Amazon brings new machine learning, conversational-AI methods to AAAI

Papers investigate dialogue, question-answering, self-learning, and more.

Amazon is a gold sponsor of this year’s meeting of the Association for the Advancement of Artificial Intelligence (AAAI), and Amazon researchers are coauthors on eight papers accepted to the conference.

Of those papers, Dilek Hakkani-Tür, a senior principal scientist in the Alexa AI group whose research focuses on dialogue systems, is a coauthor on three.

Reza Ghanadan Alexa Prize
Reza Ghanadan, senior principal scientist for Alexa AI

“AAAI’s scope is very broad,” Hakkani-Tür says. “Everything related to AI, you can find papers. We have papers on three quite different topics. Two of them are, yes, on conversational systems — dialogue state tracking, question answering — but the last one is on making human-robot interaction more conversational. And since AAAI covers broad areas of AI, it’s still a good fit.”

Seokhwan Kim, a senior applied scientist in Hakkani-Tür’s group, will also be attending the conference, both as a co-presenter of the paper on human-robot interaction and as the chair of a workshop built around the Eighth Dialog System Technology Challenge, a competition in which industry and academic teams build systems to address outstanding problems in the dialogue systems field. Now in its eighth year, the competition workshop has for the past three years been held at AAAI.

“That also improves AAAI’s attraction for people like me who work on dialogue,” Hakkani-Tür says. At the workshop, Hakkani-Tür says, Alexa researchers will be among the attendees proposing tasks for inclusion in next year’s challenge.

Also at AAAI, Krishnaram Kenthapadi, a principal scientist with Amazon Web Services (AWS), will co-lead a tutorial on explainable AI. Explainable AI, Kenthapadi says, is “an emerging discipline that aims at making the reasoning of artificial-intelligence systems intelligible to humans, thereby enhancing the transparency and trustworthiness of such systems.”

Amazon researchers’ AAAI papers include the following:

State tracking example
A sample dialogue and the corresponding updates performed by a dialogue state tracker (DST).

MA-DST: Multi-Attention-Based Scalable Dialog State Tracking
Adarsh Kumar, Peter Ku, Angeliki Metallinou, Anuj Goyal, Dilek Hakkani-Tür

Dialogue systems often need to remember contextual information from early in a conversation, and they can benefit from having a specific component that tracks the “state” of the conversation — the entities mentioned so far. When a customer asks, “Are there any Indian restaurants nearby?”, for instance, the state tracker updates the cuisine variable to “Indian” and remembers that “nearby” refers to the location of the movie theater mentioned two turns ago. Attention mechanisms are a powerful tool for tracking state because they help determine which words in the conversation history are relevant to interpreting the current turn. The researchers use attention at three different layers of granularity to improve the state-of-the-art state tracking accuracy by 5%.

MMM: Multi-stage Multi-task Learning for Multi-choice Reading Comprehension
Di Jin, Shuyang Gao, Jiun-yu Kao, Tagyoung Chung, Dilek Hakkani-Tür

Teaching machine learning systems to answer multiple-choice questions has been difficult, mostly because of the lack of training data. The researchers show that powerful new neural language models, such as BERT, can be adapted to answer multiple-choice questions if the adaptation proceeds in two stages: first, a “coarse tuning” on large natural-language-inference data sets, which encode the logical relationships between pairs of sentences; then, fine-tuning on a domain-specific set of questions and answers. In tests, this method improved the state-of-the-art accuracy on several benchmark data sets by 16%.

Just Ask: An Interactive Learning Framework for Vision and Language Navigation
Ta-Chung Chi, Mihail Eric, Seokhwan Kim, Minmin Shen, Dilek Hakkani-Tür

The researchers show how they use reinforcement learning to train a robot to ask for help when it has difficulty interpreting spoken instructions. When uncertain how to proceed, the robot would send a help request to its operator, who could provide clarification through some combination of verbal and visual feedback. In simulations, the researchers found that a single help request during each navigation task increased the chances of successfully completing the task by 15%.

Feedback-Based Self-Learning in Large-Scale Conversational AI Agents
Pragaash Ponnusamy, Alireza Roshan-Ghias, Chenlei Guo, Ruhi Sarikaya

Alexa customers sometimes cut Alexa off and rephrase requests that don’t elicit the responses they want. The researchers describe how to use implicit signals like these to automatically improve Alexa’s natural-language-understanding models, without the need for human intervention. The key is to model sequences of requests as “absorbing Markov chains”, which describe probabilistic transitions from one request to another and can be used to calculate the likelihood that rewriting a request will result in a better outcome. In tests, the system demonstrated a win/loss ratio of 12, meaning that it learned 12 valid substitutions for every one invalid one. Blog post here.

