Ensuring that new language-processing models don't backslide

New approach corrects for cases when average improvements are accompanied by specific regressions.

The models behind machine learning (ML) services are continuously being updated, and the new models are usually more accurate than the old ones. But an overall improvement in accuracy can still be accompanied by regression — a loss of accuracy — in particular cases.

This can be frustrating for users, especially if a given regression has downstream consequences. A virtual conversational agent, for example, might regress on a user request early in a dialogue, which disrupts the ensuing conversation. 

regression_example.png
After an update, even if the overall accuracy improves, an agent may start to make incorrect predictions (red) early in a conversation, leading to a frustrating user experience.

In a paper we’re presenting at this year’s meeting of the Association for Computational Linguistics (ACL), we describe a new approach to regression-free model updating in natural-language processing (NLP), which enables us to build new deep neural models that not only perform better in accuracy but consistently preserve legacy models’ correct classifications. 

The paper has two parts: a study of model update regression and a proposal for mitigating it. In the study, we use public benchmark models based on the BERT language model and train them on the seven different NLP tasks of the General Language Understanding Evaluation (GLUE) framework. Then we train updated models using either different model parameters or a more powerful BERT model. We find that regression occurs on 1.9% to 7.6% of input cases, even though overall performance improves after retraining.

To mitigate regression, we formulate the problem of matching past performance as a constrained optimization problem, then relax the problem to be approximated via knowledge distillation, which encourages the new model to imitate the old one in the right context.

Our research is part of Amazon Web Services’ (AWS’s) recent work on “graceful AI”, machine learning systems that are not just accurate but also more transparent, more interpretable, and more compatible with their predecessors. We believe that regression-minimized model updating is a critical building block for successful ML services that are continuously improving and evolving gracefully.

Regression bugs are in your NLP model!

In our study, we measure model update regression by negative flip rate (NFR), or the percentage of cases in which the old classifier predicts correctly but the new classifier predicts incorrectly. For services with tens of millions of users, the types of NFRs we measure would translate to poor experiences for hundreds of thousands of users. When regression occurs at that scale, it often requires extensive, time-consuming error analysis and model patching.

Postive-Negative-FlipGrid.png
Updating from old to new models may correct mistakes (positive-flip cases, blue) but introduce new ones as well (negative-flip cases, red). Examples are from a paraphrase classification task.
Credit: Glynis Condon

Our study showed that in updated models, NFRs are often much higher than the total accuracy gains, from two to eight times as high. This implies that simply aiming for greater accuracy improvements in updated models will not ensure a decrease in regression; i.e., improving accuracy and minimizing regression are related but separate learning targets.

Finally, we also found that minor changes, such as using different random seeds (constants that introduce randomness into the training process) can cause significant variation in regression rate, a consideration that any mitigation strategy will need to account for.

How to mitigate regressions

Regression-free model updating requires a model to both learn the target task and comply with conditions posed by the old model, making it a constrained optimization problem. We relax the hard constraint into a soft inequality condition and propose a proxy to replace NFR: a continuous measure that uses Kullback-Leibler divergence — a standard similarity measure — over prediction logits, or the unnormalized outputs of both the old and new models. We can thus approximate the constrained optimization problem as optimizing a joint objective of classification loss and knowledge distillation penalty.

In evaluating our approach, we used two baselines. One was a model updated in the traditional way, without any attempt to control regression. The other was an ensemble that included both the original model and the updated model; the ensemble’s final classification was a combination of both models’ outputs. 

Our results show that when updating involved changing language models — switching from BERT-base to BERT-large, for instance — our knowledge distillation approach was the most effective, cutting average NFR to 2.91%, versus 3.63% for the ensemble model and 4.57% for a conventional update. At the same time, our model was slightly more accurate than both baselines.

We also evaluated our models using the CheckList protocol, which assesses an NLP model’s performance using different classes of input data, designed to elicit different types behavior. We found that distillation can effectively reduce regressions across almost all types of behavioral tests, implying that our distillation method is actually aligning the new model’s behavior with the old model, rather than using short cuts in a few special cases. 

When updating involved different random seeds, without a change of language model, the ensemble method worked better than ours, which was a surprise. This is possibly because ensembles naturally reduce output variance, making them less prone to overfitting, which could reduce regressions. 

Given the results of our initial study, we hypothesized that single-model variance could be a function of the choice of random seeds. So we designed a simple model selection procedure in which we train 20 different models using 20 random seeds and pick out the one that offers the greatest NFR reduction. We found that in the cases in which updates preserve the same language model, this approach reduces regression as well as ensemble methods do, without the added operational overhead of running two models in parallel.

At AWS AI, we are committed to continuing to explore innovative solutions to this problem and to ensure that customers can always enjoy state-of-the-art technologies without painful transitions. We hope our work will inspire the AI community to develop more advanced methods and build easily maintainable, ever-improving systems.

Related content

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, IL, Chicago
MULTIPLE POSITIONS AVAILABLECompany: AMAZON.COM SERVICES LLCPosition Title: Data Scientist ILocation: Chicago, IllinoisPosition Responsibilities:Build the core intelligence, insights, and algorithms that support the real estate acquisition strategies for Amazon physical stores. Tackle cutting-edge, complex problems such as predicting the optimal location for new Amazon stores by bringing together numerous data assets, and using best-in-class modeling solutions to extract the most information out of them. Work with business stakeholders, software development engineers, and other data scientists across multiple teams to develop innovative solutions at massive scale.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