AI tools let Alexa Prize participants focus on science

March 4 marks the kickoff of the third Alexa Prize Socialbot Grand Challenge, in which university teams build socialbots capable of conversing on a wide range of topics and make them available to millions of Alexa customers through the invitation “Alexa, let’s chat”. Student teams can begin applying to the competition on March 4, and in the subsequent six weeks, the Alexa Prize team will make a series of roadshow appearances at tech hubs in the U.S. and Europe to meet with students and answer questions about the program.

As we gear up for the third Alexa Prize Socialbot Grand Challenge, the Alexa science blog is reviewing some of the technical accomplishments from the second, which were reported in a paper released in late 2018. This post examines contributions by Amazon’s Alexa Prize team; a second post will examine innovations from the participating university teams.

To ensure that Alexa Prize contestants can concentrate on dialogue systems — the core technology of socialbots — Amazon scientists and engineers built a set of machine learning modules that handle fundamental conversational tasks and a development environment that lets contestants easily mix and match existing modules with those of their own design.

The Amazon team provided contestants with five primary tools:

  • An automatic-speech-recognition system, tailored to the broader vocabulary of “open-domain” conversations;
  • Contextual topic and dialogue act models, which identify topics of conversation and types of utterance, such as requests for information, clarifications, and instructions;
  • A sensitive-content detector;
  • A conversation evaluator model, which estimates how coherent and engaging responses generated by the contestants’ dialogue systems are; and
  • CoBot, a development environment that integrates tools from the Alexa Skills Kit, services from Amazon Web Services, and the Alexa Prize team’s models and automatically handles socialbot deployment.
CoBot_architecture.jpg._CB453492855_.jpg
The architecture of the CoBot development environment (see below)

Automatic speech recognition

Because open-domain conversations between Alexa customers and Alexa Prize socialbots can range across a wide variety of topics and named entities, the socialbots require their own general-purpose automatic speech recognizer. In the 2018 Alexa Prize, we reduced the speech recognizer’s error rate by 30% by training it on conversational data from earlier interactions with Alexa Prize socialbots.

In ongoing work, we are also modifying the speech recognizer’s statistical language model, which assigns a probability to the next word in a sentence given the previous ones. We have developed a machine-learning system that, on the fly, mixes different statistical language models based on conversational cues. In tests, we compared our system to one that does not use these cues and found that it reduces speech transcription errors by as much as 6%. It also improves the recognition of named entities such as people, locations, and businesses by up to 15%.

Contextual topic and dialogue act models

In earlier work, our team found that accurate topic tracking is strongly correlated with users’ subjective assessments of the quality of socialbot conversations. To provide Alexa Prize teams with accurate topic models, we developed a system that factors in the content of not only the target utterance but the five that immediately preceded it. It also considers the target utterance’s dialogue act classification. In our experiments, that additional information improved the accuracy of the topic classification by 35%.

Sensitive-content detection

Teams’ socialbots often draw content from forum-style websites that may include offensive remarks. To filter out such remarks, we first ranked more than 5,000 Internet forum conversations according to how frequently they featured any of the 800 prohibited words on our blacklist. Then we trained a machine learning system on data from the Toxic Comment Classification Challenge, which was labeled according to offensiveness. That system sampled comments from the highest- and lowest-ranked forum conversations, and comments that had high confidence scores for either offensiveness or inoffensiveness were added to a new training set. Once we had 10 million examples each of offensive and inoffensive text, we used them to train a new sensitive-content classifier.

Conversation evaluator

To train our conversation evaluator, we used 15,000 conversations that averaged 10 or 11 turns each. Annotators labeled socialbot responses in sequence, assigning them yes-or-no scores on five criteria: whether they were comprehensible, on topic, incorrect (which is easier to assess than correctness), and interesting and whether they pushed the conversation further. The inputs to the conversation evaluator include the current utterance and response, past utterances and responses, and a collection of natural-language-processing features, such as part of speech, topic, and dialogue act.

CoBot

With the launch of the 2018 Alexa Prize, we introduced the Conversational Bot Toolkit, or CoBot, a set of tools and interfaces that let the student teams concentrate more on science and less on questions of implementation, infrastructure, and scaling.

CoBot offers a single wrapper interface to three sets of tools: the Alexa Skills Kit, which lets third-party developers create new capabilities, or skills, for Alexa; a suite of cloud-computing services offered through Amazon Web Services; and the Alexa Prize Toolkit Service, which includes our natural-language-processing (NLP) models.

In our paper, we report that CoBot cut the time required to build the complex models and systems that power the contestants’ socialbots from months to weeks, and it cut the time required to address problems of scaling from one or two weeks to less than two days.

Data extracted by the NLP models — topics, named entities, dialogue acts, and so on — are stored in a very efficient DynamoDB database, offered through Amazon Web Services. The dialogue manager takes that data, as well as representations of current and past utterances and responses, and enacts a response selection strategy, which is one of the system components that the student teams provide.

For any given input utterance, the dialogue manager may generate candidate responses by querying databases, passing data to a machine learning model or a rule-based system, or all three. Candidate responses are passed to a ranker, also built by the student team, which selects a response to send to Alexa’s speech synthesizer.

CoBot enables easy A/B testing, so that the student team can compare two different models’ performance on the same inputs, and it also keeps logs of system performance, which can be used for debugging or model improvement. These testing capabilities are key to building complex socialbots that combine multiple machine learned models and knowledge sources to generate interesting and engaging responses.

About the Author
Anu Venkatesh is a technical program manager in the Alexa AI group.

