NAACL: Industry track offers reality checks, new directions

Industry track chair and Amazon principal research scientist Rashmi Gangadharaiah on trends in industry papers and the challenges of building practical dialogue systems.

The annual meeting of the North American chapter of the Association for Computational Linguistics (NAACL) introduced an industry track in 2018, and at this year’s conference, which begins next week, one of the industry track chairs is Amazon principal research scientist Rashmi Gangadharaiah.

Rashmi Gangadharaiah.png
Rashmi Gangadharaiah, a principal research scientist at Amazon and an industry track chair at this year's meeting of the North American chapter of the Association for Computational Linguistics (NAACL).

“The NAACL industry track inspired industry tracks at other conferences such as COLING and EMNLP,” Gangadharaiah says. “The industry track provides a forum for researchers in the industry to exchange ideas and discuss successful deployments of ML [machine learning] and NLP [natural-language processing] technologies, as well as share challenges that arise in deploying such systems in real-world settings.”

For instance, Gangadharaiah explains, “academic research is often done in very controlled settings. It's not a negative thing: people have to do research, and it's useful to start in a controlled setting. But when we put such systems in real-world situations, we typically have to worry about latency, memory, and space. It's not always accuracy that we go for. It's a balance of latency, memory, space, and accuracy — and a question of how we measure accuracy. So I think it makes it more interesting that way.”

Similarly, Gangadharaiah explains, industry track papers often report negative results. “There are lots of papers that get published in academia, but when we try to put it in real-world settings, we notice that many of these methods don't work well,” she says. “So we do have papers on negative results. And it's crucial, because we do want to show that these are the methods that we tried, and they didn't work.”

The case for simplicity

Related content
Dataset contains more than 11,000 newly collected dialogues to aid research in open-domain conversation.

At Amazon, Gangadharaiah’s own research is on dialogue systems, and in her field, she says, a common reason that methods reported in academic papers prove impractical in real-world settings is that they require excessive hyperparameter optimization.

Hyperparameters are features of neural networks — such as the number of network layers, the number of nodes per layer, and the learning rate during training — whose variation can make a large difference in model performance. If the range of possible hyperparameter values is too great, and the range of values for which performance is good too narrow, hyperparameter optimization can prove prohibitively time consuming.

“In real-world applications, conversations can go really, really wild,” Gangadharaiah explains. “When you're trying to mimic the hyperparameters that are provided in academic settings, they usually don't work that well. So the best is to always go for a much simpler model. This is something that I have noticed: in industry, simple models perform way better, especially when you don't have to do so much tweaking of the models themselves.”

Hierarchical thinking

Of course, not all industry papers report negative results, and in some cases, Gangadharaiah says, industry research has pointed in directions where academic research has followed.

Again, her own research provides an example. The dialogue systems that Gangadharaiah works on are goal directed, meaning that the purpose of each dialogue is that an AI agent should identify and fulfill the goal of a human speaker. Such systems rely on natural-language-understanding models to make sense of customer utterances, but they also include state trackers that assess progress toward the speaker’s goal.

There is some need of semantic parsing in dialogue systems. ... I think the industry kind of motivated all that work.
Rashmi Gangadharaiah

“If you consider restaurant booking, you might say that you want to book a restaurant for six people, and then you might change your mind and say, ‘Hey no, now I want it for eight people,’” Gangadharaiah explains. “The system will have to make appropriate changes.

“We can introduce more complexity. So, for example, if you're ordering a pizza, maybe you would start with toppings of olives, and then you might go to pepperoni. In this case, you're not asking the system to replace olives with pepperoni; multiple values are being provided for the toppings itself.”

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

In natural-language understanding, categories such as “pizza topping” are often referred to as slots, and specific instances of the categories, such as “pepperoni” and “olives”, are referred to as slot values.

“We did have a few papers on why it makes sense to have some form of hierarchical structure to represent what the user really wants,” Gangadharaiah says. “So you have this high-level slot, which is called ‘toppings’, and under toppings, you have olives, pepperoni, and many other things. Above ‘toppings’, there might be a high-order intent like ‘pizza ordering’, and under ‘pizza ordering’, you would need ‘toppings’, but you also want to know the type of pizza, the size of the pizza, and so on.

