell for each question, which is sequentially expanded in recurrent neural network style. Each cell contains three specific vectors to first identify whether current input is related to corresponding question and then update question-specific case representation. We add a penalization term in the loss function to make extracted knowledge more reasonable and interpretable. To support this study, we construct a new outline extraction corpus, InjuryCase,1 which is composed of 3,995 real Chinese occupational injury cases. Experimental results show that our method makes a significant improvement. We further apply the proposed framework on two multi-aspect extraction tasks and find that the proposed model also remarkably outperforms existing state-of-the-art methods of the aspect extraction task.
机译：大纲提取已广泛应用于在线咨询中，以帮助专家快速了解个别案例。考虑到一个特定的案例被描述为非结构化纯文本，轮廓提取旨在通过回答一组问题来为这种情况进行摘要，其实际上是一种新型的机器阅读理解任务。灵感来自最近流行的内存网络，我们提出了一种新颖的问题特定的存储器单元网络（QSMCN），以在其读取文本时从飞行中提取与多个问题相关的信息。 QSMCN为每个问题构造特定的存储器单元，其在经常性神经网络样式中顺序扩展。每个单元格包含三个特定向量，首先识别当前输入是否与相应的问题有关，然后更新特定于质疑的情况。我们在损失职能中增加了惩罚项，以提取提取的知识更合理和解释。为了支持这项研究，我们构建了一个新的大纲提取语料库，伤伴，1，由3,995名真正的中国职业伤害案件组成。实验结果表明，我们的方法取得了重大改善。我们进一步应用于两个多方面提取任务的提议框架，并发现所提出的模型也非常优于现有的Aspect提取任务的现有最先进方法。

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