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.

Research areas

Related content

US, MA, North Reading
We are looking for experienced scientists and engineers to explore new ideas, invent new approaches, and develop new solutions in the areas of Controls, Dynamic modeling and System identification. Are you inspired by invention? Is problem solving through teamwork in your DNA? Do you like the idea of seeing how your work impacts the bigger picture? Answer yes to any of these and you’ll fit right in here at Amazon Robotics. We are a smart team of doers that work passionately to apply cutting edge advances in robotics and software to solve real-world challenges that will transform our customers’ experiences in ways we can’t even imagine yet. We invent new improvements every day. We are Amazon Robotics and we will give you the tools and support you need to invent with us in ways that are rewarding, fulfilling and fun. Key job responsibilities Applied Scientists take on big unanswered questions and guide development team to state-of-the-art solutions. We want to hear from you if you have deep industry experience in the Mechatronics domain and : * the ability to think big and conceive of new ideas and novel solutions; * the insight to correctly identify those worth exploring; * the hands-on skills to quickly develop proofs-of-concept; * the rigor to conduct careful experimental evaluations; * the discipline to fast-fail when data refutes theory; * and the fortitude to continue exploring until your solution is found We are open to hiring candidates to work out of one of the following locations: North Reading, MA, USA | Westborough, MA, USA
GB, London
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 or R 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 We are open to hiring candidates to work out of one of the following locations: London, GBR
DE, BE, Berlin
Are you excited about developing state-of-the-art computer vision models that revolutionize Amazon’s Fulfillment network? Are you looking for opportunities to apply AI on real-world problems at truly vast scale? At Amazon Fulfillment Technologies and Robotics, we are on a mission to build high-performance autonomous systems that perceive and act to further improve our world-class customer experience — at Amazon scale. To this end, we are looking for an Applied Scientist who will build and deploy models that make smarter decisions on a wide array of multi-modal signals. Together, we will be pushing beyond the state of the art in optimizing one of the most complex systems in the world: Amazon's Fulfillment Network. Key job responsibilities In this role, you will build computer vision and multi-modal deep learning models that understand the state of products and packages flowing through Amazon’s fulfillment network. You will build models that solve challenging problems like product identification and damage detection on Amazon's entire retail catalog (billions of different items, thousands of new items every day). You will primarily work with very large real-world vision datasets, as well as a diverse set of multi-modal datasets, including natural language and structured data. You will face a high level of research ambiguity and problems that require creative, ambitious, and inventive solutions. A day in the life AFT AI delivers the AI solutions that empower Amazon’s fulfillment network to make smarter decisions. You will work on an interdisciplinary team of scientists and engineers with deep expertise in developing cutting-edge AI solutions at scale. You will work with images, videos, natural language, and sequences of events from existing or new hardware. You will adapt state-of-the-art machine learning and computer vision techniques to develop solutions for business problems in the Amazon Fulfillment Network. About the team Amazon Fulfillment Technologies (AFT) powers Amazon’s global fulfillment network. We invent and deliver software, hardware, and science solutions that orchestrate processes, robots, machines, and people. We harmonize the physical and virtual world so Amazon customers can get what they want, when they want it. AFT AI is spread across multiple locations in NA (Bellevue WA and Nashville, TN) and Europe (Berlin, Germany). We are hiring candidates to work out of the Berlin location. Publicly available articles showcasing some of our work: - Damage Detection: - Product ID: We are open to hiring candidates to work out of one of the following locations: Berlin, BE, DEU
LU, Luxembourg
Have you ever wished to build high standard Operations Research and Machine Learning algorithms to optimize one of the most complex logistics network? Have you ever ordered a product on Amazon websites and wondered how it got delivered to you so fast, and what kinds of algorithms & processes are running behind the scenes to power the whole operation? If so, this role is for you. The team: Global transportation services, Research and applied science - Operations is at the heart of the Amazon customer experience. Each action we undertake is on behalf of our customers, as surpassing their expectations is our passion. We improve customer experience through continuously optimizing the complex movements of goods from vendors to customers throughout Europe. - Global transportation analytical teams are transversal centers of expertise, composed of engineers, analysts, scientists, technical program managers and developers. We are focused on Amazon most complex problems, processes and decisions. We work with fulfillment centers, transportation, software developers, finance and retail teams across the world, to improve our logistic infrastructure and algorithms. - GTS RAS is one of those Global transportation scientific team. We are obsessed by delivering state of the art OR and ML tools to support the rethinking of our advanced end-to-end supply chain. Our overall mission is simple: we want to implement the best logistics network, so Amazon can be the place where our customers can be delivered the next-day. The role: Applied scientist, speed and long term network design The person in this role will have end-to-end ownership on augmenting RAS Operation Research and Machine Learning modeling tools. They will help understand where