专栏首页专知最新综述文章推荐:自然语言生成、深度学习算法、多媒体大数据分析

最新综述文章推荐:自然语言生成、深度学习算法、多媒体大数据分析

【导读】专知内容组整理了最近人工智能领域相关期刊的5篇最新综述文章,为大家进行介绍,欢迎查看!

1

▌自然语言生成综述:任务,应用,评价



作者:Albert Gatt,Emiel Krahmer

摘要:This paper surveys the current state of the art in Natural Language Generation (nlg), dened as the task of generating text or speech from non-linguistic input. A survey of nlg is timely in view of the changes that the eld has undergone over the past two decades, especially in relation to new (usually data-driven) methods, as well as new applications of nlg technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in nlg and the architectures adopted in which such tasks are organised; (b) highlight a number of recent research topics that have arisen partly as a result of growing synergies between nlg and other areas of articial intelligence; (c) draw attention to the challenges in nlg evaluation, relating them to similar challenges faced in other areas of nlp, with an emphasis on different evaluation methods and the relationships between them.

来源:arXiv

网址:https://arxiv.org/abs/1703.09902

评注:这篇文章详细描述了自然语言生成的定义、任务、架构与方法、评价等内容,全面详实,最新的更新到2018年1月30日,是了解自然语言生成的必读文章。

2

▌面向知识自动化的自动问答研究进展



作者:曾帅, 王帅, 袁勇, 倪晓春, 欧阳永基

摘要:将自动问答系统从基于文本关键词的层面,提升到基于知识的层面,实现个性化、智能化的知识机器人,已成为自动问答系统未来的发展趋势与目标.本文从知识管理的角度出发,分析和总结自动问答领域的最新研究成果.按照知识表示方法,对代表性自动问答系统及关键问题进行了描述和分析;并对主流的英文、中文自动问答应用和主要评测方法进行了介绍.

来源:自动化学报 2017, 43(9): 1491-1508.

网址

http://www.aas.net.cn/CN/10.16383/j.aas.2017.c160667

3

▌从起源到具体算法,深度学习综述



作者:Md Zahangir Alom, Tarek M. Taha, Christopher Yakopcic, Stefan Westberg, Mahmudul Hasan, Brian C Van Esesn, Abdul A S. Awwal, Vijayan K. Asari

摘要:近年来,深度学习作为机器学习的新分支,其应用在多个领域取得巨大成功,并一直在快速发展,不断开创新的应用模式,创造新机会。深度学习方法根据训练数据是否拥有标记信息被划分为监督学习、半监督学习和无监督学习。实验结果显示了上述方法在图像处理、计算机视觉、语音识别、机器翻译、艺术、医学成像、医疗信息处理、机器人控制和生物、自然语言处理(NLP)、网络安全等领域的最新成果。本报告简要概述了深度学习方法的发展,包括深度神经网络(DNN)、卷积神经网络(CNN)、循环神经网络(RNN)(包括长短期记忆(LSTM)和门控循环单元(GRU))、自 编码器(AE)、深度信念网络(DBN),生成对抗网络(GAN)和深度强化学习(DRL)。此外,本文也涵盖了深度学习方法前沿发展和高级变体深度学习技术。此外,深度学习方法在各个应用领域进行的探索和评估也包含在本次调查中。我们还会谈到最新开发的框架、SDK 和用于评估深度学习方法的基准数据集。然而,这些论文并没有讨论某些大型深度学习模型和最新开发的生成模型方法

来源:ArXiv, 3 Mar 2018

网址

https://arxiv.org/abs/1803.01164

4

▌多媒体大数据分析综述



作者:SAMIRA POUYANFAR, YIMIN YANG, and SHU-CHING CHEN

摘要:With the proliferation of online services and mobile technologies, the world has stepped into a multimedia big data era. A vast amount of research work has been done in the multimedia area, targeting different aspects of big data analytics, such as the capture, storage, indexing, mining, and retrieval of multimedia big data. However, very few research work provides a complete survey of the whole pine-line of the multimedia big data analytics, including the management and analysis of the large amount of data, the challenges and opportunities, and the promising research directions. To serve this purpose, we present this survey, which conducts a comprehensive overview of the state-of-the-art research work on multimedia big data analytics. It also aims to bridge the gap between multimedia challenges and big data solutions by providing the current big data frameworks, their applications in multimedia analyses, the strengths and limitations of the existing methods, and the potential future directions in multimedia big data analytics. To the best of our knowledge, this is the first survey that targets the most recent multimedia management techniques for very large-scale data and also provides the research studies and technologies advancing the multimedia analyses in this big data era.

来源:ACM Comput. Surv. 51, 1, Article 10 (January 2018),

网址

https://dl.acm.org/citation.cfm?id=3150226

5

▌事件处理进展综述



作者:MIYURU DAYARATHNA and SRINATH PERERA

摘要:Event processing (EP) is a data processing technology that conducts online processing of event information. In this survey, we summarize the latest cutting-edge work done on EP from both industrial and academic research community viewpoints. We divide the entire field of EP into three subareas: EP system architectures, EP use cases, and EP open research topics. Then we deep dive into the details of each subsection. We investigate the system architecture characteristics of novel EP platforms, such as Apache Storm, Apache Spark, and Apache Flink. We found significant advancements made on novel application areas, such as the Internet of Things; streaming machine learning (ML); and processing of complex data types such as text, video data streams, and graphs. Furthermore, there has been significant body of contributions made on event ordering, system scalability, development of EP languages and exploration of use of heterogeneous devices for EP, which we investigate in the latter half of this article. Through our study, we found key areas that require significant attention from the EP community, such as Streaming ML, EP system benchmarking, and graph stream processing.

来源:ACM Comput. Surv. 51, 2, Article 33 (February 2018)

网址:https://dl.acm.org/citation.cfm?id=3170432

本文分享自微信公众号 - 专知(Quan_Zhuanzhi),作者:Quan

原文出处及转载信息见文内详细说明,如有侵权,请联系 yunjia_community@tencent.com 删除。

原始发表时间:2018-03-18

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