“What this is saying is that there is some hierarchical representation, and there is some need of semantic parsing in dialogue systems. Some of these things have been pointed out in the industry, and now people are moving in that direction. So I think the industry kind of motivated all that work.”

Large language models

Recently, the big story in natural-language processing (NLP) has been the power and adaptability of large language models, such as BERT and GPT-3, that encode the probabilities of long sequences of words and can be fine-tuned on particular NLP tasks. They have applications in dialogue management, too, Gangadharaiah says.

“We’ve successfully deployed such models in Amazon,” she says, “and we’ve been actively exploring how to improve these models in order to make our chatbots — such as AWS Chatbot, LEX, and Alexa — more powerful. For example, I can take these large language models and then fine-tune them on, let's say, a restaurant domain, where I want to book certain seats in a certain restaurant for a certain number of people, and so on.

Related content
New method would enable BERT-based natural-language-processing models to handle longer text strings, run in resource-constrained settings — or sometimes both.

“I think the crucial part is the dialogue history. These models are still not perfect at handling dialogue history, and we still do not know the best strategy to handle dialogue history. Should I just send the model everything that was said in the previous turns? Or do I come up with a better representation — a state representation — to feed that as input? This is where it becomes really critical to explore more and see what works best for dialogue systems.”

Dialogue management systems have been in the headlines recently, with the commotion about an engineer who believed a chatbot had become sentient. But, Gangadharaiah says, “I consider goal-oriented dialogue as more complex because it has to be more than non-goal-oriented. Not only does the system have to be fluent and coherent, like non-goal-oriented systems, but it also has to interact with multiple databases in order to achieve the end goal, which could be making a reservation at a restaurant or booking a flight. And these could be skill commands, too. I guess people can argue both ways, but I think in general goal-oriented dialogue systems are more complex.”

