DOCSLIB.ORG

Genie: a Generator of Natural Language Semantic Parsers For

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Genie: a Generator of Natural Language Semantic Parsers For Genie: A Generator of Natural Language Semantic Parsers for Virtual Assistant Commands Giovanni Campagna∗ Silei Xu∗ Mehrad Moradshahi Computer Science Department Computer Science Department Computer Science Department Stanford University Stanford University Stanford University Stanford, CA, USA Stanford, CA, USA Stanford, CA, USA gcampagn@cs:stanford:edu silei@cs:stanford:edu mehrad@cs:stanford:edu Richard Socher Monica S. Lam Salesforce, Inc. Computer Science Department Palo Alto, CA, USA Stanford University rsocher@salesforce:com Stanford, CA, USA lam@cs:stanford:edu Abstract → Natural language processing; • Software and its engi- To understand diverse natural language commands, virtual neering → Context specific languages. assistants today are trained with numerous labor-intensive, Keywords virtual assistants, semantic parsing, training manually annotated sentences. This paper presents a method- data generation, data augmentation, data engineering ology and the Genie toolkit that can handle new compound ACM Reference Format: commands with significantly less manual effort. Giovanni Campagna, Silei Xu, Mehrad Moradshahi, Richard Socher, We advocate formalizing the capability of virtual assistants and Monica S. Lam. 2019. Genie: A Generator of Natural Lan- with a Virtual Assistant Programming Language (VAPL) and guage Semantic Parsers for Virtual Assistant Commands. In Pro- using a neural semantic parser to translate natural language ceedings of the 40th ACM SIGPLAN Conference on Programming into VAPL code. Genie needs only a small realistic set of input Language Design and Implementation (PLDI ’19), June 22–26, 2019, sentences for validating the neural model. Developers write Phoenix, AZ, USA. ACM, New York, NY, USA, 17 pages. https: templates to synthesize data; Genie uses crowdsourced para- //doi:org/10:1145/3314221:3314594 phrases and data augmentation, along with the synthesized Thingpedia data, to train a semantic parser. User input We also propose design principles that make VAPL lan- guages amenable to natural language translation. We apply Get a cat picture and post it on Facebook with these principles to revise ThingTalk, the language used by caption funny cat. the Almond virtual assistant. We use Genie to build the first semantic parser that can support compound virtual assistants commands with unquoted free-form parameters. Genie achieves a 62% accuracy on realistic user inputs. We now => @com.thecatapi.get() ThingTalk => @com.facebook.post_picture(picture_url = demonstrate Genie’s generality by showing a 19% and 31% picture_url, caption = "funny cat"); improvement over the previous state of the art on a music skill, aggregate functions, and access control. f CCS Concepts • Human-centered computing → Per- sonal digital assistants; • Computing methodologies Execution result ∗Equal contribution PLDI ’19, June 22–26, 2019, Phoenix, AZ, USA © 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM. Figure 1. An example of translating and executing com- This is the author’s version of the work. It is posted here for your personal pound virtual assistant commands. use. Not for redistribution. The definitive Version of Record was published in Proceedings of the 40th ACM SIGPLAN Conference on Programming Language 1 Introduction Design and Implementation (PLDI ’19), June 22–26, 2019, Phoenix, AZ, USA, https://doi:org/10:1145/3314221:3314594. Personal virtual assistants provide users with a natural lan- guage interface to a wide variety of web services and IoT PLDI ’19, June 22–26, 2019, Phoenix, AZ, USA G. Campagna, S. Xu, M. Moradshahi, R. Socher, and M. S. Lam devices. Not only must they understand primitive commands However, the original ThingTalk was not amenable to nat- across many domains, but they must also understand the ural language translation, and no usable semantic parser has composition of these commands to perform entire tasks. been developed. In attempting to create an effective semantic State-of-the-art virtual assistants are based on semantic pars- parser for ThingTalk, we discovered important design princi- ing, a machine learning algorithm that converts natural lan- ples for VAPL, such as matching the non-developers’ mental guage to a semantic representation in a formal language. model and keeping the semantics of components orthogo- The