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在在线教育系统中找到类似的练习

Finding Similar Exercises in Online Education Systems
课程网址: http://videolectures.net/kdd2018_liu_finding_education/  
主讲教师: Qui Liu
开课单位: 中国科学技术大学
开课时间: 2018-11-23
课程语种: 英语
中文简介:
在在线教育系统中,找到类似的练习是许多应用程序的基本任务,例如练习检索和学生建模。通过在练习中简单地使用特定的文本内容(例如,相同的知识概念或相似的单词),已经提出了几种方法来完成这项任务。然而,如何系统地利用嵌入在多个异构数据(例如文本和图像)中的丰富语义信息来精确地检索类似的练习,这一问题仍然悬而未决。为此,在本文中,我们开发了一种新的基于多模态注意力的神经网络(MANN)框架,用于通过从异构数据中学习统一的语义表示来发现大规模在线教育系统中的类似练习。在MANN中,给定文本、图像和知识概念的练习,我们首先应用卷积神经网络来提取图像表示,并使用嵌入层来表示概念。然后,我们设计了一个基于注意力的长短记忆网络,以多模态的方式学习每个练习的统一语义表示。在此,提出了两种注意力策略,以分别捕捉文本和图像、文本和知识概念的关联。此外,通过相似注意,还测量了每个练习对中的相似部分。最后,我们制定了一个成对的训练策略来返回类似的练习。对真实世界数据的大量实验结果清楚地验证了MANN的有效性和解释能力。
课程简介: In online education systems, finding similar exercises is a fundamental task of many applications, such as exercise retrieval and student modeling. Several approaches have been proposed for this task by simply using the specific textual content (e.g. the same knowledge concepts or the similar words) in exercises. However, the problem of how to systematically exploit the rich semantic information embedded in multiple heterogenous data (e.g. texts and images) to precisely retrieve similar exercises remains pretty much open. To this end, in this paper, we develop a novel Multimodal Attention-based Neural Network (MANN) framework for finding similar exercises in large-scale online education systems by learning a unified semantic representation from the heterogenous data. In MANN, given exercises with texts, images and knowledge concepts, we first apply a convolutional neural network to extract image representations and use an embedding layer for representing concepts. Then, we design an attention-based long short-term memory network to learn a unified semantic representation of each exercise in a multimodal way. Here, two attention strategies are proposed to capture the associations of texts and images, texts and knowledge concepts, respectively. Moreover, with a Similarity Attention, the similar parts in each exercise pair are also measured. Finally, we develop a pairwise training strategy for returning similar exercises. Extensive experimental results on real-world data clearly validate the effectiveness and the interpretation power of MANN.
关 键 词: 在线教育系统; 嵌入在多个异构数据; 长短记忆网络; MANN的有效性和解释能力
课程来源: 视频讲座网
数据采集: 2023-02-01:cyh
最后编审: 2023-02-01:cyh
阅读次数: 42