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A quick guide to Amazon's 40+ papers at EMNLP 2023

Research on natural-language understanding seeks to harness the power of large language models, while query reformulation and text summarization emerge as topics of particular interest.

By Staff writer
December 06, 2023
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Natural-language understanding (NLU) has long been a central focus of the papers that Amazon researchers publish at the Conference on Empirical Methods in Natural-Language Processing (EMNLP), but at this year's conference, which starts today, Amazon's NLU research shows a particular interest in harnessing the power of large language models (LLMs). Question answering also remains an active research topic, while query reformulation and text summarization emerge as new areas of concentration.

Automatic speech recognition

AdaBERT-CTC: Leveraging BERT-CTC for text-only domain adaptation in ASR
Tyler Vuong, Karel Mundnich, Dhanush Bekal, Veera Raghavendra Elluru, Srikanth Ronanki, Sravan Bodapati

Continual learning

Coordinated replay sample selection for continual federated learning
Jack Good, Jimit Majmudar, Christophe Dupuy, Jixuan Wang, Charith Peris, Clement Chung, Richard Zemel, Rahul Gupta 

Data extraction

InsightNet: Structured insight mining from customer feedback
Sandeep Mukku, Manan Soni, Chetan Aggarwal, Jitenkumar Rana, Promod Yenigalla, Rashmi Patange, Shyam Mohan

Knowledge-selective pretraining for attribute value extraction
Hui Liu, Qingyu Yin, Zhengyang Wang, Chenwei Zhang, Haoming Jiang, Yifan Gao, Zheng Li, Xian Li, Chenwei Zhang, Bing Yin, William Wang, Xiaodan Zhu

Data selection

Influence scores at scale for efficient language data sampling
Nikhil Anand, Joshua Tan, Maria Minakova

Document understanding

A multi-modal multilingual benchmark for document image classification
Yoshinari Fujinuma, Siddharth Varia, Nishant Sankaran, Bonan Min, Srikar Appalaraju, Yogarshi Vyas

Semantic matching for text classification with complex class descriptions
Brian de Silva, Kuan-Wen Huang, Gwang Lee, Karen Hovsepian, Yan Xu, Mingwei Shen

Embodied task completion

Multimodal embodied plan prediction augmented with synthetic embodied dialogue
Aishwarya Padmakumar, Mert Inan, Spandana Gella, Patrick Lange, Dilek Hakkani-Tür

Entity linking

MReFinED: An efficient end-to-end multilingual entity linking system
Peerat Limkonchotiwat, Weiwei Cheng, Christos Christodoulopoulos, Amir Saffari, Jens Lehmann

Few-shot learning

Automated few-shot classification with instruction-finetuned language models
Rami Aly, Xingjian Shi, Kaixiang Lin, Aston Zhang, Andrew Wilson

AuT-Few.png
A schematic view of the Aut-Few prompt automation method. From "Automated few-shot classification with instruction-finetuned language models".

Information retrieval

Deep metric learning to hierarchically rank—An application in product retrieval
Kee Kiat Koo, Ashutosh Joshi, Nishaanth Reddy, Ismail Tutar, Vaclav Petricek, Changhe Yuan, Karim Bouyarmane

KD-Boost: Boosting real-time semantic matching in e-commerce with knowledge distillation
Sanjay Agrawal, Vivek Sembium, Ankith M S

CESAR.png
The CESAR framework automatically merges compound tasks — such as, in this example, keyword-controlled generation and act-grounded generation. From "CESAR: Automatic induction of compositional instructions for multi-turn dialogs".

Multi-teacher distillation for multilingual spelling correction
Jingfen Zhang, Xuan Guo, Sravan Bodapati, Christopher Potts

Instruction tuning

CESAR: Automatic induction of compositional instructions for multi-turn dialogs
Taha Aksu, Devamanyu Hazarika, Shikib Mehri, Seokhwan Kim, Dilek Hakkani-Tür, Yang Liu, Mahdi Namazifar

LLM hallucination

INVITE: A testbed of automatically generated invalid questions to evaluate large language models for hallucinations
Anil Ramakrishna, Rahul Gupta, Jens Lehmann, Morteza Ziyadi

Machine learning

Efficient long-range transformers: You need to attend more, but not necessarily at every layer
Qingru Zhang, Dhananjay Ram, Cole Hawkins, Sheng Zha, Tuo Zhao

Natural-language processing

NameGuess: Column name expansion for tabular data
Jiani Zhang, Zhengyuan Shen, Balasubramaniam Srinivasan, Shen Wang, Huzefa Rangwala, George Karypis

Natural-language understanding

Adversarial robustness for large-language NER models using disentanglement and word attributions
Xiaomeng Jin, Bhanu Vinzamuri, Sriram Venkatapathy, Heng Ji, Pradeep Natarajan

Measuring and mitigating dialog-to-API constraint violations of in-context learning
Shufan Wang, Sebastien Jean, Sailik Sengupta, James Gung, Nikolaos Pappas, Yi Zhang

Intent classification.png
Overview of the pretraining of an intent-aware encoder. Given an utterance, x1, from the pretraining corpus, Amazon researchers generate a pseudo intent name, y1pseudo, using labels from the intent-role-labeling (IRL) tagger. The model is then optimized by pulling the gold utterance x1gold, the gold intent y1, and the pseudo intent, y1pseudo, close to the input utterance, x1, in the embedding space. From "Pre-training intent-aware encoders for zero- and few-shot intent classification".

MultiCoNER v2: A large multilingual dataset for fine-grained and noisy named entity recognition
Besnik Fetahu, Zhiyu Chen, Sudipta Kar, Oleg Rokhlenko, Shervin Malmasi

Pre-training intent-aware encoders for zero- and few-shot intent classification
Mujeen Sung, James Gung, Elman Mansimov, Nikolaos Pappas, Raphael Shu, Salvatore Romeo, Yi Zhang, Vittorio Castelli

Personalization

Personalized dense retrieval on global index for voice-enabled conversational systems
Masha Belyi, Charlotte Dzialo, Chaitanya Dwivedi, Prajit Reddy Muppidi, Kanna Shimizu

Retrieve and copy: Scaling ASR personalization to large catalogs
Sai Muralidhar Jayanthi, Devang Kulshreshtha, Saket Dingliwal, Srikanth Ronanki, Sravan Bodapati

Query reformulation

CL-QR: Cross-lingual enhanced query reformulation for multi-lingual conversational AI agents
Zhongkai Sun, Zhengyang Zhao, Sixing Lu, Chengyuan Ma, Xiaohu Liu, Xing Fan, Wei (Sawyer) Shen, Chenlei (Edward) Guo

Graph meets LLM: A novel approach to collaborative filtering for robust conversational understanding
Zheng Chen, Ziyan Jiang, Fan Yang, Eunah Cho, Xing Fan, Xiaojiang Huang, Yanbin Lu, Aram Galstyan

Improving contextual query rewrite for conversational AI agents through user-preference feedback learning
Zhongkai Sun, Yingxue Zhou, Jie Hao, Xing Fan, Yanbin Lu, Chengyuan Ma, Wei (Sawyer) Shen, Chenlei (Edward) Guo

Question-answer databases

Protege: Prompt-based diverse question generation from web articles
Vinayak Puranik, Anirban Majumder, Vineet Chaoji

QUADRo: Dataset and models for question-answer database retrieval
Stefano Campese, Ivano Lauriola, Alessandro Moschitti

Question answering

Strong and efficient baselines for open domain conversational question answering
Andrei C. Coman, Gianni Barlacchi, Adrià de Gispert

Tokenization consistency matters for generative models on extractive NLP tasks
Kaiser Sun, Peng Qi, Yuhao Zhang, Lan Liu, William Yang Wang, Zhiheng Huang

Too much of product information: Don’t worry, let’s look for evidence!
Aryan Jain, Jitenkumar Rana, Chetan Aggarwal

Reasoning

Plan, verify and switch: Integrated reasoning with diverse x-of-thoughts
Tengxiao Liu, Qipeng Guo, Yuqing Yang, Xiangkun Hu, Yue Zhang, Xipeng Qiu, Zheng Zhang 

XOT.png
An overview of the x-of-thought (XoT) problem-solving framework, which integrates chain-of-thought (CoT) and program-of-thought (PoT) methods with the researchers' novel equation-of-thought (EoT) approach. From "Plan, verify and switch: Integrated reasoning with diverse x-of-thoughts".

Responsible AI

Geographical erasure in language generation
Pola Schwöbel, Jacek Golebiowski, Michele Donini, Cédric Archambeau, Danish Pruthi

Speech translation

End-to-end single-channel speaker-turn aware conversational speech translation
Juan Pablo Zuluaga Gomez, Zhaocheng Huang, Xing Niu, Rohit Paturi, Sundararajan Srinivasan, Prashant Mathur, Brian Thompson, Marcello Federico

Text summarization

Enhancing abstractiveness of summarization models through calibrated distillation
Hwanjun Song, Igor Shalyminov, Hang Su, Siffi Singh, Kaisheng Yao, Saab Mansour

Generating summaries with controllable readability levels
Leonardo Ribeiro, Mohit Bansal, Markus Dreyer

Improving consistency for text summarization with energy functions
Qi Zeng, Qingyu Yin, Zheng Li, Yifan Gao, Sreyashi Nag, Zhengyang Wang, Bing Yin, Heng Ji, Chao Zhang

InstructPTS: Instruction-tuning LLMs for product title summarization
Besnik Fetahu, Zhiyu Chen, Oleg Rokhlenko, Shervin Malmasi

Multi document summarization evaluation in the presence of damaging content
Avshalom Manevich, David Carmel, Nachshon Cohen, Elad Kravi, Ori Shapira

Re-examining summarization evaluation across multiple quality criteria
Ori Ernst, Ori Shapira, Ido Dagan, Ran Levy

Topic modeling

DeTiME: Diffusion-enhanced topic modeling using encoder-decoder based LLM
Weijie Xu, Wenxiang Hu, Fanyou Wu, Srinivasan Sengamedu, "SHS"

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Data Scientist II , AWS Energy Team
US, WA, Seattle
AWS Infrastructure Services owns the design, planning, delivery, and operation of all AWS global infrastructure. In other words, we’re the people who keep the cloud running. We support all AWS data centers and all of the servers, storage, networking, power, and cooling equipment that ensure our customers have continual access to the innovation they rely on. We work on the most challenging problems, with thousands of variables impacting the supply chain — and we’re looking for talented people who want to help. You’ll join a diverse team of software, hardware, and network engineers, supply chain specialists, security experts, operations managers, and other vital roles. You’ll collaborate with people across AWS to help us deliver the highest standards for safety and security while providing seemingly infinite capacity at the lowest possible cost for our customers. And you’ll experience an inclusive culture that welcomes bold ideas and empowers you to own them to completion. In 2019, Amazon co-founded The Climate Pledge and made a commitment to achieve net-zero carbon by 2040 —10 years ahead of the Paris Agreement. We invited others to join us and there are now more than 300 businesses and organizations across 51 industries and 29 countries that have signed the Pledge, which means we are collectively coming at the climate crisis from nearly every sector and nearly every angle. As part of our efforts to decarbonize our business, we became the world’s largest corporate purchaser of renewable energy in 2020, and last year, we reached 85% renewable energy across our business, and are on a path to power our operations with 100% renewable energy by 2025. We recently announced that AWS will be water positive by 2030, returning more water to communities than it uses in its direct operations. The company also announced its 2021 global water use efficiency (WUE) metric of 0.25 liters of water per kilowatt-hour, demonstrating AWS’s leadership in water efficiency among cloud providers. To learn more about AWS’s water+ commitment visit: Water Stewardship. Come join the team that is building the tools and innovative technology to manage our growing portfolio of renewable energy investments, including solar, on-shore and off-shore wind farms. Key job responsibilities As an data scientist, you will employ machine learning and analytics to create scalable solutions for problems in sustainable energy space. You will dissect large historical business data sets to enhance and streamline essential processes. You will partner with data and software teams to create models for predictive insights and establish automated methods for large data analysis. A day in the life To learn more, you can visit: amazon sustainability in the cloud About the team 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. Diverse Experiences Amazon 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. 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 in the cloud. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship and 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.
Principal Applied Scientist, Automated Reasoning in Identity
US, CA, Santa Clara
Are you passionate about applying automated reasoning and program analysis to real world problems? Do you want to create products that help customers? If so, then we have an exciting opportunity for you. We’re looking for an Applied Scientist to help strengthen our customers' security with automation for managed controls. AWS Identity provides the bedrock for secure and continuous access to all AWS services. By quickly connecting millions of users, across the world we empower organizations and enterprises to accelerate their cloud and digital transformation. In this role, you will interact with internal teams and external customers to understand their requirements. You will apply your knowledge to propose innovative solutions, create software prototypes, and productize prototypes into production systems using software development tools and methodologies. In addition, you will support and scale your solutions to meet the ever growing demand of customer use. Key job responsibilities * Interact with various teams to develop an understanding of their security and safety requirements. * Apply the acquired knowledge to build tools and algorithms, find problems, or show the absence of security/safety problems. * Implement these capabilities through the use of Automated Reasoning and various concepts from programming languages. * Perform analysis of the customer systems using tools developed in-house or externally provided * Create software prototypes to verify and validate the devised solutions methodologies; integrate the prototypes into production systems using standard software development tools and methodologies. 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 Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. 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 in the cloud. This team is part of AWS Utility Computing: Utility Computing (UC) AWS Utility Computing (UC) provides product innovations — from foundational services such as Amazon’s Simple Storage Service (S3) and Amazon Elastic Compute Cloud (EC2), to consistently released new product innovations that continue to set AWS’s services and features apart in the industry. As a member of the UC organization, you’ll support the development and management of Compute, Database, Storage, Internet of Things (Iot), Platform, and Productivity Apps services in AWS, including support for customers who require specialized security solutions for their cloud services.
Economist, Prime Science
US, WA, Seattle
Amazon Prime is looking for an ambitious Economist to help create econometric insights for world-wide Prime. Prime is Amazon's premiere membership program, with over 200M members world-wide. This role is at the center of many major company decisions that impact Amazon's customers. These decisions span a variety of industries, each reflecting the diversity of Prime benefits. These range from fast-free e-commerce shipping, digital content (e.g., exclusive streaming video, music, gaming, photos), and grocery offerings. Prime Science creates insights that power these decisions. As an economist in this role, you will create statistical tools that embed causal interpretations. You will utilize massive data, state-of-the-art scientific computing, econometrics (causal, counterfactual/structural, time-series forecasting, experimentation), and machine-learning, to do so. Some of the science you create will be publishable in internal or external scientific journals and conferences. You will work closely with a team of economists, applied scientists, data professionals (business analysts, business intelligence engineers), product managers, and software engineers. You will create insights from descriptive statistics, as well as from novel statistical and econometric models. You will create internal-to-Amazon-facing automated scientific data products to power company decisions. You will write strategic documents explaining how senior company leaders should utilize these insights to create sustainable value for customers. These leaders will often include the senior-most leaders at Amazon. The team is unique in its exposure to company-wide strategies as well as senior leadership. It operates at the cutting-edge of utilizing data, econometrics, artificial intelligence, and machine-learning to form business strategies. A successful candidate will have demonstrated a capacity for building, estimating, and defending statistical models (e.g., causal, counterfactual, time-series, machine-learning) using software such as R, Python, or STATA. They will have a willingness to learn and apply a broad set of statistical and computational techniques to supplement deep-training in one area of econometrics. For example, many applications on the team use structural econometrics, machine-learning, and time-series forecasting. They rely on building scalable production software, which involves a broad set of world-class software-building skills often learned on-the-job. As a consequence, already-obtained knowledge of SQL, machine learning, and large-scale scientific computing using distributed computing infrastructures such as Spark-Scala or PySpark would be a plus. Additionally, this candidate will show a track-record of delivering projects well and on-time, preferably in collaboration with other team members (e.g. co-authors). Candidates must have very strong writing and emotional intelligence skills (for collaborative teamwork, often with colleagues in different functional roles), a growth mindset, and a capacity for dealing with a high-level of ambiguity. Endowed with these traits and on-the-job-growth, the role will provide the opportunity to have a large strategic, world-wide impact on the customer experiences of Prime members.
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