Conversational Chatbot Using Deep Learning Algorithm and Attention Mechanism
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Integrating Optical Character Recognition and Machine
Integrating Optical Character Recognition and Machine Translation of Historical Documents Haithem Afli and Andy Way ADAPT Centre School of Computing Dublin City University Dublin, Ireland haithem.afli, andy.way @adaptcentre.ie { } Abstract Machine Translation (MT) plays a critical role in expanding capacity in the translation industry. However, many valuable documents, including digital documents, are encoded in non-accessible formats for machine processing (e.g., Historical or Legal documents). Such documents must be passed through a process of Optical Character Recognition (OCR) to render the text suitable for MT. No matter how good the OCR is, this process introduces recognition errors, which often renders MT ineffective. In this paper, we propose a new OCR to MT framework based on adding a new OCR error correction module to enhance the overall quality of translation. Experimenta- tion shows that our new system correction based on the combination of Language Modeling and Translation methods outperforms the baseline system by nearly 30% relative improvement. 1 Introduction While research on improving Optical Character Recognition (OCR) algorithms is ongoing, our assess- ment is that Machine Translation (MT) will continue to produce unacceptable translation errors (or non- translations) based solely on the automatic output of OCR systems. The problem comes from the fact that current OCR and Machine Translation systems are commercially distinct and separate technologies. There are often mistakes in the scanned texts as the OCR system occasionally misrecognizes letters and falsely identifies scanned text, leading to misspellings and linguistic errors in the output text (Niklas, 2010). Works involved in improving translation services purchase off-the-shelf OCR technology but have limited capability to adapt the OCR processing to improve overall machine translation perfor- mance. -
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. -
Machine Translation for Academic Purposes Grace Hui-Chin Lin
Proceedings of the International Conference on TESOL and Translation 2009 December 2009: pp.133-148 Machine Translation for Academic Purposes Grace Hui-chin Lin PhD Texas A&M University College Station Master of Science, University of Southern California Paul Shih Chieh Chien Center for General Education, Taipei Medical University Abstract Due to the globalization trend and knowledge boost in the second millennium, multi-lingual translation has become a noteworthy issue. For the purposes of learning knowledge in academic fields, Machine Translation (MT) should be noticed not only academically but also practically. MT should be informed to the translating learners because it is a valuable approach to apply by professional translators for diverse professional fields. For learning translating skills and finding a way to learn and teach through bi-lingual/multilingual translating functions in software, machine translation is an ideal approach that translation trainers, translation learners, and professional translators should be familiar with. In fact, theories for machine translation and computer assistance had been highly valued by many scholars. (e.g., Hutchines, 2003; Thriveni, 2002) Based on MIT’s Open Courseware into Chinese that Lee, Lin and Bonk (2007) have introduced, this paper demonstrates how MT can be efficiently applied as a superior way of teaching and learning. This article predicts the translated courses utilizing MT for residents of global village should emerge and be provided soon in industrialized nations and it exhibits an overview about what the current developmental status of MT is, why the MT should be fully applied for academic purpose, such as translating a textbook or teaching and learning a course, and what types of software can be successfully applied. -
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. -
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. -
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 .................................................................................................... -
Machine Translation for Language Preservation
Machine translation for language preservation Steven BIRD1,2 David CHIANG3 (1) Department of Computing and Information Systems, University of Melbourne (2) Linguistic Data Consortium, University of Pennsylvania (3) Information Sciences Institute, University of Southern California [email protected], [email protected] ABSTRACT Statistical machine translation has been remarkably successful for the world’s well-resourced languages, and much effort is focussed on creating and exploiting rich resources such as treebanks and wordnets. Machine translation can also support the urgent task of document- ing the world’s endangered languages. The primary object of statistical translation models, bilingual aligned text, closely coincides with interlinear text, the primary artefact collected in documentary linguistics. It ought to be possible to exploit this similarity in order to improve the quantity and quality of documentation for a language. Yet there are many technical and logistical problems to be addressed, starting with the problem that – for most of the languages in question – no texts or lexicons exist. In this position paper, we examine these challenges, and report on a data collection effort involving 15 endangered languages spoken in the highlands of Papua New Guinea. KEYWORDS: endangered languages, documentary linguistics, language resources, bilingual texts, comparative lexicons. Proceedings of COLING 2012: Posters, pages 125–134, COLING 2012, Mumbai, December 2012. 