breadth of the virtual assistant interface makes it par- nal. Also, VAPL programs must have a (unique) canonical ticularly challenging to design the semantic representation. form so the result of the neural network can be checked for Furthermore, there is no existing corpus of natural language correctness easily. We applied these principles to overhaul commands to train the neural model for new capabilities. and extend the design of ThingTalk. Unless noted otherwise, This paper advocates using a Virtual Assistant Programming we use ThingTalk to refer to the new design in the rest of Language (VAPL) to capture the formal semantics of the vir- the paper. tual assistant capability. We also present Genie, a toolkit for creating a semantic parser for new virtual assistant capabili- 1.2 Training Data Acquisition ties that can be used to bootstrap real data acquisition. Virtual assistant development is labor-intensive, with Alexa boasting a workforce of 10,000 employees [36]. Obtaining training data for the semantic parser is one of the challeng- 1.1 Virtual Assistant Programming Languages ing tasks. How do we get training data before deployment? Previous semantic parsing work, including commercial as- How can we reduce the cost of annotating usage data? Wang sistants, typically translates natural language into an inter- et al. [57] propose a solution to acquire training data for the mediate representation that matches the semantics of the task of question answering over simple domains. They use a sentences closely [4, 30, 33, 44, 51]. For example, the Alexa syntax-driven approach to create a canonical sentence for Meaning Representation Language [30, 44] is associated with each formal program, ask crowdsourced workers to para- a closed ontology of 20 domains, each manually tuned for phrase canonical sentences to make them more natural, then accuracy. Semantically equivalent sentences have different use the paraphrases to train a machine learning model that representations, requiring complex and expensive manual can match input sentences against possible canonical sen- annotation by experts, who must know the details of the tences. Wang et al.’s approach designs each domain ontology formalism and associated ontology. The ontology also limits individually, and each domain is small enough that all possi- the scope of the available commands, as every parameter ble logical forms can be enumerated up to a certain depth. must be an entity in the ontology (a person, a location, etc.) This approach was used in the original ThingTalk seman- and cannot be free-form text. tic parser and has been shown to be inadequate [8]. It is Our approach is to represent the capability of the virtual as- infeasible to collect paraphrases for all the sentences sup- sistant fully and formally as a VAPL; we use a deep-learning ported by a VAPL language. Virtual assistants have powerful semantic parser to translate natural language into VAPL code, constructs to connect many diverse domains, and their ca- which can directly be executed by the assistant. Thus, the pability scales superlinearly with the addition of APIs. Even assistant’s full capability is exposed to the neural network, with our small Thingpedia, ThingTalk supports hundreds eliminating the need and inefficiency of an intermediate rep- of thousands of distinct programs. Also, it is not possible resentation. The VAPL code can also be converted back into to generate just one canonical natural language that can a canonical natural language sentence to confirm the pro- be understood across different domains. Crowdworkers of- gram before execution. Furthermore, new capabilities can ten paraphrase sentences incorrectly or just make minor be supported by extending the VAPL. modifications to original sentences. The ThingTalk language designed for the open-source Al- Our approach is to design a NL-template language to help mond virtual assistant is an example of a VAPL[8]. ThingTalk developers data-engineer a good training set. This language has one construct which has three clauses: when some event lets developers capture common ways in which VAPL pro- happens, get some data, and perform some action, each of grams are expressed in natural language. The NL-templates which can be predicated. This construct combines primitives are used to synthesize pairs of natural language sentences from the extensible runtime skill library, Thingpedia, cur- and their corresponding VAPL code. A sample of such sen- rently consisting