Knowledge distillation from internal representations
An example of the type of internal representations that a "student" model attempts to duplicate in Amazon researchers' new machine learning scheme.

Knowledge Distillation from Internal Representations
Gustavo Aguilar, Yuan Ling, Yu Zhang, Benjamin Yao, Xing Fan, Chenlei Guo

Knowledge distillation is a technique in which a lean, efficient machine learning model is trained to reproduce the outputs of a larger, slower, but more powerful “teacher” model. The more complex the teacher, however, the more difficult its outputs are to reproduce faithfully. The researchers address this limitation by training the “student” model to mimic not only the teacher’s outputs but also the internal states it assumes in producing those outputs. In experiments involving a BERT model, the researchers’ method demonstrated a 5-10% performance improvement on benchmark data sets, including a large new Reddit data set that the authors assembled and have made available for research purposes.

TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection
Siddhant Garg, Thuy Vu, Alessandro Moschitti

Answer sentence selection is the process of choosing one sentence among many candidates to serve as the answer to a query. The researchers describe a new technique for adapting powerful language models built atop the Transformer neural network to the problem of answer selection. They first fine-tune the language model on a large, general data set of question-answer pairs, then fine-tune it again on a much smaller, topic-specific data set. In tests on standard benchmarks in the field, they reduce the state-of-the-art error rate by 50%. Blog post here.

Hierarchical attention masking
An example of how attention masking can encode the hierarchical structure of a conversation.

Who Did They Respond To? Conversation Structure Modeling Using Masked Hierarchical Transformer
Henghui Zhu, Feng Nan, Zhiguo Wang, Ramesh Nallapati, Bing Xiang

On online discussion boards or in meeting transcriptions, it can be hard to tell who is responding to which comment by whom. The researchers describe a new method for training machine learning models to determine the “reply-to” structure of such conversations. They use an attention mechanism, which determines how much weight to give prior comments when identifying the antecedent of the current comment. But they enforce a hierarchical conversational structure by “masking” — or setting to zero — the attention weights on comments that are not ancestors of a given candidate antecedent. In tests that involved inferring conversation structure, their method yielded significant accuracy improvements over the previous state-of-the-art system on multiple data sets.

At the conference’s Workshop on Interactive and Conversational Recommendation Systems, Alexa researchers will also present a paper titled “What Do You Mean I’m Funny? Personalizing the Joke Skill of a Voice-Controlled Virtual Assistant”. To automatically label the training data fed to a joke skill’s machine learning models, the researchers experimented with two implicit signals of joke satisfaction: customer requests for additional jokes either within 5 minutes or between 1 and 25 hours after the initial joke. Models trained on both types of implicit signals outperformed existing baseline models.

