Science innovations power Alexa Conversations dialogue management

Dialogue simulator and conversations-first modeling architecture provide ability for customers to interact with Alexa in a natural and conversational manner.

Today we announced the public beta launch of Alexa Conversations dialogue management. Alexa developers can now leverage a state-of-the-art dialogue manager powered by deep learning to create complex, nonlinear experiences — conversations that go well beyond today's typical one-shot interactions, such as "Alexa, what's the weather forecast for today?" or "Alexa, set a ten-minute pasta timer".

Alexa’s natural-language-understanding models classify requests according to domain, or the particular service that should handle the intent that the customer wants executed. The models also identify the slot types of the entities named in the requests, or the roles those entities play in fulfilling the request. In the request “Play ‘Rise Up’ by Andra Day”, the domain is Music, the intent is PlayMusic, and the names “Rise Up” and “Andra Day” fill the slots SongName and ArtistName.

Also at today's Alexa Live event, Nedim Fresko, vice president of Alexa Devices and Developers, announced that Amazon scientists have begun applying deep neural networks to custom skills and are seeing increases in accuracy. Read more here.

Natural conversations don’t follow these kinds of predetermined dialogue paths and often include anaphoric references (such as referring to a previously mentioned song by saying “play it”), contextual carryover of entities, customer revisions of requests, and many other types of interactions.

Alexa Conversations enables customers to interact with Alexa in a natural and conversational manner. At the same time, it relieves developers of the effort they would typically need to expend in authoring complex dialogue management rules, which are hard to maintain and often result in brittle customer experiences. Our dialogue augmentation algorithms and deep-learning models address the challenge of designing flexible and robust conversational experiences.

Dialogue management for Alexa Conversations is powered by two major science innovations: a dialogue simulator for data augmentation that generalizes a small number of sample dialogues provided by a developer into tens of thousands of annotated dialogues, and a conversations-first modeling architecture that leverages the generated dialogues to train deep-learning-based models to support dialogues beyond just the happy paths provided by the sample dialogues.

The Alexa Conversations dialogue simulator

Building high-performing deep-learning models requires large and diverse data sets, which are costly to acquire. With Alexa Conversations, the dialogue simulator automatically generates diversity from a few developer-provided sample dialogues that cover skill functionality, and it also generates difficult or uncommon exchanges that could occur.

The inputs to the dialogue simulator include developer application programming interfaces (APIs), slots and associated catalogues for slot values (e.g. city, state), and response templates (Alexa’s responses in different situations, such as requesting a slot value from the customer). These inputs together with their input arguments and output values define the skill-specific schema of actions and slots that the dialogue manager will predict.

Alexa Conversations dialogue simulator
The Alexa Conversations dialogue simulator generates tens of thousands of annotated dialogue examples that are used to train conversational models.

The dialogue simulator uses these inputs to generate additional sample dialogues in two steps.

In the first step, the simulator generates dialogue variations that represent different paths a conversation can take, such as different sequences of slot values and divergent paths that arise when a customer changes her mind.

More specifically, we conceive a conversation as a collaborative, goal-oriented interaction between two agents, a customer and Alexa. In this setting, the customer has a goal she wants to achieve, such as booking an airplane flight, and Alexa has access to resources, such as APIs for searching flight information or booking flights, that can help the customer reach her goal.

The simulated dialogues are generated through the interaction of two agent simulators, one for the customer, the other for Alexa. From the sample dialogues provided by the developer, the simulator first samples several plausible goals that customers interacting with the skill may want to achieve.

Conditioned on a sample goal, we generate synthetic interactions between the two simulator agents. The customer agent progressively reveals its goal to the Alexa agent, while the Alexa agent gathers the customer agent’s information, confirms information, and asks follow-up questions about missing information, guiding the interaction toward goal completion.

In the second step, the simulator injects language variations into the dialogue paths. The variations include alternate expressions of the same customer intention, such as “recommend me a movie” versus “I want to watch a movie”. Some of these alternatives are provided by the sample conversations and Alexa response templates, while others are generated through paraphrasing.

The variations also include alternate slot values (such as “Andra Day” or “Alicia Keys” for the slot ArtistName), which are sampled from slot catalogues provided by the developer. Through these two steps, the simulator generates tens of thousands of annotated dialogue examples that are used for training the conversational models.

The Alexa Conversations modeling architecture

A natural conversational experience could follow any one of a wide range of nonlinear dialogue patterns. Our conversations-first modeling architecture leverages dialogue-simulator and conversational-modeling components to support dialogue patterns that include carryover of entities, anaphora, confirmation of slots and APIs, and proactively offering related functionality, as well as robust support for a customer changing her mind midway through a conversation.

