24Chatbots & Dialogue Systems
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Adapting to Trends in Language Resource Development: a Progress
Adapting to Trends in Language Resource Development: A Progress Report on LDC Activities Christopher Cieri, Mark Liberman University of Pennsylvania, Linguistic Data Consortium 3600 Market Street, Suite 810, Philadelphia PA. 19104, USA E-mail: {ccieri,myl} AT ldc.upenn.edu Abstract This paper describes changing needs among the communities that exploit language resources and recent LDC activities and publications that support those needs by providing greater volumes of data and associated resources in a growing inventory of languages with ever more sophisticated annotation. Specifically, it covers the evolving role of data centers with specific emphasis on the LDC, the publications released by the LDC in the two years since our last report and the sponsored research programs that provide LRs initially to participants in those programs but eventually to the larger HLT research communities and beyond. creators of tools, specifications and best practices as well 1. Introduction as databases. The language resource landscape over the past two years At least in the case of LDC, the role of the data may be characterized by what has changed and what has center has grown organically, generally in response to remained constant. Constant are the growing demands stated need but occasionally in anticipation of it. LDC’s for an ever-increasing body of language resources in a role was initially that of a specialized publisher of LRs growing number of human languages with increasingly with the additional responsibility to archive and protect sophisticated annotation to satisfy an ever-expanding list published resources to guarantee their availability of the of user communities. Changing are the relative long term. -
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. -
The Listener As Addressee in Face-To-Face Dialogue
This article was downloaded by: [Janet Bavelas] On: 21 September 2011, At: 06:49 Publisher: Routledge Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK International Journal of Listening Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/hijl20 The Listener as Addressee in Face-to-Face Dialogue Janet Beavin Bavelas a & Jennifer Gerwing a a Department of Psychology, University of Victoria Available online: 20 Sep 2011 To cite this article: Janet Beavin Bavelas & Jennifer Gerwing (2011): The Listener as Addressee in Face-to-Face Dialogue, International Journal of Listening, 25:3, 178-198 To link to this article: http://dx.doi.org/10.1080/10904018.2010.508675 PLEASE SCROLL DOWN FOR ARTICLE Full terms and conditions of use: http://www.tandfonline.com/page/terms- and-conditions This article may be used for research, teaching and private study purposes. Any substantial or systematic reproduction, re-distribution, re-selling, loan, sub-licensing, systematic supply or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material. -
The Chatbot Revolution
The chatbot revolution Moving beyond the hype and maximizing customer experience Ready for a digital 2% 2017 conversation? 25% 2020 Consumers have higher expectations than ever when it comes to interacting with brands. By 2020, 25% of customer They demand personalized service, easily accessible support options, a quick response after reaching out, and successful resolutions on a tight turnaround. service operations will use To meet these needs, companies are increasing their use of digital channels to chatbot or virtual assistant communicate with customers – in fact, by 2022, 70% of all customer interactions will involve technology like messaging applications, social platforms, or chatbots. technologies, an increase Let’s take a closer look at chatbots. Their functions range from answering simple from just 2% in 2017. questions like informing customers of store hours or location to more advanced ones, like handling a credit card charge dispute. According to Gartner, by 2020, 25% of customer service operations will use chatbot or virtual assistant technologies, an increase from just 2% in 2017. When trying to balance staffing budgets, round- the-clock service availability and a preference for digital platforms, chatbots on paper seem like the obvious – and inevitable – choice to engage customers through automation. But how inevitable is it really? 1. Gartner Magic Quadrant for Customer Engagement Center, Michael Maoz, Brian Manusama, 16 May 2018 www.pega.com The chatbot revolution 01 Why the digital hold up? Consumers and businesses have concerns. Despite Gartner predictions and the obvious excitement around chatbots, overall adoption has been slow. Currently most chatbots are programmed to follow predetermined conversational flows—thus limiting their usefulness for solving complex problems or picking up conversational subtleties. -
