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Punta Cana
November 16 - 20, 2020
Punta Cana, Dominican Republic
Conferences & Events
EMNLP 2020

Overview

Amazon is proud to be a Diamond sponsor of EMNLP 2020.

About EMNLP: The 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) will be held virtually Nov. 16-20. Learn more about Amazon's involvement in EMNLP below.

Conference website

Amazon program organizing committee

  • Yang Liu
    Yang Liu
    Principal Applied Scientist
    Program Chair
  • Markus Dreyer
    Markus Dreyer
    Senior Area Chair, Question Answering
  • alessandro_moschitti.jpg
    Alessandro Moschitti
    Principal Scientist
    Senior Area Chair, Information Retrieval and Text Mining
  • Bill_Byrne.jpg
    Bill Byrne
    Amazon Scholar
    Area Chair, Machine Translation and Multilinguality
  • Heng Ji
    Heng Ji
    Amazon Scholar
    Senior Area Chair, Multidisciplinary and AC COI
  • Kai-Wei Chang
    Kai-Wei Chang
    Visiting Academic
    Area Chair, Machine Learning for NLP
  • Weiwei Cheng
    Weiwei Cheng
    Area Chair, Question Answering
  • Sgella
    Spandana Gella
    Area Chair, Semantics: Lexical Semantics
  • Julia Hirschberg
    Julia Hirschberg
    Amazon Scholar
    Area Chair, Speech and Multimodality
  • Seokhwan Kim
    Seokhwan Kim
    Area Chair, Dialog and Interactive Systems
  • Lluís Marquez
    Lluís Marquez
    Area Chair, Machine Translation and Multilinguality
  • Matthias Petri
    Matthias Petri
    Area Chair, Information Retrieval and Text Mining
  • Kevin Small.jpg
    Kevin Small
    Area Chair, Question Answering
  • Yi Zhang
    Yi Zhang
    Area Chair, Semantics: Lexical Semantics

Publications (partial list)

Workshops

The 6th Workshop on Noisy User-generated Text (W-NUT) | November 19th
Accepted Publication: Truecasing German user-generated conversational text

Scholarly Document Processing @ EMNLP| November 19th
Organizing committee member: Muthu Kumar Chandrasekaran
Program committee member: Animesh Prasad

2nd Workshop for Natural Language Processing Open Source Software | November 19th
Program committee members: Steve Sloto | Varun Kumar

Deep Learning Inside Out: Knowledge Extraction and Integration | November 19th
Program committee member: Daniil Sorokin

Workshop on Deep Learning Inside Out (DeeLIO) | November 19th

Accepted Publication: Incorporating commonsense knowledge graph in pretrained models for social commonsense tasks - Best Paper award winner

Workshop on Interactive and Executable Semantic Parsing| November 19th
Accepted Publications: Evaluating the effectiveness of efficient neural architecture search for sentence-pair tasks

Uncertainty and traffic-aware active learning for semantic parsing

Fifth Conference on Machine Translation (WMT20) | November 19 - 20th
Accepted Publication: How should markup tags be translated?

NLP Beyond Text | November 20th
Organizing committee members: Giuseppe Castellucci | Simone Filice
Program committee member: Shervin Malmasi

SustaiNLP 2020 | November 20th
Invited Speaker: Heng Ji, Amazon Scholar

PrivateNLP 2020 | November 20th
Organizing committee members: Oluwaseyi Feyisetan | Shervin Malmasi
Program committee members: Luca Melis | Nedelina Teneva | Tom Diethe
Accepted Publications: On primes, log-loss scores and (no) privacy
A differentially private text perturbation method using a regularized Mahalanobis metric

Evaluation and Comparison of NLP Systems (Eval4NLP) | November 20

Accepted Publication: Probabilistic Extension of Precision, Recall, and F1 Score for More Thorough Evaluation of Classification Models

 Workshop on Structured Prediction for NLP | November 20th

Accepted Publication: Generating synthetic data for task-oriented semantic parsing with hierarchical representations

Connect with us at EMNLP

Amazon scientists are looking forward to meeting you at EMNLP. If you would like to connect with one of our scientists, please contact emnlp2020@amazon.com.

