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Slot filling, to extract entities for specific types of information (slot), is a vitally important modular of dialogue systems for automatic diagnosis. Doctor responses can be regarded as the weak supervision of patient queries. In this way, a large amount of weakly labeled data can be obtained from unlabeled diagnosis dialogue, alleviating the problem of costly and time-consuming data annotation. However, weakly labeled data suffers from extremely noisy samples. To alleviate the problem, we propose a simple and effective Co-Weak-Teaching method. The method trains two slot filling models simultaneously. These two models learn from two different weakly labeled data, ensuring learning from two aspects. Then, one model utilizes selected weakly labeled data generated by the other, iteratively. The model, obtained by the Co-Weak-Teaching on weakly labeled data, can be directly tested on testing data or sequentially fine-tuned on a small amount of human-annotated data. Experimental results on these two settings illustrate the effectiveness of the method with an increase of 8.03% and 14.74% in micro and macro f1 scores, respectively. 相似文献
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刘晓剑 《机械工业高教研究》2011,(1):41-45
教学团队建设的效果不尽如人意。单一僵化的教学模式,社会惰化的问题,互动和交流的缺乏以及错误的角色定位严重影响了教学团队的运行效果。因此,探索一种更为合理和有效的教学团队模式是很有必要的。协同教学的团队具备合作交流的紧密性、成员结构的差异及互补性和教学形式的灵活多样性等方面的特征。同时,协同教学团队的构建包含以下三个要素:科学的角色规划、合理的团队目标和有效的考核机制。这些特性和要素能够很好地解决传统教学团队存在的种种问题。在此基础上,本文提出建设协同教学的团队的对策建议:引导建设性冲突、提高教学管理水平以及重视学生的感受与建议。 相似文献
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