125 1 Introduction Most of the world’s 6800 languages are relatively unstudied, even though they are no less im- portant for scientific investigation than major world languages. For example, before Hixkaryana (Carib, Brazil) was discovered to have object-verb-subject word order, it was assumed that this word order was not possible in a human language, and that some principle of universal grammar must exist to account for this systematic gap (Derbyshire, 1977). -
Machine Translation and Computer-Assisted Translation: a New Way of Translating? Author: Olivia Craciunescu E-Mail: Olivia [email protected]
Machine Translation and Computer-Assisted Translation: a New Way of Translating? Author: Olivia Craciunescu E-mail: [email protected] Author: Constanza Gerding-Salas E-mail: [email protected] Author: Susan Stringer-O’Keeffe E-mail: [email protected] Source: http://www.translationdirectory.com The Need for Translation IT has produced a screen culture that Technology tends to replace the print culture, with printed documents being dispensed Advances in information technology (IT) with and information being accessed have combined with modern communication and relayed directly through computers requirements to foster translation automation. (e-mail, databases and other stored The history of the relationship between information). These computer documents technology and translation goes back to are instantly available and can be opened the beginnings of the Cold War, as in the and processed with far greater fl exibility 1950s competition between the United than printed matter, with the result that the States and the Soviet Union was so intensive status of information itself has changed, at every level that thousands of documents becoming either temporary or permanent were translated from Russian to English and according to need. Over the last two vice versa. However, such high demand decades we have witnessed the enormous revealed the ineffi ciency of the translation growth of information technology with process, above all in specialized areas of the accompanying advantages of speed, knowledge, increasing interest in the idea visual -
A Survey on Different Algorithms Used in Chatbot
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 07 Issue: 05 | May 2020 www.irjet.net p-ISSN: 2395-0072 A survey on Different Algorithms used in Chatbot Siddhi Pardeshi1, Suyasha Ovhal2, Pranali Shinde3, Manasi Bansode4, Anandkumar Birajdar5 1Siddhi Pardeshi, Student, Dept. of Computer Engineering, Pimpri Chinchwad College of Engineering, Pune Maharashtra, India 2Suyasha Ovhal, Student, Dept. of Computer Engineering, Pimpri Chinchwad College of Engineering, Pune Maharashtra, India 3Pranali Shinde, Student, Dept. of Computer Engineering, Pimpri Chinchwad College of Engineering, Pune Maharashtra, India 4Manasi Bansode, Student, Dept. of Computer Engineering, Pimpri Chinchwad College of Engineering, Pune Maharashtra, India 5Professor, Dept. of Computer Engineering, Pimpri Chinchwad College of Engineering, Pune, Maharashtra, India ---------------------------------------------------------------------***---------------------------------------------------------------------- Abstract - Machines are working similar to humans Rule-based/Command based: In these types of chatbots, because of advanced technological concepts. Best example is predefined rules are stored which includes questions and chatbot which depends on advanced concepts in computer answers. Based on what question has requested by the user science. Chatbots serve as a medium for the communication chatbot searches for an answer. But this gives limitations on between human and machine. There are a number of chatbots the type of questions and answers to be stored. and design techniques available in market that perform Intelligent Chatbots/AI Chatbots: To overcome the issue different function and can be implemented in sectors like faced by rule based chatbots intelligent chatbots are business sector, medical sector, farming etc. The technology developed. As these are based on advanced machine learning used for the advancement of conversational agent is natural concepts, they have the ability to learn on their own language processing (NLP). -
Language Service Translation SAVER Technote