Are you ready for your next opportunity? Check out our open positions below. We have global opportunities available.

Related content

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Economist, WW Installments Competitive Pricing
US, WA, Seattle
Job summaryWW Installments is one of the fastest growing businesses within Amazon and we are looking for an Economist to join the team. This group has been entrusted with a massive charter that will impact every customer that visits Amazon.com. We are building the next generation of features and payment products that maximize customer enablement in a simple, transparent, and customer obsessed way. Through these products, we will deliver value directly to Amazon customers improving the shopping experience for hundreds of millions of customers worldwide. Our mission is to delight our customers by building payment experiences and financial services that are trusted, valued, and easy to use from anywhere in any way.Economists at Amazon are solving some of the most challenging applied economics questions in the tech sector. Amazon economists apply the frontier of economic thinking to market design, pricing, forecasting, program evaluation, online advertising and other areas. Our economists build econometric models using our world class data systems, and apply economic theory to solve business problems in a fast-moving environment. A career at Amazon affords economists the opportunity to work with data of unparalleled quality, apply rigorous applied econometric approaches, and work with some of the most talented applied econometricians in the trade.As the Economist within WW Installments, you will be responsible for building long-term causal inference models and experiments. These analysis represent a core capability for WW Installments and businesses across Amazon. Your work will directly impact customers by influencing how objective functions are designed and which inputs are consumed for modeling. You will work across functions including machine learning, business intelligence, data engineering, software development, and finance to induce data driven decisions at every level of the organization.Key job responsibilitiesThis role will be responsible for:• Developing a causal inference and experimentation roadmap for the WW Installments Competitive Pricing team.• Apply expertise in causal and econometric modeling to develop large-scale systems that are deployed across Amazon businesses.• Identify business opportunities, define and execute modeling approach, then deliver outcomes to various Amazon businesses with an Amazon-wide perspective for solutions.• Lead the project plan from a scientific perspective on product launches including identifying potential risks, key milestones, and paths to mitigate risks• Own key inputs to reports consumed by VPs and Directors across Amazon.• Identifying new opportunities to influence business strategy and product vision using causal inference.• Continually improve the WW Installments experimentation roadmap automating and simplifying whenever possible.• Coordinate support across engineers, scientists, and stakeholders to deliver analytical projects and build proof of concept applications.• Work through significant business and technical ambiguity delivering on analytics roadmap across the team with autonomy.
Applied Scientist, WW Installments Competitive Pricing
US, WA, Seattle
Job summaryWW Installments is one of the fastest growing businesses within Amazon and we are looking for an Applied Scientist to join the team. This group has been entrusted with a massive charter that will impact every customer that visits Amazon.com. We are building the next generation of features and payment products that maximize customer enablement in a simple, transparent, and customer obsessed way. Through these products, we will deliver value directly to Amazon customers improving the shopping experience for hundreds of millions of customers worldwide. Our mission is to delight our customers by building payment experiences and financial services that are trusted, valued, and easy to use from anywhere in any way.As an Applied Scientist within WW Installments, you will be responsible for building machine learning models and pipelines with direct customer impact. These models represent a core capability for WW Installments and businesses across Amazon. Your work will directly impact customers by influencing how they interact with financing options to make purchases. You will work across functions including data engineering, software development, and business to induce data driven decisions at every level of the organization.Key job responsibilitiesThis role will be responsible for:• Developing production machine learning models and pipelines for the WW Installments Competitive Pricing team that directly impact customers.• Apply expertise in machine learning to develop large-scale production systems that are deployed across Amazon businesses.• Identify business opportunities, define and execute modeling approach, then deliver outcomes to various Amazon businesses with an Amazon-wide perspective for solutions.• Lead the implementation of production ML from a scientific perspective including identifying potential risks, key milestones, and paths to mitigate risks.• Identifying new opportunities to influence business strategy and product vision using data science and machine learning.• Continually improve the WW Installments ML roadmap automating and simplifying whenever possible.• Coordinate support across engineers, scientists, and stakeholders to deliver ML pipelines, analytics projects, and build proof of concept applications.• Work through significant business and technical ambiguity delivering on analytics roadmap across the team with autonomy.