Related content

US, MA, North Reading
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. The Research Science team at Amazon Robotics is seeking interns with a passion for robotic research to work on cutting edge algorithms for robotics. Our team works on challenging and high-impact projects, including allocating resources to complete a million orders a day, coordinating the motion of thousands of robots, autonomous navigation in warehouses, and learning how to grasp all the products Amazon sells. We are seeking internship candidates with backgrounds in computer vision, machine learning, resource allocation, discrete optimization, search, planning/scheduling, and reinforcement learning. As an intern you will develop a new algorithm to solve one of the challenging computer vision and manipulation problems in Amazon's robotic warehouses. Your project will fit your academic research experience and interests. You will code and test out your solutions in increasingly realistic scenarios and iterate on the idea with your mentor to find the best solution to the problem.
US, WA, Seattle
Are you excited about building high-performance robotic systems that can perceive, learn, and act intelligently alongside humans? The Robotics AI team is creating new science products and technologies that make this possible, at Amazon scale. We work at the intersection of computer vision, machine learning, robotic manipulation, navigation, and human-robot interaction.The Amazon Robotics team is seeking broad, curious applied scientists and engineering interns to join our diverse, full-stack team. In addition to designing, building, and delivering end-to-end robotic systems, our team is responsible for core infrastructure and tools that serve as the backbone of our robotic applications, enabling roboticists, applied scientists, software and hardware engineers to collaborate and deploy systems in the lab and in the field. Come join us!
US, VA, Arlington
The Central Science Team within Amazon’s People Experience and Technology org (PXTCS) uses economics, behavioral science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, well-being, and the value of work to Amazonians. We are an interdisciplinary team, which combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal. As Director for PXT Central Science Technology, you will be responsible for leading multiple teams through rapidly evolving complex demands and define, develop, deliver and execute on our science roadmap and vision. You will provide thought leadership to scientists and engineers to invent and implement scalable machine learning recommendations and data driven algorithms supporting flexible UI frameworks. You will manage and be responsible for delivering some of our most strategic technical initiatives. You will design, develop and operate new, highly scalable software systems that support Amazon’s efforts to be Earth’s Best Employer and have a significant impact on Amazon’s commitment to our employees and communities where we both serve and employ 1.3 million Amazonians. As Director of Applied Science, you will be part of the larger technical leadership community at Amazon. This community forms the backbone of the company, plays a critical role in the broad business planning, works closely with senior executives to develop business targets and resource requirements, influences our long-term technical and business strategy, helps hire and develop engineering leaders and developers, and ultimately enables us to deliver engineering innovations.This role is posted for Arlington, VA, but we are flexible on location at many of our offices in the US and Canada.
US, VA, Arlington
Employer: Amazon.com Services LLCPosition: Data Scientist IILocation: Arlington, VAMultiple Positions Available1. Manage and execute entire projects or components of large projects from start to finish including data gathering and manipulation, synthesis and modeling, problem solving, and communication of insights and recommendations.2. Oversee the development and implementation of data integration and analytic strategies to support population health initiatives.3. Leverage big data to explore and introduce areas of analytics and technologies.4. Analyze data to identify opportunities to impact populations.5. Perform advanced integrated comprehensive reporting, consultative, and analytical expertise to provide healthcare cost and utilization data and translate findings into actionable information for internal and external stakeholders.6. Oversee the collection of data, ensuring timelines are met, data is accurate and within established format.7. Act as a data and technical resource and escalation point for data issues, ensuring they are brought to resolution.8. Serve as the subject matter expert on health care benefits data modeling, system architecture, data governance, and business intelligence tools. #0000
US, TX, Dallas
Employer: Amazon.com Services LLCPosition: Data Scientist II (multiple positions available)Location: Dallas, TX Multiple Positions Available:1. Assist customers to deliver Machine Learning (ML) and Deep Learning (DL) projects from beginning to end, by aggregating data, exploring data, building and validating predictive models, and deploying completed models to deliver business impact to the organization;2. Apply understanding of the customer’s business need and guide them to a solution using AWS AI Services, AWS AI Platforms, AWS AI Frameworks, and AWS AI EC2 Instances;3. Use Deep Learning frameworks like MXNet, PyTorch, Caffe 2, Tensorflow, Theano, CNTK, and Keras to help our customers build DL models;4. Research, design, implement and evaluate novel computer vision algorithms and ML/DL algorithms;5. Work with data architects and engineers to analyze, extract, normalize, and label relevant data;6. Work with DevOps engineers to help customers operationalize models after they are built;7. Assist customers with identifying model drift and retraining models;8. Research and implement novel ML and DL approaches, including using FPGA;9. Develop computer vision and machine learning methods and algorithms to address real-world customer use-cases; and10. Design and run experiments, research new algorithms, and work closely with engineers to put algorithms and models into practice to help solve customers' most challenging problems.11. Approximately 15% domestic and international travel required.12. Telecommuting benefits are available.#0000
US, WA, Seattle