We follow an end-to-end dialogue-modeling approach, where the models take into account the current customer utterance and context from the entire conversation history to predict the optimal next actions for Alexa. Those actions might include calling a developer-provided API to retrieve information and relaying that information to the customer; asking for more information from the customer; or any number of other possibilities.

The modeling architecture is built using state-of-the-art deep-learning technology and consists of three models: a named-entity-recognition (NER) model, an action prediction (AP) model, and an argument-filling (AF) model. The models are built by combining supervised training techniques on the annotated synthetic dialogues generated by the dialogue simulator and unsupervised pretraining of large Transformer-based components on text corpora.

Alexa Conversations modeling architecture
The Alexa Conversations modeling architecture uses state-of-the-art deep-learning technology and consists of three models: a named-entity-recognition model, an action prediction model, and an argument-filling model. The models are built by combining supervised training techniques on the annotated synthetic dialogues generated by the dialogue simulator and unsupervised pretraining of large Transformer-based components on text corpora.

First, the NER model identifies slots in each of the customer utterances, selecting from slots the developer defined as part of the build-time assets (date, city, etc.). For example, for the request “search for flights to Seattle tomorrow”, the NER model will identify “Seattle” as a city slot and “tomorrow” as a date slot.

The NER model is a sequence-tagging model built using a bidirectional LSTM layer on top of a Transformer-based pretrained sentence encoder. In addition to the current sentence, NER also takes dialogue context as input, which is encoded through a hierarchical LSTM architecture that captures the conversational history, including past slots and Alexa actions.

Next, the AP model predicts the optimal next action for Alexa to take, such as calling an API or responding to the customer to either elicit more information or complete a request. The action space is defined by the APIs and Alexa response templates that the developer provides during the skill-authoring process.

The AP model is a classification model that, like the NER model, uses a hierarchical LSTM architecture to encode the current utterance and past dialogue context, which ultimately passes to a feed-forward network to generate the action prediction.

Finally, the AF model fills in the argument values for the API and response templates by looking at the entire dialogue for context. Using an attention-based pointing mechanism over the dialogue context, the AF model selects compatible slots from all slot values that the NER model recognized earlier.

For example, suppose slot values “Seattle” and “tomorrow” exist in the dialogue context for city and date slots respectively, and the AP model predicted the SearchFlight API as the optimal next action. The AF model will fill in the API arguments with the appropriate values, generating a complete API call: SearchFlight (city=“Seattle”, date="tomorrow").

The AP and AF models may also predict and generate more than one action after a customer utterance. For example, they may decide to first call an API to retrieve flight information and then call an Alexa response template to communicate this information to the customer. Therefore, the AP and AF models can make sequential predictions of actions, including the decision to stop predicting more actions and wait for the next customer request.

The finer points

Consistency check logic ensures that the resulting predictions are all valid actions, consistent with developer-provided information about their APIs. For example, the system would not generate an API call with an empty input argument, if that input argument is required by the developer.

The inputs include the entire dialogue history, as well as the latest customer request, and the resulting model predictions are contextual, relevant, and not repetitive. For example, if a customer has already provided the date of a trip while searching for a flight, Alexa will not ask for the date when booking the flight. Instead, the date provided earlier will contextually carry over and pass to the appropriate API.

We leveraged large pretrained Transformer components (BERT) that encode current and past requests in the conversation. To ensure state-of-the-art model build-time and runtime latency, we performed inference architecture optimizations such as accelerating embedding computation on GPUs, implementing efficient caching, and leveraging both data- and model-level parallelism.

We are excited about the advances that enable Alexa developers to build flexible and robust conversational experiences that allow customers to have natural interactions with their devices. Developers interested in learning more about the "how" of building these conversational experiences should read our accompanying developer blog.

For more information about the technical advances behind Alexa Conversations, at right are relevant publications related to our work in dialogue systems, dialogue state tracking, and data augmentation.

Acknowledgments: The entire Alexa Conversations team for making the innovations highlighted here possible.

About the Author
Angeliki Metallinou is an Alexa senior speech scientist.