of over 250 APIs to Internet services and tences is paraphrased by crowdsource workers to make them IoT devices. Despite its lean syntax, ThingTalk is expres- more natural. The paraphrases further inform more useful sive. It is a superset of what can be expressed with IFTTT, templates, which in turn derives more diverse sentences which has crowdsourced more than 250,000 unique com- for paraphrasing. This iterative process increases the cost- pound commands [56]. Fig.1 shows how a natural-language effectiveness of paraphrasing. sentence can be translated into a ThingTalk program, using Whereas the traditional approach is only to train with the services in Thingpedia. paraphrase data, we are the first to add synthesized
Load more

Recommended publications
  • Digital Workaholics:A Click Away
    GSJ: Volume 9, Issue 5, May 2021 ISSN 2320-9186 1479 GSJ: Volume 9, Issue 5, May 2021, Online: ISSN 2320-9186 www.globalscientificjournal.com DIGITAL WORKAHOLICS:A CLICK AWAY Tripti Srivastava Muskan Chauhan Rohit Pandey Galgotias University Galgotias University Galgotias University [email protected] muskanchauhansmile@g [email protected] m mail.com om ABSTRACT:-In today’s world, Artificial Intelligence (AI) has become an 1. INTRODUCTION integral part of human life. There are many applications of AI such as Chatbot, AI defines as those device that understands network security, complex problem there surroundings and took actions which solving, assistants, and many such. increase there chance to accomplish its Artificial Intelligence is designed to have outcomes. Artifical Intelligence used as “a cognitive intelligence which learns from system’s ability to precisely interpret its experience to take future decisions. A external data, to learn previous such data, virtual assistant is also an example of and to use these learnings to accomplish cognitive intelligence. Virtual assistant distinct outcomes and tasks through supple implies the AI operated program which adaptation.” Artificial Intelligence is the can assist you to reply to your query or developing branch of computer science. virtually do something for you. Currently, Having much more power and ability to the virtual assistant is used for personal develop the various application. AI implies and professional use. Most of the virtual the use of a different algorithm to solve assistant is device-dependent and is bind to various problems. The major application a single user or device. It recognizes only being Optical character recognition, one user.
    [Show full text]
  • Deep Almond: a Deep Learning-Based Virtual Assistant [Language-To-Code Synthesis of Trigger-Action Programs Using Seq2seq Neural Networks]
    Deep Almond: A Deep Learning-based Virtual Assistant [Language-to-code synthesis of Trigger-Action programs using Seq2Seq Neural Networks] Giovanni Campagna Rakesh Ramesh Computer Science Department Stanford University Stanford, CA 94305 {gcampagn, rakeshr1}@stanford.edu Abstract Virtual assistants are the cutting edge of end user interaction, thanks to endless set of capabilities across multiple services. The natural language techniques thus need to be evolved to match the level of power and sophistication that users ex- pect from virtual assistants. In this report we investigate an existing deep learning model for semantic parsing, and we apply it to the problem of converting nat- ural language to trigger-action programs for the Almond virtual assistant. We implement a one layer seq2seq model with attention layer, and experiment with grammar constraints and different RNN cells. We take advantage of its existing dataset and we experiment with different ways to extend the training set. Our parser shows mixed results on the different Almond test sets, performing better than the state of the art on synthetic benchmarks by about 10% but poorer on real- istic user data by about 15%. Furthermore, our parser is shown to be extensible to generalization, as well as or better than the current system employed by Almond. 1 Introduction Today, we can ask virtual assistants like Amazon Alexa, Apple’s Siri, Google Now to perform simple tasks like, “What’s the weather”, “Remind me to take pills in the morning”, etc. in natural language. The next evolution of natural language interaction with virtual assistants is in the form of task automation such as “turn on the air conditioner whenever the temperature rises above 30 degrees Celsius”, or “if there is motion on the security camera after 10pm, call Bob”.