Data Scientist, WW Installments Competitive Pricing
US, WA, Seattle
Job summaryWW Installments is one of the fastest growing businesses within Amazon and we are looking for a Data Scientist to join the team. This group has been entrusted with a massive charter that will impact every customer that visits Amazon.com. We are building the next generation of features and payment products that maximize customer enablement in a simple, transparent, and customer obsessed way. Through these products, we will deliver value directly to Amazon customers improving the shopping experience for hundreds of millions of customers worldwide. Our mission is to delight our customers by building payment experiences and financial services that are trusted, valued, and easy to use from anywhere in any way.As a Data Scientist within WW Installments, you will be responsible for building machine learning models and pipelines with direct customer impact. These models represent a core capability for WW Installments and businesses across Amazon. Your work will directly impact customers by influencing how they interact with financing options to make purchases. You will work across functions including data engineering, software development, and business to induce data driven decisions at every level of the organization.Key job responsibilitiesThis role will be responsible for:• Developing machine learning models and pipelines for the WW Installments Competitive Pricing team.• Apply expertise in machine learning to develop large-scale systems that are deployed across Amazon businesses.• Identify business opportunities, define and execute modeling approach, then deliver outcomes to various Amazon businesses with an Amazon-wide perspective for solutions.• Lead the project plan from a scientific perspective on product launches including identifying potential risks, key milestones, and paths to mitigate risks.• Own key inputs to reports consumed by VPs and Directors across Amazon.• Identifying new opportunities to influence business strategy and product vision using data science and machine learning.• Continually improve the WW Installments ML roadmap automating and simplifying whenever possible.• Coordinate support across engineers, scientists, and stakeholders to deliver ML pipelines, analytics projects, and build proof of concept applications.• Work through significant business and technical ambiguity delivering on analytics roadmap across the team with autonomy.
Data Scientist II, Private Brands Intelligence
US, CA, San Diego
Job summaryPrivate Brands is fast-growing within Amazon, and is a highly visible, emerging business. We have a unique business and obsess over quality and building global brands our customers love. We aspire to be part of our customers’ everyday lives by offering them unique products at compelling prices backed by Amazon’s strong customer obsessed reputation.Private Brands Intelligence (PBI) is looking for a Data Scientist to join our team in building Machine Learning solutions at scale. PBI applies Machine Learning, Causal Inference, and Econometrics/Economics to derive actionable insights about the complex economy of Amazon’s retail business. We also develop statistical models and algorithms to drive strategic business decisions and improve operations. We are an interdisciplinary team of Economists, Scientists, and Engineers incubating and building Day One solutions using cutting-edge technology, to solve some of the toughest business problems at Amazon.You will work with business leaders, scientists, economists, and engineers to translate business and functional requirements into concrete deliverables, including the design, development, testing, and deployment of highly scalable distributed services. You will partner with scientists, economists, and engineers to help invent and implement scalable ML and econometric models while building tools to help our customers gain and apply insights.This is a unique, high visibility opportunity for someone who wants to have business impact, dive deep into large-scale economic problems, enable measurable actions on the Consumer economy, and work closely with scientists and economists. We are particularly interested in candidates with experience building predictive models and working with distributed systems.As a Data Scientist, you bring business and industry context to science and technology decisions. You set the standard for scientific excellence and make decisions that affect the way we build and integrate algorithms. Your solutions are exemplary in terms of algorithm design, clarity, model structure, efficiency, and extensibility. You tackle intrinsically hard problems, acquiring expertise as needed. You decompose complex problems into straightforward solutions.
Data Scientist - AWS Infrastructure
US, VA, Arlington