TechNote August 2020 LANGUAGE TRANSLATION APPLICATIONS The U.S. Department of Homeland Security (DHS) AEL Number: 09ME-07-TRAN established the System Assessment and Validation for Language translation equipment used by emergency services has evolved into Emergency Responders commercially available, mobile device applications (apps). The wide adoption of (SAVER) Program to help cell phones has enabled language translation applications to be useful in emergency responders emergency scenarios where first responders may interact with individuals who improve their procurement speak a different language. Most language translation applications can identify decisions. a spoken language and translate in real-time. Applications vary in functionality, Located within the Science such as the ability to translate voice or text offline and are unique in the number and Technology Directorate, of available translatable languages. These applications are useful tools to the National Urban Security remedy communication barriers in any emergency response scenario. Technology Laboratory (NUSTL) manages the SAVER Overview Program and conducts objective operational During an emergency response, language barriers can challenge a first assessments of commercial responder’s ability to quickly assess and respond to a situation. First responders equipment and systems must be able to accurately communicate with persons at an emergency scene in relevant to the emergency order to provide a prompt, appropriate response. The quick translations provided responder community. by mobile apps remove the language barrier and are essential to the safety of The SAVER Program gathers both first responders and civilians with whom they interact. and reports information about equipment that falls within the Before the adoption of smartphones, first responders used language interpreters categories listed in the DHS and later language translation equipment such as hand-held language Authorized Equipment List translation devices (Figure 1). -
Terminology Extraction, Translation Tools and Comparable Corpora Helena Blancafort, Béatrice Daille, Tatiana Gornostay, Ulrich Heid, Claude Méchoulam, Serge Sharoff
TTC: Terminology Extraction, Translation Tools and Comparable Corpora Helena Blancafort, Béatrice Daille, Tatiana Gornostay, Ulrich Heid, Claude Méchoulam, Serge Sharoff To cite this version: Helena Blancafort, Béatrice Daille, Tatiana Gornostay, Ulrich Heid, Claude Méchoulam, et al.. TTC: Terminology Extraction, Translation Tools and Comparable Corpora. 14th EURALEX International Congress, Jul 2010, Leeuwarden/Ljouwert, Netherlands. pp.263-268. hal-00819365 HAL Id: hal-00819365 https://hal.archives-ouvertes.fr/hal-00819365 Submitted on 2 May 2013 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. TTC: Terminology Extraction, Translation Tools and Comparable Corpora Helena Blancafort, Syllabs, Universitat Pompeu Fabra, Barcelona, Spain Béatrice Daille, LINA, Université de Nantes, France Tatiana Gornostay, Tilde, Latva Ulrich Heid, IMS, Universität Stuttgart, Germany Claude Mechoulam, Sogitec, France Serge Sharoff, CTS, University of Leeds, England The need for linguistic resources in any natural language application is undeniable. Lexicons and terminologies -
Acknowledging the Needs of Computer-Assisted Translation Tools Users: the Human Perspective in Human-Machine Translation Annemarie Taravella and Alain O
The Journal of Specialised Translation Issue 19 – January 2013 Acknowledging the needs of computer-assisted translation tools users: the human perspective in human-machine translation AnneMarie Taravella and Alain O. Villeneuve, Université de Sherbrooke, Quebec ABSTRACT The lack of translation specialists poses a problem for the growing translation markets around the world. One of the solutions proposed for the lack of human resources is automated translation tools. In the last few decades, organisations have had the opportunity to increase their use of technological resources. However, there is no consensus on the way that technological resources should be integrated into translation service providers (TSP). The approach taken by this article is to set aside both 100% human translation and 100% machine translation (without human intervention), to examine a third, more realistic solution: interactive translation where humans and machines co-operate. What is the human role? Based on the conceptual framework of information systems and organisational sciences, we recommend giving users, who are mainly translators for whom interactive translation tools are designed, a fundamental role in the thinking surrounding the implementation of a technological tool. KEYWORDS Interactive translation, information system, implementation, business process reengineering, organisational science. 1. Introduction Globalisation and the acceleration of world trade operations have led to an impressive growth of the global translation services market. According to EUATC (European Union of Associations of Translation Companies), translation industry is set to grow around five percent annually in the foreseeable future (Hager, 2008). With its two official languages, Canada is a good example of an important translation market, where highly skilled professional translators serve not only the domestic market, but international clients as well.