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Project Kuiper is an initiative to launch a constellation of Low Earth Orbit satellites that will provide low-latency, high-speed broadband connectivity to underserved communities around the world. This position is part of the Satellite Attitude Determination and Control team. You will design and analyze the control system and algorithms, support development of our flight hardware and software, help integrate the satellite in our labs, participate in flight operations, and see a constellation of satellites flow through the production line in the building next door.A day in the lifeThis is an opportunity to play a significant role early in the design of an entirely new satellite system with challenging performance requirements. The large, integrated constellation brings opportunities for advanced capabilities that need investigation and development. The constellation size also puts emphasis on engineering excellence so our tools and methods, from conceptualization through manufacturing and all phases of test, will be state of the art as will the satellite and supporting infrastructure on the ground.About the hiring groupOur team has lots of experience with various satellite systems and many other flight vehicles. We have bench strength in both our mission and core GNC disciplines. We design, prototype, test, iterate and learn together. Because GNC is central to safe flight, we tend to drive Concepts of Operation and many system level analyses.You will find that Kuiper's mission is compelling, so our program is staffed with some of the top engineers in the industry. Our daily collaboration with other teams on the program brings constant opportunity for discovery, learning, and growth.Job responsibilitiesYou will design and analyze algorithms for estimation, flight control, and precise pointing using linear methods and simulation. Along with that will be plenty of work developing and applying models and simulations, with various levels of fidelity, of the satellite and our constellation. The labs in our building provide for component level environmental testing, functional and performance checkout, subsystem integration, and satellite integration up to Day in the Life testing and mission rehearsal. We are excited to get into space, so you can look forward to seeing your designs launch and fly soon!Export Control Requirement:Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum.Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.
US, WA, Seattle
We are a team of doers working passionately to apply cutting-edge advances in diagnostics technology to solve real-world problems. As a Senior Immunoassay Scientist, you will work with a unique and gifted team developing exciting medical diagnostic products and collaborate with cross-functional teams. Our team rewards intellectual curiosity while maintaining a laser-focus in bringing products to market. Competitive candidates are responsive, flexible, and able to succeed within an open, collaborative, entrepreneurial, startup-like environment. At the cutting edge of both academic and applied research in this product area, you have the opportunity to work together with some of the most talented scientists, engineers, and product managers.Inclusive Team Culture:Here at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.Work/Life BalanceOur team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.The Senior Immunoassay Scientist should be an expert in using immunoassays to develop assays leading to commercially viable products. They should have a deep understanding of underlying biochemical processes and components within conventional immunoassay platforms, be someone capable of diving deep into the data (requires in-depth understanding of the lab processes, for example to do root cause analysis of experimental outcome based on the data), and be someone who can work independently.Job responsibilities· Research and develop both existing and new cutting edge immunoassay technologies· Carefully execute laboratory experiments and provide guidance to teams of scientists and engineers through the writing of clear standard operating protocols.· Assist with assay process transfer to production environments and design and execute verification/validation plans· Work with a team of scientists and engineers to communicate experiments and conclusion for review· Work closely with clinical, regulatory and quality stakeholders· Follow literature in the field
US, WA, Bellevue
SCOT Network Topology Optimization science team focuses on research areas and tools that determine Amazon outbound transportation network design as we transition to relying on our internal carrier network and accelerate one-day delivery speed. There are various strategic questions the team is attempting to answer, such as: what is the impact of inventory placement on outbound transportation cost and delivery speed? What is the optimal transportation network design given processing capacity constraints? How can we forecast accurately fulfillment pattern for different customer clusters?. If you are interested in diving into a multi-discipline, high impact space this team is for you. So far, we utilized models from various science disciplines such as: Mixed Integer optimization, Random Forest (or other ML techniques), stochastic/probabilistic model, economic analysis, to name a few.In addition to transportation network, we also use forecasting and optimization techniques to evaluate new facilities recommendation for long term estimates, We use machine learning to approximate the network, and simulation of how our choices will perform. The team is a mixture of Software Engineers, Operations Research Scientists, Applied Scientists, Business Intelligence Engineers and Product Managers.We are looking for a Sr. Research Scientist who has a deep knowledge of analyzing large-scale fulfillment data using Machine learning and optimization. Those who are strong in forecasting space should have a breadth of other ML experience in a production environment using techniques. This role will focus on expanding our reach to analyze various fulfillment and transportation for Amazon's supply chain network worldwide.To help describe some of our challenges, we created a short video about Supply Chain Optimization at Amazon - http://bit.ly/amazon-scotAmazon is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation / Age
US, WA, Seattle