MULTIPLE POSITIONS AVAILABLECompany: AMAZON.COM SERVICES LLCPosition Title: Manager III, Data ScienceLocation: Bellevue, WashingtonPosition Responsibilities:Manage a team of data scientists working to build large-scale, technical solutions to increase effectiveness of Amazon Fulfillment systems. Define key business goals and map them to the success of technical solutions. Aggregate, analyze and model data from multiple sources to inform business decisions. Manage and quantify improvement in the customer experience resulting from research outcomes. Develop and manage a long-term research vision and portfolio of research initiatives, with algorithms and models that to be integrated in production systems. Hire and mentor junior scientists.Amazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation #0000
US, VA, Arlington
MULTIPLE POSITIONS AVAILABLECompany: AMAZON.COM SERVICES LLCPosition Title: Data Scientist IILocation: Arlington, VirginiaPosition Responsibilities:Design and implement scalable and reliable approaches to support or automate decision making throughout the business. Apply a range of data science techniques and tools combined with subject matter expertise to solve difficult business problems and cases in which the solution approach is unclear. Acquire data by building the necessary SQL / ETL queries. Import processes through various company specific interfaces for accessing Oracle, RedShift, and Spark storage systems. Build relationships with stakeholders and counterparts. Analyze data for trends and input validity by inspecting univariate distributions, exploring bivariate relationships, constructing appropriate transformations, and tracking down the source and meaning of anomalies. Build models using statistical modeling, mathematical modeling, econometric modeling, network modeling, social network modeling, natural language processing, machine learning algorithms, genetic algorithms, and neural networks. Validate models against alternative approaches, expected and observed outcome, and other business defined key performance indicators. Implement models that comply with evaluations of the computational demands, accuracy, and reliability of the relevant ETL processes at various stages of production.Amazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation #0000
US, WA, Seattle
Are you motivated to explore research in ambiguous spaces? Are you interested in conducting research that will improve the employee and manager experience at Amazon? Do you want to work on an interdisciplinary team of scientists that collaborate rather than compete? Join us at PXT Central Science!The People eXperience and Technology Central Science Team (PXTCS) uses economics, behavioral science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, wellbeing, and the value of work to Amazonians. We are an interdisciplinary team that combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal.We are seeking a senior Applied Scientist with expertise in more than one or more of the following areas: machine learning, natural language processing, computational linguistics, algorithmic fairness, statistical inference, causal modeling, reinforcement learning, Bayesian methods, predictive analytics, decision theory, recommender systems, deep learning, time series modeling. In this role, you will lead and support research efforts within all aspects of the employee lifecycle: from candidate identification to recruiting, to onboarding and talent management, to leadership and development, to finally retention and brand advocacy upon exit.The ideal candidate should have strong problem-solving skills, excellent business acumen, the ability to work independently and collaboratively, and have an expertise in both science and engineering. The ideal candidate is not methods-driven, but driven by the research question at hand; in other words, they will select the appropriate method for the problem, rather than searching for questions to answer with a preferred method. The candidate will need to navigate complex and ambiguous business challenges by asking the right questions, understanding what methodologies to employ, and communicating results to multiple audiences (e.g., technical peers, functional teams, business leaders).About the teamWe are a collegial and multidisciplinary team of researchers in People eXperience and Technology (PXT) that combines the talents of science and engineering to develop innovative solutions to make Amazon Earth's Best Employer. We leverage data and rigorous analysis to help Amazon attract, retain, and develop one of the world’s largest and most talented workforces.
US, WA, Bellevue
Job summaryThe Global Supply Chain-ACES organization aims to raise the bar on Amazon’s customer experience by delivering holistic solutions for Global Customer Fulfillment that facilitate the effective and efficient movement of product through our supply chain. We develop strategies, processes, material handling and technology solutions, reporting and other mechanisms, which are simple, technology enabled, globally scalable, and locally relevant. We achieve this through cross-functional partnerships, listening to the needs of our customers and prioritizing initiatives to deliver maximum impact across the value chain. Within the organization, our Quality team balances tactical operation with operations partners with global engagement on programs to deliver improved inventory accuracy in our network. The organization is looking for an experienced Principal Research Scientist to partner with senior leadership to develop long term strategic solutions. As a Principal Scientist, they will lead critical initiatives for Global Supply Chain, leveraging complex data analysis and visualization to:a. Collaborate with business teams to define data requirements and processes;b. Automate data pipelines;c. Design, develop, and maintain scalable (automated) reports and dashboards that track progress towards plans;d. Define, track and report program success metrics.e. Serve as a technical science lead on our most demanding, cross-functional projects.
US, MA, Cambridge
Job summaryMULTIPLE POSITIONS AVAILABLECompany: AMAZON.COM SERVICES LLCPosition Title: Data Scientist IILocation: Cambridge, MassachusettsPosition Responsibilities:Utilize code (Python, R, etc.) to build ML models to solve specific business problems. Build and measure novel online & offline metrics for personal digital assistants and customer scenarios, on diverse devices and endpoints. Research and implement novel machine learning algorithms and models. Collaborate with researchers, software developers, and business leaders to define product requirements and provide modeling solutions. Communicate verbally and in writing to business customers and leadership team with various levels of technical knowledge, educating them about our systems, as well as sharing insights and recommendations.Amazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation #0000