Job summaryThis role will sit in our new headquarters in Northern Virginia, where Amazon will invest $2.5 billion dollars, occupy 4 million square feet of energy efficient office space, and create at least 25,000 new full-time jobs.The AWS Infrastructure Data Center Planning and Delivery (DCPD) Data Science team owns supply chain management activities at a global scale.We consolidate usage and supply chain health data and forecasts at a variety of horizons to ensure that we have the right strategic lens associated with each decision we make.We identify gaps to ensure that the AWS business is able to support any and all customers who want to capitalize on the scalability, flexibility, and cost-efficiency of AWS. Our actions and decisions decide the where, how, and what will make it into each of our data centers and we need you to help us to make those decisions and clearly explain the why.The Business Insights and Optimization (BIO) team owns data science, engineering, and business intelligence solutions feeding this team.We identify gaps in our capacity planning and delivery mechanisms and design/build systems which will fix those gaps.We are end to end data product owners and the analysis, models we produce drives billions of dollars of decisions annually.Data Scientists on this team have end to end range and capabilities.They work directly with business owners to understand how they use data to drive their business.They design modeling frameworks to dive deep into these raw sources of information to get the most out of the data they have.They work directly with data engineers to build automated pipelines and production scale information systems and models.They build automated tools which will allow their results to be shared with the business at scale.They align with business owners to continuously track their work to ensure maximum impact from their projects.They monitor performance of their work to evaluate whether improvements are needed after tracking has started in production.
Applied Scientist, Alexa Devices AI
US, CA, Sunnyvale
Job summaryAmong the goals of the Alexa Devices AI team, is to make Alexa the most knowledgeable and trusted ally for notifications, annoucements, pickup services and voice assistance while on the go.Key job responsibilities1. As an Applied Scientist on our team you will work with talented peers to develop novel algorithms and modeling techniques to advance the state of the art NLU (Natural language understanding) developments.2. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to traing Machine Learning models for their application in NLU.3. This role requires a pragmatic technical leader comfortable with ambiguity, capable of summarizing complex data and models through clear visual and written explanations.4. The ideal candidate will have experience with machine learning models and their application in AI systems. We are particularly interested in experience applying natural language processing, deep learning at scale. Additionally, we are seeking candidates with strong interest in data/research sciences and engineering, creativity, curiosity, and great judgment.5. You will interact with various stake holders: product leaders, program managers, other domain managers and developers on regular basis for requirement collections, deliveries, and other related communication6. You will help attract and recruit technical talentA day in the lifeApplied Scientist will help develop novel algorithms and apply modeling techniques to advance the state of the art in spoken language understanding (SLU) and to improve the customer experience in engaging with Alexa.About the teamThe Alexa Devices AI science team's work directly impacts the experience and engagement of customers who rely on Alexa while in-the-car, on-the-go and at-home.
Economist, PXT Central Science
US, VA, Arlington
Job summaryThe 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.We are looking for an economist with expertise in applying causal inference, experimental design, or causal machine learning techniques to topics in labor, personnel, education, health, public, or behavioral economics. We are particularly interested in candidates with experience applying these skills to strategic problems with significant business and/or social policy impact.Candidates will work with economists and engineers to estimate and validate their models on large scale data, and will help business partners turn the results of their analysis into policies, programs, and actions that have a major impact on Amazon’s business and its workforce. We are looking for creative thinkers who can combine a strong economic toolbox with a desire to learn from others, and who know how to execute and deliver on big ideas.Ideal candidates will own key inputs to all stages of research projects, including model development, survey administration, experimental design, and data analysis. They will be customer-centric, working closely with business partners to define key research questions, communicate scientific approaches and findings, listen to and incorporate partner feedback, and deliver successful solutions.
Senior Manager, Applied Science
US, CA, Palo Alto