Voice Interfaces
VIEW POINT VOICE INTERFACES Abstract A voice-user interface (VUI) makes human interaction with computers possible through a voice/speech platform in order to initiate an automated service or process. This Point of View explores the reasons behind the rise of voice interface, key challenges enterprises face in voice interface adoption and the solution to these. Are We Ready for Voice Interfaces? Let’s get talking! IO showed the new promise of voice offering integrations with their Voice interfaces. Assistants. Since Apple integration with Siri, voice interfaces has significantly Almost all the big players (Google, Apple, As per industry forecasts, over the next progressed. Echo and Google Home Microsoft) have ‘office productivity’ decade, 8 out of every 10 people in the have demonstrated that we do not need applications that are being adopted by world will own a device (a smartphone or a user interface to talk to computers businesses (Microsoft and their Office some kind of assistant) which will support and have opened-up a new channel for Suite already have a big advantage here, voice based conversations in native communication. Recent demos of voice but things like Google Docs and Keynote language. Just imagine the impact! based personal assistance at Google are sneaking in), they have also started Voice Assistant Market USD~7.8 Billion CAGR ~39% Market Size (USD Billion) 2016 2017 2018 2019 2020 2021 2022 2023 The Sudden Interest in Voice Interfaces Although voice technology/assistants Voice Recognition Accuracy Convenience – speaking vs typing have been around in some shape or form Voice Recognition accuracy continues to Humans can speak 150 words per minute for many years, the relentless growth of improve as we now have the capability to vs the typing speed of 40 words per low-cost computational power—and train the models using neural networks minute. -
A Survey on Dialogue Systems: Recent Advances and New Frontiers
A Survey on Dialogue Systems: Recent Advances and New Frontiers Hongshen Cheny, Xiaorui Liuz, Dawei Yiny, and Jiliang Tangz yData Science Lab, JD.com zData Science and Engineering Lab, Michigan State University [email protected], [email protected],fxiaorui,[email protected] ABSTRACT May I know your name ? Dialogue systems have attracted more and more attention. Dialogue State NLU Recent advances on dialogue systems are overwhelmingly Tracking contributed by deep learning techniques, which have been employed to enhance a wide range of big data applications such as computer vision, natural language processing, and recommender systems. For dialogue systems, deep learning I am Robot. can leverage a massive amount of data to learn meaningful NLG Policy learning feature representations and response generation strategies, while requiring a minimum amount of hand-crafting. In this article, we give an overview to these recent advances on di- Figure 1: Traditional Pipeline for Task-oriented Systems. alogue systems from various perspectives and discuss some possible research directions. In particular, we generally di- vide existing dialogue systems into task-oriented and non- certain tasks (e.g. finding products, and booking accommo- task-oriented models, then detail how deep learning tech- dations and restaurants). The widely applied approaches to niques help them with representative algorithms and finally task-oriented systems are to treat the dialogue response as a discuss some appealing research directions that can bring pipeline as shown in Figure1. The systems first understand the dialogue system research into a new frontier. the message given by human, represent it as a internal state, then take some actions according to the policy with respect 1. -
Towards the Implementation of an Intelligent Software Agent for the Elderly Amir Hossein Faghih Dinevari
Towards the Implementation of an Intelligent Software Agent for the Elderly by Amir Hossein Faghih Dinevari A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science Department of Computing Science University of Alberta c Amir Hossein Faghih Dinevari, 2017 Abstract With the growing population of the elderly and the decline of population growth rate, developed countries are facing problems in taking care of their elderly. One of the issues that is becoming more severe is the issue of compan- ionship for the aged people, particularly those who chose to live independently. In order to assist the elderly, we suggest the idea of a software conversational intelligent agent as a companion and assistant. In this work, we look into the different components that are necessary for creating a personal conversational agent. We have a preliminary implementa- tion of each component. Among them, we have a personalized knowledge base which is populated by the extracted information from the conversations be- tween the user and the agent. We believe that having a personalized knowledge base helps the agent in having better, more fluent and contextual conversa- tions. We created a prototype system and conducted a preliminary evaluation to assess by users conversations of an agent with and without a personalized knowledge base. ii Table of Contents 1 Introduction 1 1.1 Motivation . 1 1.1.1 Population Trends . 1 1.1.2 Living Options for the Elderly . 2 1.1.3 Companionship . 3 1.1.4 Current Technologies . 4 1.2 Proposed System . 5 1.2.1 Personal Assistant Functionalities . -