are the constraints in our transportation network, and how we can remove them to make faster deliveries at a lower cost. You will be responsible for designing and implementing state-of-the-art algorithmic in transportation planning and network design, to expand the scope of our Operations Research and Machine Learning tools, to reflect the constantly evolving constraints in our network. You will enable the creation of a product that drives ever-greater automation, scalability and optimization of every aspect of transportation, planning the best network and modeling the constraints that prevent us from offering more speed to our customer, to maximize the utilization of the associated resources. The impact of your work will be in the Amazon EU global network. The product you will build will span across multiple organizations that play a role in Amazon’s operations and transportation and the shopping experience we deliver to customer. Those stakeholders include fulfilment operations and transportation teams; scientists and developers, and product managers. You will understand those teams constraints, to include them in your product; you will discuss with technical teams across the organization to understand the existing tools and assess the opportunity to integrate them in your product.You will engage with fellow scientists across the globe, to discuss the solutions they have implemented and share your peculiar expertise with them. This is a critical role and will require an aptitude for independent initiative and the ability to drive innovation in transportation planning and network design. Successful candidates should be able to design and implement high quality algorithm solutions, using state-of-the art Operations Research and Machine Learning techniques. Key job responsibilities - Engage with stakeholders to understand what prevents them to build a better transportation network for Amazon - Review literature to identify similar problems, or new solving techniques - Build the mathematical model representing your problem - Implement light version of the model, to gather early feed-back from your stakeholders and fellow scientists - Implement the final product, leveraging the highest development standards - Share your work in internal and external conferences - Train on the newest techniques available in your field, to ensure the team stays at the highest bar About the team GTS Research and Applied Science is a team of scientists and engineers whom mission is to build the best decision support tools for strategic decisions. We model and optimize Amazon end-to-end operations. The team is composed of enthusiastic members, that love to discuss any scientific problem, foster new ideas and think out of the box. We are eager to support each others and share our unique knowledge to our colleagues. We are open to hiring candidates to work out of one of the following locations: Luxembourg, LUX
US, CA, Santa Clara
Amazon AI is looking for world class scientists and engineers to join its AWS AI Labs. This group is entrusted with developing core data mining, natural language processing, deep learning, and machine learning algorithms for AWS. You will invent, implement, and deploy state of the art machine learning algorithms and systems. You will build prototypes and explore conceptually new solutions. You will interact closely with our customers and with the academic community. You will be at the heart of a growing and exciting focus area for AWS and work with other acclaimed engineers and world famous scientists. Inclusive Team Culture Here at AWS, 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. Work/Life Balance Our 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 Growth Our 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. We are open to hiring candidates to work out of one of the following locations: New York, NY, USA | Santa Clara, CA, USA | Seattle, WA, USA
IN, KA, Bengaluru
Job Description ATE (Analytics, Technology and Engineering) is a multi-disciplinary team of scientists, engineers, and technicians, all working to innovate in operations for the benefit of our customers. Our team is responsible for creating core analytics, science capabilities, platforms development and data engineering. We develop scalable analytics applications and research modeling to optimize operation processes.. You will work with professional software development managers, data engineers, data scientists, applied scientists, business intelligence engineers and product managers using rigorous quantitative approaches to ensure high quality data tech products for our customers around the world, including India, Australia, Brazil, Mexico, Singapore and Middle East. We are on the lookout for an enthusiastic and highly analytical individual to be a part of our journey. Amazon is growing rapidly and because we are driven by faster delivery to customers, a more efficient supply chain network, and lower cost of operations, our main focus is in the development of strategic models and automation tools fed by our massive amounts of available data. You will be responsible for building these models/tools that improve the economics of Amazon’s worldwide fulfillment networks in emerging countries as Amazon increases the speed and decreases the cost to deliver products to customers. You will identify and evaluate opportunities to reduce variable costs by improving fulfillment center processes, transportation operations and scheduling, and the execution to operational plans. You will also improve the efficiency of capital investment by helping the fulfillment centers to improve storage utilization and the effective use of automation. Finally, you will help create the metrics to quantify improvements to the fulfillment costs (e.g., transportation and labor costs) resulting from the application of these optimization models and tools. Major responsibilities include: · In this role, you will be responsible for developing and implementing innovative, scalable models and tools aimed at tackling novel challenges within Amazon’s global fulfillment network. Collaborating with fellow scientists from various teams, you will work on integrated solutions to enhance fulfillment speed, reduce costs. Your in-depth