Job summaryAmazon is the 4th most popular site in the US (http://www.alexa.com/topsites/countries/US). Our product search engine is one of the most heavily used services in the world, indexes billions of products, and serves hundreds of millions of customers world-wide. We are working on a new AI-first initiative to re-architect and reinvent the way we do search through the use of extremely large scale next-generation deep learning techniques. Our goal is to make step function improvements in the use of advanced Machine Learning (ML) on very large scale datasets, specifically through the use of aggressive systems engineering and hardware accelerators. This is a rare opportunity to develop cutting edge ML solutions and apply them to a problem of this magnitude. Some exciting questions that we expect to answer over the next few years include:· Can a focus on compilers and custom hardware help us accelerate model training and reduce hardware costs?· Can combining supervised multi-task training with unsupervised training help us to improve model accuracy?· Can we transfer our knowledge of the customer to every language and every locale ? The Search Science team is looking for a Senior Applied Science Manager to drive roadmap on making large business impact through application of Deep Learning models via close collaboration with partner teams. The team also has a focus on technology solution for deep-learning based embedding generation, sensitive data ingestion and applications, data quality measurement, improvement, data bias identification and reduction to achieve model fairness.Success in this role will require the courage to chart a new course. You will manage your own team to understand all aspects of the customer journey. You and your team will inform other scientists and engineers by providing insights and building models to help improving training data quality and reducing bias. The research focus includes but not limited to Natural Language Processing, recommendation, applications relevant to Amazon buyers, sellers and more. You will be working with cutting edge technologies that enable big data and parallelizable algorithms. You will play an active role in translating business and functional requirements into concrete deliverables and working closely with software development teams to put solutions into production.
Applied Scientist (EC2 Core)
US, WA, Seattle
Job summaryAmazon EC2 provides cloud computing which forms the foundation for the majority of AWS services, as well as a large portion of compute use cases for businesses and individuals around the world. A critical factor in the continued success of EC2 is the ability to provide reliable and cost effective computing. The EC2 Fleet Health and Lifecycle (EC2 FHL) organization is responsible for ensuring that the global EC2 server fleet continues to raise the bar for reliability, security, and efficiency. We are looking for seasoned engineering leaders with passion for technology and an entrepreneurial mindset. At Amazon, it is all about working hard, having fun and making history. If you are ready to make history, we want to hear from you!Come join a brand new team, EC2 Health Analytics, under EC2 Foundational Technology, to solve complex cutting-edge problems to power a faster, more robust and performant EC2 of tomorrow. The charter of our team is to improve customer experience on the EC2 fleet by analyzing hundreds of signals and driving next-generation detection and remediation tools. We apply Machine Learning to predict outcomes and optimize decisions that improve customer experience and operational efficiency. As an Applied Scientist in the EC2 Health Analytics team, you will join an industry-leading engineering team solving challenging problems at massive scale.· Build a world-class forecasting platform that scales to handling billions of time series data in real time.· Drive fleet utilization improvement where each 1% means tens of millions of additional free cash flow.· Automate tactical and strategic capacity planning tools to optimize for service availability and infrastructure cost.· Build recommendation algorithms for improving the AWS customer experience.· · Reduce dependence on manual troubleshooting for deep-dives.What you will learn:· State-of-the-art analytics and forecasting methodologies.· Application of machine learning to large-scale data sets.· · Product recommendation algorithms.· Resource management and admission control for the Cloud.· The internals of all AWS services.Inclusive Team CultureHere 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 BalanceOur 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 GrowthOur 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.
Principal Applied Scientist
US, CA, Palo Alto
Job summaryThe Amazon Search team creates powerful, customer-focused search and advertising solutions and technologies. Whenever a customer visits an Amazon site worldwide and types in a query or browses through product categories, the Amazon Search services go to work. We design, develop, and deploy high performance, fault-tolerant distributed search systems used by millions of Amazon customers every day. Our team works to maximize the quality and effectiveness of the search experience for visitors to Amazon websites worldwide.
View from space of a connected network around planet Earth representing the Internet of Things.
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