Intellibot: a Domain-Specific Chatbot for the Insurance Industry
IntelliBot: A Domain-specific Chatbot for the Insurance Industry MOHAMMAD NURUZZAMAN A thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy UNSW Canberra at Australia Defence Force Academy (ADFA) School of Business 20 October 2020 ORIGINALITY STATEMENT ‘I hereby declare that this submission is my own work and to the best of my knowledge it contains no materials previously published or written by another person, or substantial proportions of material which have been accepted for the award of any other degree or diploma at UNSW or any other educational institute, except where due acknowledgement is made in the thesis. Any contribution made to the research by others, with whom I have worked at UNSW or elsewhere, is explicitly acknowledged in the thesis. I also declare that the intellectual content of this thesis is the product of my own work, except to the extent that assistance from others in the project’s design and conception or in style, presentation and linguistic expression is acknowledged.’ Signed Date To my beloved parents Acknowledgement Writing a thesis is a great process to review not only my academic work but also the journey I took as a PhD student. I have spent four lovely years at UNSW Canberra in the Australian Defence Force Academy (ADFA). Throughout my journey in graduate school, I have been fortunate to come across so many brilliant researchers and genuine friends. It is the people who I met shaped who I am today. This thesis would not have been possible without them. My gratitude goes out to all of them. -
A Robust Dialogue System with Spontaneous Speech
A Robust Dialogue System with Spontaneous Speech Understanding and Cooperative Response Toshihiko ITOH, Akihiro DENDA, Satoru KOGURE and Seiichi NAKAGAWA Department of Information and Computer Sciences Toyohashi University of Technology Tenpaku-cho, Toyohashi-shi, Aichi-ken, 441, Japan E-mail address • {itoh, akihiro, kogure, nakagawa}@slp.tutics.tut.ac.jp 1 Introduction 2 A Multi-Modal Dialogue System A spoken dialogue system that can understand spon- The domain of our dialogue system is "Mt. Fuji taneous speech needs to handle extensive range of sightseeing guidance (the vocabulary size is 292 speech in comparision with the read speech that has words for the recognizer and 948 words for the inter- been studied so far. The spoken language has looser preter, and the test-set word perplexity is 103)", The restriction of the grammar than the written language dialogue system is composed of 4 parts: Input by and has ambiguous phenomena such as interjections, speech recognizer and touch screen, graphical user ellipses, inversions, repairs, unknown words and so interface, interpreter, and response generator. The on. It must be noted that the recognition rate of the latter two parts are described in Sections 3 and 4. speech recognizer is limited by the trade-off between the looseness of linguistic constraints and recogni- 2.1 Spontaneous Speech Recognizer tion precision , and that the recognizer may out- The speech recognizer uses a frame-synchronous one put a sentence as recognition results which human pass Viterbi algorithm and Earley like parser for would never say. Therefore, the interpreter that re- context-free grammar, while using HMMs as sylla- ceives recognized sentences must cope not only with ble units. -
The Inner Circle Guide to AI, Chatbots & Machine Learning
The Inner Circle Guide to AI, Chatbots & Machine Learning Sponsored by The Inner Circle Guide to AI, Chatbots and Machine Learning © ContactBabel 2019 Please note that all information is believed correct at the time of publication, but ContactBabel does not accept responsibility for any action arising from errors or omissions within the report, links to external websites or other third-party content. 2 Understand the customer experience with the power of AI Employees Customers Businesses Increase agent Elevate customer Gain improved engagement experience VoC insights Artificial Customer Machine Intelligence surveys learning Recorded CRM VoC calls notes analytics Social media Chatbots Surveys opentext.com/explore CONTENTS Contents ..................................................................................................................................................... 