    [Show full text]
  • Inside Chatbots – Answer Bot Vs. Personal Assistant Vs. Virtual Support Agent
    WHITE PAPER Inside Chatbots – Answer Bot vs. Personal Assistant vs. Virtual Support Agent Artificial intelligence (AI) has made huge strides in recent years and chatbots have become all the rage as they transform customer service. Terminology, such as answer bots, intelligent personal assistants, virtual assistants for business, and virtual support agents are used interchangeably, but are they the same thing? The concepts are similar, but each serves a different purpose, has specific capabilities, varying extensibility, and provides a range of business value. 2018 © Copyright ServiceAide, Inc. 1-650-206-8988 | www.serviceaide.com | [email protected] INSIDE CHATBOTS – ANSWER BOT VS. PERSONAL ASSISTANT VS. VIRTUAL SUPPORT AGENT WHITE PAPER Before we dive into each solution, a short technical primer is in order. A chatbot is an AI-based solution that uses natural language understanding to “understand” a user’s statement or request and map that to a specific intent. The ‘intent’ is equivalent to the intention or ‘the want’ of the user, such as ordering a pizza or booking a flight. Once the chatbot understands the intent of the user, it can carry out the corresponding task(s). To create a chatbot, someone (the developer or vendor) must determine the services that the ‘bot’ will provide and then collect the information to support requests for the services. The designer must train the chatbot on numerous speech patterns (called utterances) which cover various ways a user or customer might express intent. In this development stage, the developer defines the information required for a particular service (e.g. for a pizza order the chatbot will require the size of the pizza, crust type, and toppings).
    [Show full text]
  • Welsh Language Technology Action Plan Progress Report 2020 Welsh Language Technology Action Plan: Progress Report 2020
    Welsh language technology action plan Progress report 2020 Welsh language technology action plan: Progress report 2020 Audience All those interested in ensuring that the Welsh language thrives digitally. Overview This report reviews progress with work packages of the Welsh Government’s Welsh language technology action plan between its October 2018 publication and the end of 2020. The Welsh language technology action plan derives from the Welsh Government’s strategy Cymraeg 2050: A million Welsh speakers (2017). Its aim is to plan technological developments to ensure that the Welsh language can be used in a wide variety of contexts, be that by using voice, keyboard or other means of human-computer interaction. Action required For information. Further information Enquiries about this document should be directed to: Welsh Language Division Welsh Government Cathays Park Cardiff CF10 3NQ e-mail: [email protected] @cymraeg Facebook/Cymraeg Additional copies This document can be accessed from gov.wales Related documents Prosperity for All: the national strategy (2017); Education in Wales: Our national mission, Action plan 2017–21 (2017); Cymraeg 2050: A million Welsh speakers (2017); Cymraeg 2050: A million Welsh speakers, Work programme 2017–21 (2017); Welsh language technology action plan (2018); Welsh-language Technology and Digital Media Action Plan (2013); Technology, Websites and Software: Welsh Language Considerations (Welsh Language Commissioner, 2016) Mae’r ddogfen yma hefyd ar gael yn Gymraeg. This document is also available in Welsh.
    [Show full text]
  • A Generator of Natural Language Semantic Parsers for Virtual
    Genie: A Generator of Natural Language Semantic Parsers for Virtual Assistant Commands Giovanni Campagna∗ Silei Xu∗ Mehrad Moradshahi Computer Science Department Computer Science Department Computer Science Department Stanford University Stanford University Stanford University Stanford, CA, USA Stanford, CA, USA Stanford, CA, USA [email protected] [email protected] [email protected] Richard Socher Monica S. Lam Salesforce, Inc. Computer Science Department Palo Alto, CA, USA Stanford University [email protected] Stanford, CA, USA [email protected] Abstract CCS Concepts • Human-centered computing → Per- To understand diverse natural language commands, virtual sonal digital assistants; • Computing methodologies assistants today are trained with numerous labor-intensive, → Natural language processing; • Software and its engi- manually annotated sentences. This paper presents a method- neering → Context specific languages. ology and the Genie toolkit that can handle new compound Keywords virtual assistants, semantic parsing, training commands with significantly less manual effort. data generation, data augmentation, data engineering We advocate formalizing the capability of virtual assistants with a Virtual Assistant Programming Language (VAPL) and ACM Reference Format: using a neural semantic parser to translate natural language Giovanni Campagna, Silei Xu, Mehrad Moradshahi, Richard Socher, into VAPL code. Genie needs only a small realistic set of input and Monica S. Lam. 2019. Genie: A Generator of Natural Lan- sentences for validating the neural model. Developers write guage Semantic Parsers for Virtual Assistant Commands. In Pro- templates to synthesize data; Genie uses crowdsourced para- ceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI ’19), June 22–26, 2019, phrases and data augmentation, along with the synthesized Phoenix, AZ, USA.
