Representing Data at Three Levels of Generality Improves Multitask Machine Learning

Alexa currently has more than 90,000 skills, or abilities contributed by third-party developers — the Uber ride-sharing skill, the Jeopardy! trivia game skill, the Starbucks drink-ordering skill, and so on.

To build a skill, a third-party developer needs to supply written examples of customer requests, such as “Order my usual” or “get me a latte”, together with the actions those requests should map to. These examples are used to train the machine learning system that will process real requests when the skill goes live.

Constructing lists of sample requests, however, can be labor intensive, and smaller developers could benefit if, during training, their examples were pooled with those for similar skills. In machine learning, more training data usually leads to better performance, and the examples provided by one developer could plug holes in the list of examples provided by another.

In a paper we presented last week at the annual meeting of the Association for Computational Linguistics, my colleagues and I explore several different techniques for pooling sample requests from different skills when training a natural-language-understanding (NLU) system. We evaluated our techniques using two different public data sets and an internal data set and found that, across the board, training an NLU system simultaneously on multiple skills yielded better results than training it separately for each skill.

The advantage of multitask training is that learning the structure of, say, the request “Order me a cab” could also help an NLU system process the request “Order me a sandwich”. The risk is that too much training data about condiments could interfere with the system’s ability to, say, identify cab destinations.

To ensure that our system benefits from generalizations about common linguistic structures without losing focus on task-specific structures, we force the machine learning systems in our experiments to learn three different representations of all incoming data.

The first is a general representation, which encodes shared information across all tasks. The second is a group-level representation: Each skill’s category is known — for example, the Uber and Lyft skills are in the Travel category, while the CNN and ESPN skills are in the News category. The group-level representations capture commonalities among utterances in a given skill category. Finally, the third representation is task-specific.

The machine learning systems we used were encoder-decoder neural networks, which first learn fixed-size representations (encodings) of input data and then use those as the basis for predictions (decoding). We experimented with four different neural-network architectures. The first was a parallel architecture, meaning that each input utterance passed through the general encoder, a group-level encoder, and a task-specific encoder simultaneously, and the resulting representations were combined before passing to a task-specific decoder.

Parallel_architecture.png._CB440058006_.png
The architecture of our parallel model, which simultaneously learns to perform three tasks (a, b, and c). Tasks a and b belong to the same group (Group 1), task c to a separate group (Group 2).

The other three networks were serial, meaning that the outputs of one bank of encoders passed to a second bank before moving on to the decoders. The serial architectures differ in the order in which the shared and task-level encodings takes place and in whether the outputs of the first encoder bank are directly available to the decoders.

Serial_architectures.png._CB440058000_.png
Our three serial architectures. In the first two ((a) and (b)), the outputs of the more general encoders pass to the task-specific encoders before moving on to the decoders. In the second two ((b) and (c)), the outputs (long arrows) of the first bank of encoders are available to the decoders as separate inputs.

All of these network architectures contain separate encoder modules for individual tasks, groups of tasks, and the “universe” of all tasks. On any given input utterance, a “switch” in the network controls which of the encoders gets to process the utterance. If the user hasn’t mentioned a skill by name, the system determines the intended skill using a predictive model. If, for instance, the utterance is “Get me an Uber to the hotel”, the task-specific Uber encoder, the group-specific Travel skills encoder, and the general universe encoder process it.

During the training phase, the group-specific encoders learn how to best encode utterances characteristic of their groups, and the skill-specific encoders learn how to best encode utterances characteristic of their skills. As a result, the decoders, which always make task-specific predictions, can take advantage of three different representations of the input, ranging from general to specific. If a particular skill does not have sufficient training examples, its task-specific representations may be poor, but the group- and universe-level representations can compensate.

All of the tasks on which we tested our architectures were joint intent classification and slot-filling tasks. “Intents” are the actions that a voice agent is supposed to take. If an Alexa customer says, “Play ‘Overjoyed’ by Stevie Wonder”, the NLU system should label the whole utterance with the intent PlayMusic. Slots are the data items on which the intent acts. Here, “Overjoyed” should receive the slot tag SongName and “Stevie Wonder” the slot tag ArtistName.

To ensure that the group-level and universe-level representations remain general — that the universe-level representations don’t get hung up on the mechanics of condiment requests, for instance — we impose two constraints during training. The first is adversarial: during training, the network is rewarded when it accurately classifies slots and intents but penalized when its group- and universe-level encodings make it easy to predict which skill an utterance belongs to. This prevents task-specific features from creeping into the shared representation space.

The second constraint is an orthogonality constraint. Because the outputs of the encoders are of fixed length, they can be interpreted as points in a multidimensional space. During training, the system is rewarded if the points produced by the different types of encoders tend to cluster in different regions of the space — that is, if the task-specific encoders and the shared encoders are capturing different information.