comprehension of business challenges will enable you to provide scientific analyses that underpin critical business decisions, utilizing a diverse range of methodologies. You’ll have the opportunity to design scientific tool platforms, deploy models, create efficient data pipelines, and streamline existing processes. Join us in shaping the future of Amazon’s global retail business by optimizing delivery speed at scale and making a lasting impact on the world of e-commerce. If you’re passionate about solving complex problems and driving innovation, we encourage you to apply. About the team This team is responsible for applying science based algo and techniques to solve the problems in operation and supply chain. Some of these problems include, volume forecasting, capacity planning, fraud detection, scenario simulation and using LLM/GenAI for process efficiency We are open to hiring candidates to work out of one of the following locations: Bengaluru, KA, IND
IL, Tel Aviv
Are you passionate about pushing the boundaries of computer vision, generative AI, deep learning, and machine learning? Ready to tackle challenges in document understanding at scale? We’re looking for innovative minds to join our world-class team at AWS, where you’ll collaborate with leading researchers, academics, and engineers on Amazon Textract. Why AWS? Be part of the leading cloud service provider powering innovation and positive impact. Work on real-world problems alongside tech and business giants. Access to unlimited data and computational resources. Collaborate with world-class researchers and developers. Deploy solutions at AWS scale and publish your work at top conferences. Focus Areas: - LLMs, document understanding, scene text recognition. - Visual question answering, NLP+vision, layout understanding. Locations: Tel Aviv and Haifa Think you’re a fit? Dive into the world of AWS Computer Vision and help us innovate at the forefront of technology. Key job responsibilities - Design cutting-edge neural network architectures. - Create document understanding solutions for complex scenarios and large visual datasets. - Set benchmarks and success criteria for model performance. - Collaborate across AWS and Amazon to bring scientific breakthroughs to our customers. - Add your unique creativity to our multidisciplinary team. - Mentor junior scientists and interns/PhD students. We are open to hiring candidates to work out of one of the following locations: Haifa, ISR | Tel Aviv, ISR
US, WA, Seattle
Innovators wanted! Are you an entrepreneur? A builder? A dreamer? This role is part of an Amazon Special Projects team that takes the company’s Think Big leadership principle to the extreme. We focus on creating entirely new products and services with a goal of positively impacting the lives of our customers. No industries or subject areas are out of bounds. If you’re interested in innovating at scale to address big challenges in the world, this is the team for you. Here at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. We have thirteen employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We are constantly learning through programs that are local, regional, and global. 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. As a Applied Scientist at the intersection of machine learning and the life sciences, you will participate in developing exciting products for customers. Our team rewards 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 a diverse and talented team of scientists, engineers, and product managers and collaborate with others teams. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
US, WA, Bellevue
As a Principal Research Scientist in the Amazon Artificial General Intelligence (AGI) Data Services organization, you will be responsible for sourcing and quality of massive datasets powering Amazon's AI. You will play a critical role in driving innovation and advancing the state-of-the-art in natural language processing and machine learning. You will be responsible for developing and implementing cutting-edge algorithms and techniques to extract valuable insights from large-scale data sources. You will work closely with cross-functional teams, including product managers, engineers, and data scientists to ensure that our AI systems are aligned with human policies and preferences. Key job responsibilities - Responsible for sourcing and quality of massive datasets powering Amazon's AI. - Collaborate with cross-functional teams to ensure that Amazon’s AI models are aligned with human preferences. - Develop and implement strategies to improve the efficiency and effectiveness of programs delivering massive datasets. - Identify and prioritize research opportunities that have the potential to significantly impact our AI systems. - Communicate research findings and progress to senior leadership and stakeholders. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA | Boston, MA, USA
US, WA, Redmond
Project Kuiper is an initiative to launch a constellation of Low Earth Orbit satellites that will provide low-latency, high-speed broadband connectivity to unserved and underserved communities around the world. We are searching for talented candidates with experience in spaceflight trajectory modeling and simulation, orbit mechanics, and launch vehicle mission planning. Key job responsibilities This position requires experience in simulation and analysis of astrodynamics models and spaceflight trajectories. Strong analysis skills are required to develop engineering studies of complex large-scale dynamical systems. This position requires demonstrated expertise in computational analysis automation and tool development. Working with the Kuiper engineering team, you will: - Develop modeling techniques for analysis and simulation of deployment dynamics of multiple satellites - Support Project Kuiper’s Launch Vehicle Mission Management team with technical expertise in Launch Vehicle trajectory requirements specification - Develop tools to support Mission Management planning for over 80 launches! - Work collaboratively with launch vehicle system technical teams 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. We are open to hiring candidates to work out of one of the following locations: Redmond, WA, USA