    [Show full text]
  • Virtual Assistant Services Portfolio
    PORTFOLIO OF Virtual Assistant Services www.riojacinto.com WELCOME Hey there! My name is Rio Jacinto and I am a Virtual Assistant passionately helping busy business owners, women entrepreneurs and bloggers succeed by helping them with their website, social media accounts and others so they have more time to attract more ideal clients! I understand, You're here because you need an extra pair of hands to manage other business tasks that you're no longer have time to do. I get it, your main responsibility is to focus on building your business. I can help you with that. As a business owner you want to save some time to do more important roles in your business and I am here to help you and make that happen. Please check out my services and previous works in the next few pages. I can't wait to work with you! HOW CAN I HELP YOU MY SERVICES Creating Graphics using Canva Creating ebooks Personal errands (purchasing gifts for loved ones / family members online) Creating landing pages and sales pages FB Ads, Chatbots Hotel and Flight Booking Preparing Slideshows for Webinars or Presentations Recruitment and Outsourcing Management (source for other team members like writers) Social Media Management (Facebook, LinkedIn, Youtube, Pinterest, Instagram.) Creation of Basic Squarespace/Shopify Website Manage your Blog (Basic Squarespace/Shopify website Management) Filter and reply to comments on your blog Other virtual services, as available. PORTFOLIO FLYERS/MARKETING PORTFOLIO WEBSITE PORTFOLIO PINTEREST PORTFOLIO SOCIAL MEDIA GRAPHICS Facebook PORTFOLIO SOCIAL MEDIA GRAPHICS Instagram PORTFOLIO PRESENTATION WHITE PAPER RATES CHOOSE HERE FOR THE BEST PACKAGE THAT SUITS YOU Social Media Management Package starts from $150/mo *graphics creation/curation/scheduling/client support Pinterest Management Package starts from $50/wk *client provides stock photo or additional $2/ea.
    [Show full text]
  • Voice Assistants and Smart Speakers in Everyday Life and in Education
    Informatics in Education, 2020, Vol. 19, No. 3, 473–490 473 © 2020 Vilnius University, ETH Zürich DOI: 10.15388/infedu.2020.21 Voice Assistants and Smart Speakers in Everyday Life and in Education George TERZOPOULOS, Maya SATRATZEMI Department of Applied Informatics, University of Macedonia, Thessaloniki, Greece Email: [email protected], [email protected] Received: November 2019 Abstract. In recent years, Artificial Intelligence (AI) has shown significant progress and its -po tential is growing. An application area of AI is Natural Language Processing (NLP). Voice as- sistants incorporate AI by using cloud computing and can communicate with the users in natural language. Voice assistants are easy to use and thus there are millions of devices that incorporates them in households nowadays. Most common devices with voice assistants are smart speakers and they have just started to be used in schools and universities. The purpose of this paper is to study how voice assistants and smart speakers are used in everyday life and whether there is potential in order for them to be used for educational purposes. Keywords: artificial intelligence, smart speakers, voice assistants, education. 1. Introduction Emerging technologies like virtual reality, augmented reality and voice interaction are reshaping the way people engage with the world and transforming digital experiences. Voice control is the next evolution of human-machine interaction, thanks to advances in cloud computing, Artificial Intelligence (AI) and the Internet of Things (IoT). In the last years, the heavy use of smartphones led to the appearance of voice assistants such as Apple’s Siri, Google’s Assistant, Microsoft’s Cortana and Amazon’s Alexa.