Research areas

Related content

US, MA, Boston
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Applied Scientist with a strong deep learning background, to build industry-leading technology with Large Language Models (LLMs) and multi-modal systems. You will support projects that work on technologies including multi-modal model alignment, moderation systems and evaluation. Key job responsibilities As an Applied Scientist with the AGI team, you will support the development of novel algorithms and modeling techniques, to advance the state of the art with LLMs. Your work will directly impact our customers in the form of products and services that make use of speech and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in generative artificial intelligence (GenAI). You are also expected to publish in top tier conferences. About the team The AGI team has a mission to push the envelope in LLMs and multimodal systems. Specifically, we focus on model alignment with an aim to maintain safety while not denting utility, in order to provide the best-possible experience for our customers.
IN, HR, Gurugram
Our customers have immense faith in our ability to deliver packages timely and as expected. A well planned network seamlessly scales to handle millions of package movements a day. It has monitoring mechanisms that detect failures before they even happen (such as predicting network congestion, operations breakdown), and perform proactive corrective actions. When failures do happen, it has inbuilt redundancies to mitigate impact (such as determine other routes or service providers that can handle the extra load), and avoids relying on single points of failure (service provider, node, or arc). Finally, it is cost optimal, so that customers can be passed the benefit from an efficiently set up network. Amazon Shipping is hiring Applied Scientists to help improve our ability to plan and execute package movements. As an Applied Scientist in Amazon Shipping, you will work on multiple challenging machine learning problems spread across a wide spectrum of business problems. You will build ML models to help our transportation cost auditing platforms effectively audit off-manifest (discrepancies between planned and actual shipping cost). You will build models to improve the quality of financial and planning data by accurately predicting ship cost at a package level. Your models will help forecast the packages required to be pick from shipper warehouses to reduce First Mile shipping cost. Using signals from within the transportation network (such as network load, and velocity of movements derived from package scan events) and outside (such as weather signals), you will build models that predict delivery delay for every package. These models will help improve buyer experience by triggering early corrective actions, and generating proactive customer notifications. Your role will require you to demonstrate Think Big and Invent and Simplify, by refining and translating Transportation domain-related business problems into one or more Machine Learning problems. You will use techniques from a wide array of machine learning paradigms, such as supervised, unsupervised, semi-supervised and reinforcement learning. Your model choices will include, but not be limited to, linear/logistic models, tree based models, deep learning models, ensemble models, and Q-learning models. You will use techniques such as LIME and SHAP to make your models interpretable for your customers. You will employ a family of reusable modelling solutions to ensure that your ML solution scales across multiple regions (such as North America, Europe, Asia) and package movement types (such as small parcel movements and truck movements). You will partner with Applied Scientists and Research Scientists from other teams in US and India working on related business domains. Your models are expected to be of production quality, and will be directly used in production services. You will work as part of a diverse data science and engineering team comprising of other Applied Scientists, Software Development Engineers and Business Intelligence Engineers. You will participate in the Amazon ML community by authoring scientific papers and submitting them to Machine Learning conferences. You will mentor Applied Scientists and Software Development Engineers having a strong interest in ML. You will also be called upon to provide ML consultation outside your team for other problem statements. If you are excited by this charter, come join us!
US, WA, Seattle
Do you want to re-invent how millions of people consume video content on their TVs, Tablets and Alexa? We are building a free to watch streaming service called Fire TV Channels (https://techcrunch.com/2023/08/21/amazon-launches-fire-tv-channels-app-400-fast-channels/). Our goal is to provide customers with a delightful and personalized experience for consuming content across News, Sports, Cooking, Gaming, Entertainment, Lifestyle and more. You will work closely with engineering and product stakeholders to realize our ambitious product vision. You will get to work with Generative AI and other state of the art technologies to help build personalization and recommendation solutions from the ground up. You will be in the driver's seat to present customers with content they will love. Using Amazon’s large-scale computing resources, you will ask research questions about customer behavior, build state-of-the-art models to generate recommendations and run these models to enhance the customer experience. You will participate in the Amazon ML community and mentor Applied Scientists and Software Engineers with a strong interest in and knowledge of ML. Your work will directly benefit customers and you will measure the impact using scientific tools.
US, MA, Boston
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Senior Applied Scientist with a strong deep learning background, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As a Senior Applied Scientist with the AGI team, you will work with talented peers to lead the development of novel algorithms and modeling techniques, to advance the state of the art with LLMs. Your work will directly impact our customers in the form of products and services that make use of speech and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in generative artificial intelligence (GenAI). About the team The AGI team has a mission to push the envelope in LLMs and multimodal systems, in order to provide the best-possible experience for our customers.
IN, KA, Bengaluru