4 Table of Figures ........................................................................................................................................... 6 About the Inner Circle Guides ..................................................................................................................... 7 AI: Definitions and Terminology ................................................................................................................. 9 Definitions............................................................................................................................................. 11 Use Cases for AI in the Contact Centre .................................................................................................... -
Oliver Knill: March 2000 Literature: Peter Norvig, Paradigns of Artificial Intelligence Programming Daniel Juravsky and James Martin, Speech and Language Processing
ENTRY ARTIFICIAL INTELLIGENCE [ENTRY ARTIFICIAL INTELLIGENCE] Authors: Oliver Knill: March 2000 Literature: Peter Norvig, Paradigns of Artificial Intelligence Programming Daniel Juravsky and James Martin, Speech and Language Processing Adaptive Simulated Annealing [Adaptive Simulated Annealing] A language interface to a neural net simulator. artificial intelligence [artificial intelligence] (AI) is a field of computer science concerned with the concepts and methods of symbolic knowledge representation. AI attempts to model aspects of human thought on computers. Aspectrs of AI: computer vision • language processing • pattern recognition • expert systems • problem solving • roboting • optical character recognition • artificial life • grammars • game theory • Babelfish [Babelfish] Online translation system from Systran. Chomsky [Chomsky] Noam Chomsky is a pioneer in formal language theory. He is MIT Professor of Linguistics, Linguistic Theory, Syntax, Semantics and Philosophy of Language. Eliza [Eliza] One of the first programs to feature English output as well as input. It was developed by Joseph Weizenbaum at MIT. The paper appears in the January 1966 issue of the "Communications of the Association of Computing Machinery". Google [Google] A search engine emerging at the end of the 20'th century. It has AI features, allows not only to answer questions by pointing to relevant webpages but can also do simple tasks like doing arithmetic computations, convert units, read the news or find pictures with some content. GPS [GPS] General Problem Solver. A program developed in 1957 by Alan Newell and Herbert Simon. The aim was to write a single computer program which could solve any problem. One reason why GPS was destined to fail is now at the core of computer science. -
Characteristics of Text-To-Speech and Other Corpora
Characteristics of Text-to-Speech and Other Corpora Erica Cooper, Emily Li, Julia Hirschberg Columbia University, USA [email protected], [email protected], [email protected] Abstract “standard” TTS speaking style, whether other forms of profes- sional and non-professional speech differ substantially from the Extensive TTS corpora exist for commercial systems cre- TTS style, and which features are most salient in differentiating ated for high-resource languages such as Mandarin, English, the speech genres. and Japanese. Speakers recorded for these corpora are typically instructed to maintain constant f0, energy, and speaking rate and 2. Related Work are recorded in ideal acoustic environments, producing clean, consistent audio. We have been developing TTS systems from TTS speakers are typically instructed to speak as consistently “found” data collected for other purposes (e.g. training ASR as possible, without varying their voice quality, speaking style, systems) or available on the web (e.g. news broadcasts, au- pitch, volume, or tempo significantly [1]. This is different from diobooks) to produce TTS systems for low-resource languages other forms of professional speech in that even with the rela- (LRLs) which do not currently have expensive, commercial sys- tively neutral content of broadcast news, anchors will still have tems. This study investigates whether traditional TTS speakers some variance in their speech. Audiobooks present an even do exhibit significantly less variation and better speaking char- greater challenge, with a more expressive reading style and dif- acteristics than speakers in found genres. By examining char- ferent character voices. Nevertheless, [2, 3, 4] have had suc- acteristics of f0, energy, speaking rate, articulation, NHR, jit- cess in building voices from audiobook data by identifying and ter, and shimmer in found genres and comparing these to tra- using the most neutral and highest-quality utterances.