    [Show full text]
  • Virtual Assistant Interview Questions and Answers Guide
    Virtual Assistant Interview Questions And Answers Guide. Global Guideline. https://www.globalguideline.com/ Virtual Assistant Interview Questions And Answers Global Guideline . COM Virtual Assistant Job Interview Preparation Guide. Question # 1 Tell me what are your special skills working as a virtual assistant? Answer:- Over the years, I have gained expertise in handling bookkeeping duties, online research, database management and data entry, calendar management and call handling duties. Additionally, I am well-versed in responding to support tickets and providing online troubleshooting advice. Read More Answers. Question # 2 Tell me what are your working hours? Answer:- Knowing the best times and days you can contact your VA is essential to your business. As such, you should get the VA's working hours and compare them to your requirements. And if you're going to hire someone located on the other side of the world, be mindful of the time difference. Read More Answers. Question # 3 Tell me do you have references I can contact? Answer:- While there are virtual assistants who have reviews and testimonials you can read on their blogs or website, it is still important to contact the applicant's previous clients who can vouch for his professionalism so you can validate his claims. If there is any sign of hesitation on the applicant's end when you ask this question, you might want to think twice before hiring that person. Read More Answers. Question # 4 Please explain what timezone are you in and what hours are you available? Answer:- Because virtual assistants may be based anywhere in the world, it's important to make sure you're able to communicate.
    [Show full text]
  • Chatbots & Virtual Assistants
    CHATBOTS & VIRTUAL ASSISTANTS CONVERSE’S VISION & VALUE PROPOSITION 2019 / 20 VISION PAPER 1 INTRODUCTION The market for chatbots and virtual assistants is growing at an exponential rate. A Gartner study in 2018, saw more than 160% increase in client interest around implementing chatbots and associated technologies in 2018 INTEREST from previous years. This increase has been driven by customer service, knowledge management and user support. Gartner forecasted that 70% of white collar workers will interact with conversational platforms on a daily basis by 2022. 160% This rapid adoption and maturing chatbot technology has risen the increase in client interest expectations for both customers and administrators. These expectations in 2018 are not those of speed, efficiency and accuracy alone but the ability to address diverse customers, provide business insights to partners and - Gartner function seamlessly and efficiently in cross-functional matrix organisations. And of course, do more than just having dialogues. “Today, chatbots are expected to relate INTERACTION to all users. Performing transactional functions not just informational 70% functions” of white collar workers will interact Chatbots are traditionally used in service intensive industries to provide with conversational customer service consistency at speed and at scale. Chatbots today platforms are evolving to perform services in addition to responding to questions. Services like room bookings in hospitality sectors, form submissions - Gartner and document verifications in financial sectors, delivering more value to customers and playing a vital role in an organisation’s growth in providing more services while managing manpower and associated costs. CONVERSE’S VISION The vision of Converse is to enable connected organisations by being the single platform virtual assistant embedded in whole of organisations.
    [Show full text]
  • Towards an Intelligent Virtual Assistant for Low Resource Spoken-Only
    PRELIMINARY VERSION: DO NOT CITE The AAAI Digital Library will contain the published version some time after the conference Using Radio Archives for Low-Resource Speech Recognition: Towards an Intelligent Virtual Assistant for Illiterate Users Moussa Doumbouya,1 Lisa Einstein,1,2 Chris Piech2 1 GNCode 2 Stanford University [email protected], [email protected], [email protected] Abstract recognition technology requires large annotated datasets. Unfortunately, languages spoken by illiterate people who For many of the 700 million illiterate people around the would most benefit from voice recognition technology tend world, speech recognition technology could provide a bridge to valuable information and services. Yet, those most in to fall in the “low resource” category, which in contrast need of this technology are often the most underserved by with “high resource” languages, have few available datasets. it. In many countries, illiterate people tend to speak only Transfer learning, transferring representations learned on low-resource languages, for which the datasets necessary for high resource unrelated languages, has not been explored speech technology development are scarce. In this paper, we for many low resource languages (Kunze et al. 2017). Even investigate the effectiveness of unsupervised speech represen- if transfer learning can help, labeled data is still needed to tation learning on noisy radio broadcasting archives, which develop useful models. are abundant even in low-resource languages. We make three There could be many reasons for this data deficit, includ- core contributions. First, we release two datasets to the re- ing that languages spoken by smaller populations are of- search community. The first, West African Radio Corpus, ten left behind for commercial prioritization reasons.