The Amazon Alexa AI team in India is seeking a talented, self-driven Applied Scientist to work on prototyping, optimizing, and deploying ML algorithms within the realm of Generative AI. Key responsibilities include: - Research, experiment and build Proof Of Concepts advancing the state of the art in AI & ML for GenAI. - Collaborate with cross-functional teams to architect and execute technically rigorous AI projects. - Thrive in dynamic environments, adapting quickly to evolving technical requirements and deadlines. - Engage in effective technical communication (written & spoken) with coordination across teams. - Conduct thorough documentation of algorithms, methodologies, and findings for transparency and reproducibility. - Publish research papers in internal and external venues of repute - Support on-call activities for critical issues Basic Qualifications: - Master’s or PhD in computer science, statistics or a related field - 2-7 years experience in deep learning, machine learning, and data science. - Proficiency in coding and software development, with a strong focus on machine learning frameworks. - Experience in Python, or another language; command line usage; familiarity with Linux and AWS ecosystems. - Understanding of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc. - Excellent communication skills (written & spoken) and ability to collaborate effectively in a distributed, cross-functional team setting. - Papers published in AI/ML venues of repute Preferred Qualifications: - Track record of diving into data to discover hidden patterns and conducting error/deviation analysis - Ability to develop experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relations - The motivation to achieve results in a fast-paced environment. - Exceptional level of organization and strong attention to detail - Comfortable working in a fast paced, highly collaborative, dynamic work environment
IN, KA, Bengaluru
Amazon is investing heavily in building a world class advertising business and we are responsible for defining and delivering a collection of self-service performance advertising products that drive discovery and sales. Our products are strategically important to our Retail and Marketplace businesses driving long term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products. We are highly motivated, collaborative and fun-loving with an entrepreneurial spirit and bias for action. With a broad mandate to experiment and innovate, we are growing at an unprecedented rate with a seemingly endless range of new opportunities. The ATT team, based in Bangalore, is responsible for ensuring that ads are relevant and is of good quality, leading to higher conversion for the sellers and providing a great experience for the customers. We deal with one of the world’s largest product catalog, handle billions of requests a day with plans to grow it by order of magnitude and use automated systems to validate tens of millions of offers submitted by thousands of merchants in multiple countries and languages. In this role, you will build and develop ML models to address content understanding problems in Ads. These models will rely on a variety of visual and textual features requiring expertise in both domains. These models need to scale to multiple languages and countries. You will collaborate with engineers and other scientists to build, train and deploy these models. As part of these activities, you will develop production level code that enables moderation of millions of ads submitted each day.
US, WA, Seattle
The Search Supply & Experiences team, within Sponsored Products, is seeking an Applied Scientist to solve challenging problems in natural language understanding, personalization, and other areas using the latest techniques in machine learning. In our team, you will have the opportunity to create new ads experiences that elevate the shopping experience for our hundreds of millions customers worldwide. As an Applied Scientist, you will partner with other talented scientists and engineers to design, train, test, and deploy machine learning models. You will be responsible for translating business and engineering requirements into deliverables, and performing detailed experiment analysis to determine how shoppers and advertisers are responding to your changes. We are looking for candidates who thrive in an exciting, fast-paced environment and who have a strong personal interest in learning, researching, and creating new technologies with high customer impact. Key job responsibilities As an Applied Scientist on the Search Supply & Experiences team you will: - Perform hands-on analysis and modeling of enormous datasets to develop insights that increase traffic monetization and merchandise sales, without compromising the shopper experience. - Drive end-to-end machine learning projects that have a high degree of ambiguity, scale, and complexity. - Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing your ML models. - Design and run experiments, gather data, and perform statistical analysis. - Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving. - Stay up to date on the latest advances in machine learning. About the team We are a customer-obsessed team of engineers, technologists, product leaders, and scientists. We are focused on continuous exploration of contexts and creatives where advertising delivers value to shoppers and advertisers. We specifically work on new ads experiences globally with the goal of helping shoppers make the most informed purchase decision. We obsess about our customers and we are continuously innovating on their behalf to enrich their shopping experience on Amazon
US, WA, Seattle
Amazon.com strives to be Earth's most customer-centric company where customers can shop in our stores to find and discover anything they want to buy. We hire the world's brightest minds, offering them a fast paced, technologically sophisticated and friendly work environment. Economists at Amazon partner closely with senior management, business stakeholders, scientist and engineers, and economist leadership to solve key business problems ranging from Amazon Web Services, Kindle, Prime, inventory planning, international retail, third party merchants, search, pricing, labor and employment planning, effective benefits (health, retirement, etc.) and beyond. Amazon Economists build econometric models using our world class data systems and apply approaches from a variety of skillsets – applied macro/time series, applied micro, econometric theory, empirical IO, empirical health, labor, public economics and related fields are all highly valued skillsets at Amazon. You will work in a fast moving environment to solve business problems as a member of either a cross-functional team embedded within a business unit or a central science and economics organization. You will be expected to develop techniques that apply econometrics to large data sets, address quantitative problems, and contribute to the design of automated systems around the company. About the team The International Seller Services (ISS) Economics team is a dynamic group at the forefront of shaping Amazon's global seller ecosystem. As part of ISS, we drive innovation and growth through sophisticated economic analysis and data-driven insights. Our mission is critical: we're transforming how Amazon empowers millions of international sellers to succeed in the digital marketplace. Our team stands at the intersection of innovative technology and practical business solutions. We're leading Amazon's transformation in seller services through work with Large Language Models (LLMs) and generative AI, while tackling fundamental questions about seller growth, marketplace dynamics, and operational efficiency. What sets us apart is our unique blend of rigorous economic methodology and practical business impact. We're not just analyzing data – we're building the frameworks and measurement systems that will define the future of Amazon's seller services. Whether we're optimizing the seller journey, evaluating new technologies, or designing innovative service models, our team transforms complex economic challenges into actionable insights that drive real-world results. Join us in shaping how millions of businesses worldwide succeed on Amazon's marketplace, while working on problems that combine economic theory, advanced analytics, and innovative technology.