We tested our systems on three different data sets and compared their performance to four different single-task baseline systems. On 90 Alexa skills, two of the serial systems (Serial+Highway and Serial+Highway+Swap) yielded significantly better performance on mean intent accuracy and slot F1 (which factors in both false-negative and false-positive rate) over the baseline systems. On any given test, one or another of the multitask systems was consistently the best-performing, with improvements of up to 9% over baseline.

Acknowledgments: Shiva Pentyala, Markus Dreyer

Research areas

Related content

GB, London
Our team's mission is to improve Shopping experience for customers interacting with Amazon devices via voice. We work with Alexa and multiple other teams to research and develop advanced state-of-the-art speech technologies. Do you want to be part of the team developing the latest technology that impacts the customer experience of ground-breaking products? Then come join us and make history. Key job responsibilities We are looking for a passionate, talented, and inventive Senior Applied Scientist with a background in Machine Learning to help build industry-leading Speech and Language technology. As a Senior Applied Scientist at Amazon you will work with talented peers to develop novel algorithms and modelling techniques to drive the state of the art in speech synthesis. Position Responsibilities: * Participate in the design, development, evaluation, deployment and updating of data-driven models for Speech and Language applications. * Participate in research activities including the application and evaluation of Speech and Language techniques for novel applications. * Research and implement novel ML and statistical approaches to add value to the business. * Mentor junior engineers and scientists. We are open to hiring candidates to work out of one of the following locations: London, GBR
ES, M, Madrid
Amazon's International Technology org in EU (EU INTech) is creating new ways for Amazon customers discovering Amazon catalog through new and innovative Customer experiences. Our vision is to provide the most relevant content and CX for their shopping mission. We are responsible for building the software and machine learning models to surface high quality and relevant content to the Amazon customers worldwide across the site. The team, mainly located in Madrid Technical Hub, London and Luxembourg, comprises Software Developer and ML Engineers, Applied Scientists, Product Managers, Technical Product Managers and UX Designers who are experts on several areas of ranking, computer vision, recommendations systems, Search as well as CX. Are you interested on how the experiences that fuel Catalog and Search are built to scale to customers WW? Are interesting on how we use state of the art AI to generate and provide the most relevant content? Key job responsibilities We are looking for Applied Scientists who are passionate to solve highly ambiguous and challenging problems at global scale. You will be responsible for major science challenges for our team, including working with text to image and image to text state of the art models to scale to enable new Customer Experiences WW. You will design, develop, deliver and support a variety of models in collaboration with a variety of roles and partner teams around the world. You will influence scientific direction and best practices and maintain quality on team deliverables. We are open to hiring candidates to work out of one of the following locations: Madrid, M, ESP
US, WA, Seattle
Here at Amazon, we embrace our differences. We are committed to furthering our culture of diversity and inclusion of our teams within the organization. How do you get items to customers quickly, cost-effectively, and—most importantly—safely, in less than an hour? And how do you do it in a way that can scale? Our teams of hundreds of scientists, engineers, aerospace professionals, and futurists have been working hard to do just that! We are delivering to customers, and are excited for what’s to come. Check out more information about Prime Air on the About Amazon blog (https://www.aboutamazon.com/news/transportation/amazon-prime-air-delivery-drone-reveal-photos). If you are seeking an iterative environment where you can drive innovation, apply state-of-the-art technologies to solve real world delivery challenges, and provide benefits to customers, Prime Air is the place for you. Come work on the Amazon Prime Air Team! Our Prime Air Drone Vehicle Design and Test team within Flight Sciences is looking for an outstanding engineer to help us rapidly configure, design, analyze, prototype, and test innovative drone vehicles. You’ll be responsible for developing, improving, and maintaining a suite of multi-disciplinary optimization (MDO) tools across all aircraft design disciplines. You’ll use these to explore new and novel drone vehicle conceptual designs in both focused and wide open design spaces, with the ultimate goal of meeting our customer requirements. You’ll have the opportunity to prototype vehicle designs and support wind tunnel and other testing of vehicle designs. You will directly support the Office of the Chief Program Engineer, and work closely across all vehicle subsystem teams to ensure integrated designs that meet performance, reliability, operability, manufacturing, and cost requirements. In addition, you’ll own the Flight Sciences assessments and analysis methods for the drone vehicle design as it progresses through later stages of development. About the team Our Flight Sciences Vehicle Design & Test organization includes teams that span the following disciplines: Aerodynamics, Performance, Stability & Control, Configuration & Spatial Integration, Loads, Structures, Mass Properties, Multi-disciplinary Optimization (MDO), Wind Tunnel Testing, Noise Testing, Flight Test Instrumentation, and Rapid Prototyping. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
US, WA, Bellevue