We are a team of doers working passionately to apply cutting-edge advances in diagnostics technology to solve real-world problems. As a Bioinformatics Scientist, you will work with a unique and gifted team developing exciting medical diagnostic products and collaborate with cross-functional teams. Our team rewards intellectual curiosity while maintaining a laser-focus in bringing products to market. Competitive candidates are responsive, flexible, and able to succeed within an open, collaborative, entrepreneurial, startup-like environment. At the cutting edge of both academic and applied research in this product area, you have the opportunity to work together with some of the most talented scientists, engineers, and product managers.Inclusive Team CultureHere at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.Work/Life BalanceOur team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded scientist and enable them to take on more complex tasks in the future.
US, WA, Seattle
EC2 Commercial Software Services is looking for an experienced Senior Data Scientist who can leverage the mountain of data our services produce in order to conduct statistical analysis, design machine learning models, and develop analytical solutions that answer increasingly complex business questions. As a Senior Data Scientist, you are considered a data science leader on your team. You take the lead on large projects and drive solutions to complex or ambiguous problems. You design solutions that help us take advantage of business opportunities and you are a key influencer of our engineering strategies using the data analysis you provide. You work efficiently and routinely deliver the right things with limited guidance. You help raise awareness of new and well-established data science techniques, and actively mentor and develop in your organization.As a Senior Data Scientist, you will discover and solve real world problems by analyzing large amounts of business data, defining new metrics and business cases, designing simulations and experiments, creating models, and collaborating with colleagues in business, software, and research. You will get the exciting opportunity to work on some of the world’s largest and diverse datasets. You will strive for simplicity, and demonstrate significant creativity and high judgment backed by statistical proof. As a Senior Data Scientist, you will be responsible for developing KPIs, cost savings analysis, ML models, and research in order to drive business solutions using computational techniques and statistics to support strategic and tactical decision-making. You will have significant experience performing large-scale data analysis and reporting, and will implement data analytics using cutting edge technologies that are inclusive of but not limited to various AWS products- Redshift, Lambda, Kinesis, Athena, and QuickSight. You should be expert at implementing and operating stable, scalable data flow solutions from production systems into end-user facing applications/reports.This position requires a solution-oriented candidate with a combination of deep business acumen, expert knowledge of statistics and algorithm development, and an analytical mindset. The candidate must have the ability to work with diverse customer groups to solve business problems and provide data solutions that are organized and simple to understand. This is a highly visible position that will interact at all levels of the business. Become part of this unique opportunity to make history defining the evolution of cloud computing.About Us:Inclusive Team CultureOur team is diverse! We drive towards an inclusive culture and work environment. We are intentional about attracting, developing, and retaining amazing talent from diverse backgrounds. Team members are active in Amazon’s 10+ affinity groups, sometimes known as employee resource groups, which bring employees together across businesses and locations around the world. These range from groups such as the Black Employee Network, Latinos at Amazon, Indigenous at Amazon, Families at Amazon, Amazon Women and Engineering, LGBTQ+, Warriors at Amazon (Military), Amazon People With Disabilities, and more.Work/Life BalanceOur team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives. This position involves on-call responsibilities, typically for one week every two months. We don’t like getting paged in the middle of the night or on the weekend, so we work to ensure that our systems are fault tolerant. When we do get paged, we work together to resolve the root cause so that we don’t get paged for the same issue twice.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.
US, WA, Seattle
We’re working on the future. If you are seeking an iterative fast-paced environment where you can drive innovation, apply state-of-the-art technologies to solve real world delivery challenges, and provide visible benefit to customers, this is your opportunity.Come work on the Amazon Prime Air Team!We are looking for an Applied Scientist II with expertise with geospatial data analysis and GIS technologies.As an Applied Scientist II, you will contribute to the Prime Air project by working with Systems and Software Engineers and Scientists, participate in the Science community at Amazon, and collaborate with academic researchers in the broader academic community. Within Prime Air our Science community values teamwork and supports continued learning. Furthermore, our builder culture means that Scientists and Software Development Engineers work closely together to invent and construct solutions that must work at scale.Export Control LicenseThis position may require a deemed export control license for compliance with applicable laws and regulations. Placement is contingent on Amazon’s ability to apply for and obtain an export control license on your behalf.RESPONSIBILITIES:· Prototype and implement new algorithms and concepts for geospatial data modeling and analysis.· · Perform studies to determine the impacts of new design concepts on overall system performance.· · Collaborate with product managers and engineering teams to design and implement software solutions for challenges.· · Drive collaborative research and creative problem solving.· · Constructively critique peer research and mentor junior scientists and engineers.· · Contribute to progress of the Amazon and broader research communities by producing publications.
US, CA, Sunnyvale
Interested in Amazon Alexa? We’re building the speech and language solutions behind Amazon products and services. Come join us!As a Senior Applied Scientist in Alexa AI, you will perform the duties of a research scientist and are also expected to be strong at implementing the algorithm. You are expected be an expert in machine learning approaches for conversational language and speech processing. Our mission is to innovate the state-of-the-art core technologies in these areas.Job responsibilities· Research, design, implement, analyze and evaluate novel algorithms for multimodal conversational AI using deep learning· Develop state-of-the-art algorithms, contribute to Amazon's Intellectual Property and publish at top-tier conferences· Collaborate closely with team members on developing algorithms and demonstrating them in systems including prototypes and productsAmazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.