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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're looking for an outstanding engineer who combines strong knowledge of aerodynamics with expertise in using CFD simulations to drive vehicle design. As a member of the high-fidelity aero team, you will have a direct hand in shaping our future drone designs and will interface closely with several other teams, including the conceptual design, wind tunnel, and controls teams.Responsibilities will include using CFD to aid in:· Vehicle-level conceptual and detailed design· Propeller design· Aerodynamic database generation· Wind tunnel support and validationDeveloping pre- and post-processing tooling is another key component to the role, so strong programming skills are encouraged.Export License ControlThis 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.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, VA, Arlington
Excited by using massive amounts of data to develop Machine Learning (ML) and Deep Learning (DL) models? Want to help public sector, medical center, and non-profit customers derive business value through the adoption of Artificial Intelligence (AI)? Eager to learn from many different enterprise’s use cases of AWS ML and DL? Thrilled to be key part of Amazon, who has been investing in Machine Learning for decades, pioneering and shaping the world’s AI technology?At Amazon Web Services (AWS), we are helping large enterprises build ML and DL models on the AWS Cloud. We are applying predictive technology to large volumes of data and against a wide spectrum of problems. Our Professional Services organization works together with our AWS customers to address their business needs using AI.AWS Professional Services is a unique consulting team. We pride ourselves on being customer obsessed and highly focused on the AI enablement of our customers. If you have experience with AI, including building ML or DL models, we’d like to have you join our team. You will get to work with an innovative company, with great teammates, and have a lot of fun helping our customers.If you do not live in a market where we have an open Data Scientist position, please feel free to apply. Our Data Scientists can live in any location where we have a WWPS Professional Service office.We’re looking for top architects, system and software engineers capable of using ML and other techniques to design, evangelize, and implement state-of-the-art solutions for never-before-solved problems.The primary responsibilities of this role are to:· Design data architectures and data lakes· Provide expertise in the development of ETL solutions on AWS· Use ML tools, such as Amazon SageMaker Ground Truth (GT) to annotate data. Work with Professional Services on designing workflow and user interface for GT annotation.· Collaborate with our data scientists to create scalable ML solutions for business problems· Interact with customer directly to understand the business problem, help and aid them in implementation of their ML ecosystem· Analyze and extract relevant information from large amounts of historical data — provide hands-on data wrangling expertise· Work closely with account team, research scientist teams and product engineering teams to drive model implementations and new algorithms· This position can have periods of up to 10% travel.
US
Are you passionate about building successful Data transformations within the Public Sector? At Amazon Web Services (AWS), we’re hiring highly technical Data engineers to collaborate with our customers and partners on key engagements. Our consultants will develop and deliver proof-of-concept projects, technical workshops, and support implementation projects. These professional services engagements will focus on customer solutions such as Data and Analytics, HPC and more.In this role, you will work with our partners, customers and focus on our AWS offerings such Amazon Kinesis, AWS Glue, Amazon Redshift, Amazon EMR, Amazon Athena and more. You will help our customers and partners to remove the constraints that prevent them from leveraging their data to develop business insights.AWS Professional Services engage in a wide variety of projects for customers and partners, providing collective experience from across the AWS customer base and are obsessed about customer success. Our team collaborates across the entire AWS organization to bring access to product and service teams, to get the right solution delivered and drive feature innovation based upon customer needs.You will also have the opportunity to create white papers, writing blogs, build demos and other reusable collateral that can be used by our customers. Most importantly, you will work closely with our Solution Architects, Data Architects and Service Engineering teams.The ideal candidate will have extensive experience with design, development and operations that leverages deep knowledge in the use of services like Amazon Kinesis, Apache Kafka, Apache Spark, Amazon EMR, NoSQL technologies and other 3rd parties.This is a customer facing role. You will be required to travel to client locations and deliver professional services when needed (expected travel time is 20%)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 we 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.
US, WA, Seattle
Would you like to shape the future of the video entertainment industry for movies, TV and live sports events? Does solving complex problems within large scale, production systems excite you? If you answered yes, we have an opportunity for you!Prime Video is disrupting the traditional television and movie industry with a growing library of high-quality media. Prime Video launched in 2007 and has quickly become a strategic priority for the company, reflected in the service’s recent expansion into over 240 countries and territories worldwide.This is a big opportunity to apply Computer Vision and directly impact millions of customers.A day in the lifeIn your day-to-day activities in this role, you'll embrace the challenges of a fast paced market and evolving technologies, and develop Computer Vision and Machine Learning models to extract deep 2/3-D video-understanding of Prime Video content. You will be encouraged to see the big picture, be innovative, and iteratively develop technology to impact millions of our customers. This is a young and evolving business where creativity and drive will have a lasting impact on the way video is enjoyed worldwide.About the hiring groupThe PV-CVML team is a group of Applied Scientists working on a diverse set of 2/3-D video understanding problems while partnering with various teams across Prime Video (PV). The most unique aspect of our team is the broad set of exciting problems we get to work on for our multiple stakeholders across the entire video-streaming vertical. If you want to work on technically cutting-edge problems with massive customer impact, then our team is the perfect fit for you!Job responsibilitiesAs a member of our team, you will apply Computer Vision and Machine Learning to problems that have cross-organizational technological impact. Your work will focus on cleansing and preparing large scale datasets, training and evaluating models and deploying them to production. You will work on large engineering efforts that solve significantly complex problems facing global customers. You will be trusted to operate with independence and are often assigned to focus on areas with significant impact on audience satisfaction. You must be equally comfortable with digging in to customer requirements as you are drilling into design with development teams.We would like you to build models that can perform 2D/3D scene-understanding of all video-content available on Prime Video using computer vision, natural language processing, deep learning and advanced machine learning algorithms. We need to solve problems across many cultures and languages and have a huge amount of human-labelled data as well as operations team to generate labels across many languages to help us achieve these goals. Our team consistently strives to innovate, and holds several novel patents and inventions in the motion picture and television industry. We are highly motivated to extend the state of the art.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.