We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Some knowledge of econometrics, as well as basic familiarity with Python is necessary, and experience with SQL and UNIX would be a plus. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. You will learn how to build data sets and perform applied econometric analysis at Internet speed collaborating with economists, scientists, and product managers. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement. Roughly 85% of previous cohorts have converted to full time economics employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA
US, MA, Boston
We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Knowledge of applied econometrics is necessary, and experience with SQL and Python would be a plus. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. You will build data sets and perform applied econometric analysis, collaborating with economists, scientists, and product managers. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with future job market placement. Roughly 85% of previous cohorts have converted to full time economics employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com. We are open to hiring candidates to work out of one of the following locations: Boston, MA, USA | Seattle, WA, USA
ES, M, Madrid
Amazon's International Technology org in EU (EU INTech) is creating new ways for Amazon customers discovering Amazon catalog through new and innovative Customer experiences. Our vision is to provide the most relevant content and CX for their shopping mission. We are responsible for building the software and machine learning models to surface high quality and relevant content to the Amazon customers worldwide across the site. The team, mainly located in Madrid Technical Hub, London and Luxembourg, comprises Software Developer and ML Engineers, Applied Scientists, Product Managers, Technical Product Managers and UX Designers who are experts on several areas of ranking, computer vision, recommendations systems, Search as well as CX. Are you interested on how the experiences that fuel Catalog and Search are built to scale to customers WW? Are interesting on how we use state of the art AI to generate and provide the most relevant content? Key job responsibilities We are looking for Applied Scientists who are passionate to solve highly ambiguous and challenging problems at global scale. You will be responsible for major science challenges for our team, including working with text to image and image to text state of the art models to scale to enable new Customer Experiences WW. You will design, develop, deliver and support a variety of models in collaboration with a variety of roles and partner teams around the world. You will influence scientific direction and best practices and maintain quality on team deliverables. We are open to hiring candidates to work out of one of the following locations: Madrid, M, ESP
US, WA, Bellevue
Amazon’s Modeling and Optimization Team (MOP) is looking for a passionate individual with strong optimization and analytical skills to join us in the endeavor of designing and planning the most complex supply chain in the world. The team is responsible for optimizing the global supply chain for Amazon.com and ensuring that the company is able to inbound goods from seller and vendors, transport them to their target fulfillment center, and deliver to our customers as quickly, accurately, and cost effectively as possible. We work on problems ranging from network design to inventory management, in order to support strategic decisions. It is a terrific opportunity to have a direct impact in the business while pushing the boundaries of science. Key job responsibilities We are seeking an experienced scientist who has solid background in Operations Research, Operations Management, Applied Mathematics or other similar domain. In this role, you will develop models and solution algorithms that are innovative and scalable to solve new challenges in the inventory management space. You will collaborate with other scientists across teams to create integrated solutions that improves fulfillment speed, cost, and carbon emission. You have deep understanding of business challenges and provide scientific analysis to support business decision using a range of methodologies. You will also work with engineering teams to identify new data requirements, deploy new models or simplifying existing processes. About the team https://www.aboutamazon.com/news/innovation-at-amazon/how-artificial-intelligence-helps-amazon-deliver We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA
US, CA, Santa Clara
Do you wish to create the greatest possible worldwide impact in healthcare? We, at Amazon Health Store Tech, are working towards the best-in-class healthcare storefront to make high-quality healthcare reliable, accessible, and intuitive. Our mission is to make it dramatically easier for customers to access the healthcare products and services they need to get and stay healthy. Towards this mission, we are building the technology, products and services, that help customers find, buy, and engage with the healthcare solutions they need. We are looking to hire and develop subject-matter experts in AI who focus on data analytics, machine learning (ML), natural language understanding (NLP), and deep learning for healthcare. We target high-impact algorithmic unlocks in areas such as natural language understanding (NLU), Foundation Models, Large Language Models (LLMs), document understanding, and knowledge representation systems—all of which are of high-value to our healthcare products and services. If you are a seasoned, hands-on Principal Applied Scientist with a track record of delivering to timelines with high quality, deeply technical and innovative, we want to talk to you. You will bring AI and machine learning advancements to real-time analytics for customer-facing solutions in healthcare. You will explore, innovate, and deliver advanced ML-based technologies that involve clinical and medical data. You are a domain expert in one or more of the following areas: natural language processing and understanding (language models, transformers like BERT, GPT-3, T-5, etc.), Foundation Models and LLMs, deep learning, active learning, reinforcement learning, and bioinformatics. Key job responsibilities As an Principal Applied Scientist, you will