US, WA, Seattle
Have you ever wondered how Amazon launches and maintains a consistent customer experience across hundreds of countries and languages it serves its customers? Are you passionate about data and mathematics, and hope to impact the experience of millions of customers? Are you obsessed with designing simple algorithmic solutions to very challenging problems? If so, we look forward to hearing from you! At Amazon, we strive to be Earth's most customer-centric company, where both internal and external customers can find and discover anything they want in their own language of preference. Our Translations Services (TS) team plays a pivotal role in expanding the reach of our marketplace worldwide and enables thousands of developers and other stakeholders (Product Managers, Program Managers, Linguists) in developing locale specific solutions. Amazon Translations Services (TS) is seeking an Applied Scientist to be based in our Seattle office. As a key member of the Science and Engineering team of TS, this person will be responsible for designing algorithmic solutions based on data and mathematics for translating billions of words annually across 130+ and expanding set of locales. The successful applicant will ensure that there is minimal human touch involved in any language translation and accurate translated text is available to our worldwide customers in a streamlined and optimized manner. With access to vast amounts of data, cutting-edge technology, and a diverse community of talented individuals, you will have the opportunity to make a meaningful impact on the way customers and stakeholders engage with Amazon and our platform worldwide. Together, we will drive innovation, solve complex problems, and shape the future of e-commerce. Key job responsibilities * Apply your expertise in LLM models to design, develop, and implement scalable machine learning solutions that address complex language translation-related challenges in the eCommerce space. * Collaborate with cross-functional teams, including software engineers, data scientists, and product managers, to define project requirements, establish success metrics, and deliver high-quality solutions. * Conduct thorough data analysis to gain insights, identify patterns, and drive actionable recommendations that enhance seller performance and customer experiences across various international marketplaces. * Continuously explore and evaluate state-of-the-art modeling techniques and methodologies to improve the accuracy and efficiency of language translation-related systems. * Communicate complex technical concepts effectively to both technical and non-technical stakeholders, providing clear explanations and guidance on proposed solutions and their potential impact. About the team We are a start-up mindset team. As the long-term technical strategy is still taking shape, there is a lot of opportunity for this fresh Science team to innovate by leveraging Gen AI technoligies to build scalable solutions from scratch. Our Vision: Language will not stand in the way of anyone on earth using Amazon products and services. Our Mission: We are the enablers and guardians of translation for Amazon's customers. We do this by offering hands-off-the-wheel service to all Amazon teams, optimizing translation quality and speed at the lowest cost possible.
US, CA, Santa Clara
Amazon Q Business is an AI assistant powered by generative technology. It provides capabilities such as answering queries, summarizing information, generating content, and executing tasks based on enterprise data. We are seeking a Language Data Scientist II to join our data team. Our mission is to engineer high-quality datasets that are essential to the success of Amazon Q Business. From human evaluations and Responsible AI safeguards to Retrieval-Augmented Generation and beyond, our work ensures that Generative AI is enterprise-ready, safe, and effective for users. As part of our diverse team—including language engineers, linguists, data scientists, data engineers, and program managers—you will collaborate closely with science, engineering, and product teams. We are driven by customer obsession and a commitment to excellence. In this role, you will leverage data-centric AI principles to assess the impact of data on model performance and the broader machine learning pipeline. You will apply Generative AI techniques to evaluate how well our data represents human language and conduct experiments to measure downstream interactions. Key job responsibilities * oversee end-to-end evaluation data pipeline and propose evaluation metrics and methods * incorporate your knowledge of linguistic fundamentals, NLU, NLP to the data pipeline * process and analyze diverse media formats including audio recordings, video, images and text * perform statistical analysis of the data * write intuitive data generation & annotation guidelines * write advanced and nuanced prompts to optimize LLM outputs * write python scripts for data wrangling * automate repetitive workflows and improve existing processes * perform background research and vet available public datasets on topics such as long text retrieval, text generation, summarization, question-answering, and reasoning * leverage and integrate AWS services to optimize data collection workflows * collaborate with scientists, engineers, and product managers in defining data quality metrics and guidelines. * lead dive deep sessions with data annotators About the team About AWS Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture AWS values curiosity and connection. Our employee-led and company-sponsored affinity groups promote inclusion and empower our people to take pride in what makes us unique. Our inclusion events foster stronger, more collaborative teams. Our continual innovation is fueled by the bold ideas, fresh perspectives, and passionate voices our teams bring to everything we do. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve.