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Dublin Ireland
Conference
ACL 2022
May 22 - 27, 2022
Dublin, Ireland

Overview

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing.

Sponsorship Details

Diamond
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Amazon organizing committee members

  • Smaranda-Muresan.jpg
    Smaranda Muresan
    Amazon Scholar,
    Program co-chair
  • MLRA Photos/Mohit_Bansal.jpg
    Mohit Bansal
    Amazon Scholar
    Senior area chair, Machine Learning for NLP
  • Dilek Hakkani-Tur.jpg
    Dilek Hakkani-Tür
    Senior area chair, Dialogue and Interactive Systems
  • Heng Ji
    Heng Ji
    Amazon Scholar
    Senior area chair, Information Extraction
  • Yang Liu
    Yang Liu
    Senior area chair, Speech and Multimodality
  • kathleen.jpg
    Kathleen McKeown
    Amazon Scholar
    Senior area chair, Summarization
  • alessandro_moschitti.jpg
    Alessandro Moschitti
    Senior area chair, Question Answering

Accepted publications

ACL Findings

Workshops

ACL 2022 Workshop on Insights from Negative Results in NLP
May 26
This workshop invites both practical and theoretical unexpected or negative results that have important implications for future research, highlight methodological issues with existing approaches, and/or point out pervasive misunderstandings or bad practices.

Amazon organizer: Anna Rumshisky

Website: https://insights-workshop.github.io
Evaluating the practical utility of confidence-score based techniques for unsupervised open-world intent classification
Sopan Khosla, Rashmi Gangadharaiah
2022
Conversational AI / Natural-language processing
On the limits of evaluating embodied agent model generalization using validation sets
Hyounghun Kim, Aishwarya Padmakumar, Di Jin, Mohit Bansal, Dilek Hakkani-Tür
2022
Computer vision
ACL 2022 Workshop on ECNLP
May 26 - May 28
The ECNLP workshop aims to provide a venue for the dissemination of NLP/IR research related to e-commerce and online shopping, bringing together researchers from both academia and industry.

Amazon organizing committee: Shervin Malmasi, Oleg Rokhlenko

Website: https://sites.google.com/view/ecnlp/home?authuser=0
CML: A contrastive meta learning method to estimate human label confidence scores and reduce data collection cost
Bo Dong, Yiyi Wang, Hanbo Sun, Yunji Wang, Alireza Hashemi, Zheng Du
2022
Conversational AI / Natural-language processing
Improving relevance quality in product search using high-precision query-product semantic similarity
Ali Bagheri Garakani, Fan Yang, Wen-Yu Hua, Yetian Chen, Michinari Momma, Jingyuan Deng, Yan Gao, Yi Sun
2022
Search and information retrieval
Leveraging seq2seq language generation for multi-level product issue identification
Yang Liu, Varnith Uttam Chordia, Hua Li, Siavash Fazeli, Yifei Sun, Vincent Gao, Na Zhang
2022
Conversational AI / Natural-language processing
Logical reasoning for task oriented dialogue systems
Sajjad beygi, Maryam Fazel-Zarandi, Alessandra Cervone, Prakash Krishnan, Siddhartha Reddy Jonnalagadda
2022
Conversational AI / Natural-language processing
Product answer generation from heterogeneous sources: A new benchmark and best practices
Xiaoyu Shen, Gianni Barlacchi, Marco Del Tredici, Weiwei Cheng, Adrià de Gispert, Bill Byrne
2022
Conversational AI / Natural-language processing
Robust product classification with instance-dependent noise
Huy Nguyen, Devashish Khatwani
2022
Conversational AI / Natural-language processing
semiPQA: A study on product question answering over semi-structured data
Xiaoyu Shen, Gianni Barlacchi, Marco Del Tredici, Weiwei Cheng, Adrià de Gispert
2022
Conversational AI / Natural-language processing
Spelling correction using phonetics in e-commerce search
Fan Yang, Ali Bagheri Garakani, Yifei Teng, Yan Gao, Jia Liu, Jingyuan Deng, Yi Sun
2022
Search and information retrieval
ACL 2022 Workshop on Federated Learning for Natural Language Processing
May 26
ActPerFL: Active personalized federated learning
Huili Chen, Jie Ding, Eric Tramel, Shuang Wu, Anit Kumar Sahu, Salman Avestimehr, Tao Zhang
2022
Conversational AI / Natural-language processing
ACL 2022 Workshop on NLP for Conversational AI
May 27
This workshop will bring together researchers and practitioners to discuss impactful research problems in conversational AI, share findings from real-world applications, and generate ideas for future research directions. The workshop will include keynotes, posters, and panel sessions.

Amazon speaker: Gokhan Tur

Amazon organizers: Alexandros Papangelis

Amazon program committee: Dilek Hakkani-Tur, Gokhan Tur, Lei Shu, Seokhwan Kim, Yang Liu

Website: https://sites.google.com/view/4thnlp4convai
MTL-SLT: Multi-task learning for spoken language tasks
Zhiqi Huang, Milind Rao, Anirudh Raju, Zhe Zhang, Bach Bui, Chul Lee
2022
Conversational AI / Natural-language processing
Selfie: Reflections on TLS 1.3 with PSK
Nir Drucker, Shay Gueron
2020
Conversational AI / Natural-language processing
ACL 2022 Workshop on Biomedical Natural Language Processing (BioNLP)
May 26
The BioNLP workshop associated with the ACL SIGBIOMED special interest group has established itself as the primary venue for presenting foundational research in language processing for the biological and medical domains. BioNLP 2022 will be particularly interested in work on detection and mitigation of bias, BioNLP research in languages other than English, particularly, under-represented languages, and health disparities.

Amazon program committee: Manirupa Das

Website: https://aclweb.org/aclwiki/BioNLP_Workshop
ACL 2022 Workshop on Efficient Benchmarking in NLP
May 26
The main objectives of this workshop are to (1) create a space for critical reflection on current benchmarks and evaluation tools, (2) encourage the development of improved or new benchmarks and evaluation tools that resolve current challenges, (3) develop better approaches to model ranking, (4) rethink benchmarking strategies that best account for computational costs, energy and ethical considerations, out-of-domain language capabilities and meeting the end-user preferences.

Amazon speaker: Anna Rumshisky

Website: https://nlp-power.github.io

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Sr Principal Scientist, Search, Search Science
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Applied Science Manager, Japan Retail Science
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Senior Applied Scientist, Sponsored Products, Demand Identification
CA, ON, Toronto
WE ARE OPEN TO HIRING THIS ROLE IN SF BAY AREA, SEATTLE, TORONTO, OR VIRGINIA (HQ2). Amazon Advertising is one of Amazon's fastest growing and most profitable businesses, responsible for defining and delivering a collection of advertising products that drive discovery and sales. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day! The Sponsored Products Demand Identification and Campaign Optimization (DEICO) team is responsible for building experiences across all seller and advertiser touchpoints to generate performant Sponsored Products demand and improved campaign performance through guardrail-based controls. We do this through 1) Creating performant demand focusing on Amazon supplier use cases (e.g. launching a new ASIN) through presets for campaign creation. 2) Making existing demand performant through diagnosing gaps and providing proactive and targeted recommendations, and 3) Developing and launching new campaign optimization rules that allow advertisers to provide performance guardrails and automate optimization of their campaigns. Within these domains, we focus on creating holistic recommendations through machine learning for products to advertise and optimal presets across every parameter that go into Sponsored Products ad campaign setup, as well as consolidated recommendations that improve performance of existing Sponsored Product campaigns. As an Applied Scientist on this team, you will: • Drive end-to-end Machine Learning projects that have a high degree of ambiguity, scale, complexity. • Perform hands-on analysis and modeling of enormous data sets to develop insights that increase traffic monetization and merchandise sales, without compromising the shopper experience. • 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. • Run A/B 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. • Research new and innovative machine learning approaches. • Recruit Applied Scientists to the team and provide mentorship. Why you will love this opportunity: Amazon is investing heavily in building a world-class advertising business. This team defines and delivers a collection of advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon’s Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate. Impact and Career Growth: You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding. Team video https://youtu.be/zD_6Lzw8raE
Applied Science Manager, ATT
IN, KA, Bangalore
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. The ATT team, based in Bangalore, is responsible for ensuring that ads are compliant to world-wide advertising policies and are of high quality, leading to higher conversion for the advertisers and providing a great experience for the shoppers. Machine learning, particularly multi-modal data understanding, is fundamental to the way we drive our business, meet our goals and satisfy our customers. ATT team invests in researching and developing state of art models that analyze various type of ad assets – text, audio, images and videos - to ensure compliance to advertising policies. We also help advertisers create more successful ads by creating ML models to assist ad generation as well as to provide data-driven interpretable insights. Key job responsibilities Major responsibilities · Deliver key goals to enhance advertiser experience and protect shopper trust by innovative use of computer vision, NLP and statistical techniques · Drive core business analytics and data science explorations to inform key business decisions and algorithm roadmap · Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation · Hire and develop top talent in machine learning and data science and accelerate the pace of innovation in the group · Work proactively with engineering teams and product managers to evangelize new algorithms and drive the implementation of large-scale complex ML models in production
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