take on challenging and ambiguous customer problems, distill customer requirements, and then deliver solutions that either leverage existing academic and medical research or utilize your own out-of-the-box but pragmatic thinking. In addition to coming up with novel solutions and prototypes, you will directly contribute to its implementation. A successful candidate has excellent technical depth, scientific vision, great implementation skills, and a drive to achieve results in a collaborative team environment. You should enjoy the process of solving real-world, open-ended problems that, quite frankly, haven’t been solved at scale anywhere before. Along the way, we guarantee you’ll get opportunities to be a fearless disruptor, prolific innovator, and a reputed problem solver—someone who truly enables machine learning and statistics to truly impact the lives and health of millions of customers. You mentor and help develop a team of Applied Scientists and SDEs and work with key leaders to guide this top talent to push the boundary of science and next generation of product. They will lead the technical implementation of our evidence-based retrieval sub-system that ingests, indexes and retrieves relevant data in different forms and from multiple sources given the customer question and context. We are open to hiring candidates to work out of one of the following locations: Santa Clara, CA, USA | Seattle, WA, USA
US, WA, Bellevue
Imagine being part of an agile team where your ideas have the potential to reach millions of customers. Picture working on cutting-edge, customer-facing solutions, where every team member is a critical voice in the decision making process. Envision being able to leverage the resources of a Fortune 500 company within the atmosphere of a start-up. Welcome to Amazon’s NCRC team. We solve complex problems in an ambiguous space, focusing on reducing return costs and improving the customer experience. We build solutions that are distributed on a large scale, positively impacting experiences for our customers and sellers. Come innovate with the NCRC team! The Net Cost of Refunds and Concessions (NCRC) team is looking for a Senior Manager Data Science to lead a team of economists, business intelligence engineers and business analysts who investigate business problems, develop insights and build models & algorithms that predict and quantify new opportunity. The team instigates and productionalizes nascent solutions around four pillars: outbound defects, inbound defects, yield optimization and returns reduction. These four pillars interact, resulting in impacts to our overall return rate, associated costs, and customer satisfaction. You may have seen some downstream impacts of our work including Amazon.com customer satisfaction badges on the website and app, new returns drop off optionality, and faster refunds for low cost items. In this role, you will set the science vision and direction for the team, collaborating with internal stakeholders across our returns and re-commerce teams to scale and advance science solutions. This role is based in Bellevue, WA Key job responsibilities * Single threaded leader responsible for setting and driving science strategy for the organization. * Lead and provide coaching to a team of Scientists, Economists, Business Intelligence Engineers and Business Analysts. * Partner with Engineering, Product and Machine Learning leaders to deliver insights and recommendations across NCRC initiatives. * Lead research and development of models and science products powering return cost reduction. * Educate and evangelize across internal teams on analytics, insights and measurement by writing whitepapers, knowledge documentation and delivering learning sessions. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA
US, WA, Bellevue
We are designing the future. If you are in quest of an iterative fast-paced environment, where you can drive innovation through scientific inquiry, and provide tangible benefit to hundreds of thousands of our associates worldwide, this is your opportunity. Come work on the Amazon Worldwide Fulfillment Design & Engineering Team! We are looking for an experienced and Research Scientist with background in Ergonomics and Industrial Human Factors, someone that is excited to work on complex real-world challenges for which a comprehensive scientific approach is necessary to drive solutions. Your investigations will define human factor / ergonomic thresholds resulting in design and implementation of safe and efficient workspaces and processes for our associates. Your role will entail assessment and design of manual material handling tasks throughout the entire Amazon network. You will identify fundamental questions pertaining to the human capabilities and tolerances in a myriad of work environments, and will initiate and lead studies that will drive decision making on an extreme scale. .You will provide definitive human factors/ ergonomics input and participate in design with every single design group in our network, including Amazon Robotics, Engineering R&D, and Operations Engineering. You will work closely with our Worldwide Health and Safety organization to gain feedback on designs and work tenaciously to continuously improve our associate’s experience. Key job responsibilities - Collaborating and designing work processes and workspaces that adhere to human factors / ergonomics standards worldwide. - Producing comprehensive and assessments of workstations and processes covering biomechanical, physiological, and psychophysical demands. - Effectively communicate your design rationale to multiple engineering and operations entities. - Identifying gaps in current human factors standards and guidelines, and lead comprehensive studies to redefine “industry best practices” based on solid scientific foundations. - Continuously strive to gain in-depth knowledge of your profession, as well as branch out to learn about intersecting fields, such as robotics and mechatronics. - Travelling to our various sites to perform thorough assessments and gain in-depth operational feedback, approximately 25%-50% of the time. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA