The Future of Artificially Intelligent Assistants
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Deep Learning for Dialogue Systems
Deep Learning for Dialogue Systems Yun-Nung Chen Asli Celikyilmaz Dilek Hakkani-Tur¨ National Taiwan University Microsoft Research Google Research Taipei, Taiwan Redmond, WA Mountain View, CA [email protected] [email protected] [email protected] Abstract Goal-oriented spoken dialogue systems have been the most prominent component in todays vir- tual personal assistants, which allow users to speak naturally in order to finish tasks more effi- ciently. The advancement of deep learning technologies has recently risen the applications of neural models to dialogue modeling. However, applying deep learning technologies for build- ing robust and scalable dialogue systems is still a challenging task and an open research area as it requires deeper understanding of the classic pipelines as well as detailed knowledge of the prior work and the recent state-of-the-art work. Therefore, this tutorial is designed to focus on an overview of dialogue system development while describing most recent research for building dialogue systems, and summarizing the challenges, in order to allow researchers to study the po- tential improvements of the state-of-the-art dialogue systems. The tutorial material is available at http://deepdialogue.miulab.tw. 1 Tutorial Overview With the rising trend of artificial intelligence, more and more devices have incorporated goal-oriented spoken dialogue systems. Among popular virtual personal assistants, Microsoft’s Cortana, Apple’s Siri, Amazon Alexa, and Google Assistant have incorporated dialogue system modules in various devices, which allow users to speak naturally in order to finish tasks more efficiently. Traditional conversational systems have rather complex and/or modular pipelines. -
Conversational In-Vehicle Dialog Systems the Past, Present, and Future
SIGNAL PROCESSING FOR SMART VEHICLE TECHNOLOGIES Fuliang Weng, Pongtep Angkititrakul, Elizabeth E. Shriberg, Larry Heck, Stanley Peters, and John H.L. Hansen Conversational In-Vehicle Dialog Systems The past, present, and future utomotive technology rapidly advances with increasing connectivity and automa- tion. These advancements aim to assist safe driving and improve user travel experi- ence. Before the realization of a full automation, in-vehicle dialog systems may reduce the driver distraction from many services available through connectivity. A Even when a full automation is realized, in-vehicle dialog systems still play a special role in assisting vehicle occupants to perform vari- ous tasks. On the other hand, in-vehicle use cases need to address very different user conditions, environments, and industry requirements than other uses. This makes the development of effective and efficient in-vehicle dialog systems challenging; it requires multidisciplinary expertise in auto- matic speech recognition, spoken language understanding, dialog management (DM), natural language generation, and applica- tion management, as well as field sys- tem and safety testing. In this article, we review research and development (R&D) activities for in-vehicle dialog systems from both academic and industrial perspectives, examine findings, dis- ©ISTOCKPHOTO.COM/POSTERIORI SKY IMAGE LICENSED BY GRAPHIC STOCK LICENSED BY SKY IMAGE cuss key challenges, and share our visions for voice-enabled interaction and intelligent assistance for smart vehicles over the next decade. Introduction The automotive industry is undergoing a revolu- tion, with progress in technologies ranging from computational power and sensors to Internet connectivi- ty and machine intelligence. Expectations for smart vehicles are also changing, due to an increasing need for frequent engage- ment with work and family, and also due to technological progress of prod- ucts in other areas, such as mobile and wearable devices. -
Artificial Intelligence
6/8/2017 The views, opinions, and Artificial Intelligence information expressed during this webinar are those of the presenter why it is not scary … yet and are not the views or opinions of the Newton Public Library. The Newton Public Library makes no representation or warranty with respect to the webinar or any To log in live from home go to: information or materials presented https://kanren.zoom.us/j/561178181 therein. Users of webinar materials Watch the recorded presentation anytime after the 18th should not rely upon or construe the Presenter: Nathan, IT Supervisor, at the Newton Public Library information or resource materials contained in this webinar as legal or 1. Protect your computer •A computer should always have the most recent updates installed for spam filters, anti-virus and Everything we added electricity to anti-spyware software and in the industrial revolution can a secure firewall. now be made smart and that is a good thing… for now. http://cdn.greenprophet.com/wp-content/uploads/2012/04/frying-pan-kolbotek-neoflam-560x475.jpg Artificial intelligence (AI) is intelligence exhibited by machines. These are just a few examples of how smart machines and In computer science, the field of AI research defines itself as the study of artificial intelligence already impact our everyday lives. "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of success at some goal. • For example, artificially intelligent systems recognize and Colloquially, the term "artificial intelligence" is applied when a machine mimics produce speech, control bionic limbs for wounded warriors, "cognitive" functions that humans associate with other human minds, such as automatically recognize and sort pictures of family and friends, "learning" and "problem solving". -
Using Recurrent Neural Networks for Slot Filling in Spoken Language
530 IEEE/ACM TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 23, NO. 3, MARCH 2015 Using Recurrent Neural Networks for Slot Filling in Spoken Language Understanding Grégoire Mesnil, Yann Dauphin, Kaisheng Yao, Yoshua Bengio, Li Deng, Dilek Hakkani-Tur, Xiaodong He, Larry Heck, Gokhan Tur, Dong Yu, and Geoffrey Zweig Abstract—Semantic slot filling is one of the most challenging The semantic parsing of input utterances in SLU typically problems in spoken language understanding (SLU). In this paper, consists of three tasks: domain detection, intent determination, we propose to use recurrent neural networks (RNNs) for this task, and slot filling. Originating from call routing systems, the and present several novel architectures designed to efficiently domain detection and intent determination tasks are typically model past and future temporal dependencies. Specifically, we implemented and compared several important RNN architec- treated as semantic utterance classification problems [2], [3], tures, including Elman, Jordan, and hybrid variants. To facilitate [4], [30], [62], [63]. Slot filling is typically treated as a sequence reproducibility, we implemented these networks with the pub- classification problem in which contiguous sequences of words licly available Theano neural network toolkit and completed are assigned semantic class labels. [5], [7], [31], [32], [33], experiments on the well-known airline travel information system [34], [40], [55]. (ATIS) benchmark. In addition, we compared the approaches on In this paper, following the success of deep learning methods two custom SLU data sets from the entertainment and movies domains. Our results show that the RNN-based models outper- for semantic utterance classification such as domain detection form the conditional random field (CRF) baseline by 2% in [30] and intent determination [13], [39], [50], we focus on absolute error reduction on the ATIS benchmark.