AILA: Attentive Interactive Labeling Assistant for Document Classification through Attention-Based Deep Neural Networks
AILA: Attentive Interactive Labeling Assistant for Document Classification through Attention-Based Deep Neural Networks
复制标题
AILA：通过基于注意力的深度神经网络进行文档分类的注意力交互式标签助手
DOI：
10.1145/3290605.3300460
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Sungsoo Ray Hong
中科院分区：
文献类型：
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作者：
Min;Cheonbok Park;Soyoung Yang;Yonggyu Kim;J. Choo;Sungsoo Ray Hong
文档标记是构建各种机器学习应用程序的关键步骤。然而，这一步骤可能是耗时和艰巨的，需要大量的人力。为了支持一个高效的文档标注环境，我们提出了一个系统称为注意交互式标注助手（AILA）。在其核心，AILA使用交互式注意力模块（IAM），这是一个新颖的模块，可以在视觉上突出显示文档中的单词，标签人员在标记文档时可能会注意这些单词。IAM利用基于注意力的深度神经网络，不仅支持预测哪些单词需要突出显示，还使标注者能够在标注时指示应该被分配高注意力权重的单词，以提高未来单词预测的质量。我们在研究中通过比较有和没有IAM的条件来评估标注效率和准确性。结果表明，在有IAM的条件下，参与者的标记效率比没有IAM的条件下显着增加，而两种条件下保持大致相同的标记准确性。
Document labeling is a critical step in building various machine learning applications. However, the step can be time-consuming and arduous, requiring a significant amount of human efforts. To support an efficient document labeling environment, we present a system called Attentive Interactive Labeling Assistant (AILA). In its core, AILA uses Interactive Attention Module (IAM), a novel module that visually highlights words in a document that labelers may pay attention to when labeling a document. IAM utilizes attention-based Deep Neural Networks which not only support a prediction of which words to highlight but also enable labelers to indicate words that should be assigned a high attention weight while labeling to improve the future quality of word prediction.We evaluated the labeling efficiency and the accuracy by comparing the conditions with and without IAM in our study. The results showed that participants' labeling efficiency increased significantly under the condition with IAM than the condition without IAM, while the two conditions maintained roughly the same labeling accuracy.