    [Show full text]
  • Artificial Intelligence for Virtual Assistant
    Flyer Information Management and Governance Artificial Intelligence for Virtual Assistant IDOL for Virtual Assistant, is an AI (Artificial Intelligence) powered solution, that allows organizations to offer their customers and employees access to information and processes using an automated human-like operator which can engage in natural language dialogues. streamline the retrieval process, which allows such databases of information to allow natural To address an increasingly diverse information to be obtained in a more conve- language questions to receive direct factual nient and user-friendly fashion. Moreover, it answers. This is achieved by processing and range of information requests, should offer an interface to allow configura- understanding the question and mapping it to IDOL uses our unique Natural tion of that process to ensure existing human an appropriate structured query that will in turn Language Question Answering knowledge is allowed to create and train the respond with the desired answer. An example (NLQA) technology to allow natural system to answer questions optimally. In gen- of this would be databases of financial prices conversations between end users eral, there are three independent steps to ac- over time, allowing a query such as, “What was complish this. the EPS of HPQ in Q3 2016?” to receive the and a virtual assistant, to perform precise answer. This database of structure in- queries against: curated (FAQ) Answer Bank formation is generally known as the fact bank. responses (Answer Bank), data Many administrators of support or user-help tables and data services (Fact systems have an existing set of frequently In addition, the system should have the abil- Bank), and unstructured documents asked questions that human support agents ity to understand and extract information (for (Passage Extraction); and to are trained on, or the help pages are populated example names, numbers) from unstructured data such as free-form documents.
    [Show full text]
  • Neural Logic Framework for Digital Assistants
    MEng Individual Project Imperial College London Department of Computing Neural Logic Framework for Digital Assistants Supervisor: Prof. Alessandra Russo Author: Nuri Cingillioglu Second Marker: Dr. Krysia Broda June 16, 2017 Abstract Digital assistants are becoming ubiquitous with consumers across mobile platforms help- ing with everyday tasks. The natural language interface of most assistants are built on machine learning based intent parsing techniques. This design cannot handle higher level abstract reasoning such as defaults while logic programs can incorporate them. In this project we present Kevin, a digital personal assistant with a logical framework built on top of neural networks to provide a flexible execution environment while har- nessing the capabilities of machine learning at a lower level. Kevin demonstrates natural language based logical constructs such as unification and resolution with integrated neural network information retrieval. Acknowledgements I would like to thank: • my parents for their wholehearted support, endless love and perpetual encourage- ment. • my supervisor Prof. Alessandra Russo for giving me this opportunity by accepting the project proposal and allowing me to explore the topic as well as her PhD student Mark Law for his feedback. • my personal tutor Dr. Alistair Donaldson for his support and advice for the years. • my friends for their companionship and solace throughout my degree. Contents 1 Introduction 11 1.1 Motivation.................................... 11 1.2 Objectives.................................... 12 1.3 Challenges.................................... 12 1.4 Contributions.................................. 13 2 Related Work 14 2.1 Natural Language Interactions......................... 14 2.1.1 Turing Test............................... 14 2.1.2 Conversational Agents......................... 15 2.2 Domain-specific Assistants........................... 17 2.2.1 PANDA: Virtual assistant for in-car child entertainment.....
    [Show full text]