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A quick guide to Amazon's 40+ papers at EMNLP 2022

Familiar topics like information extraction and question answering share space with robotics and geolocation learning, and query rewriting emerges as a dynamic area of research.

By Staff writer
December 6, 2022
7 min read
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Amazon’s more than 40 papers at this year’s Conference on Empirical Methods in Natural-Language Processing (EMNLP) — including papers accepted to EMNLP’s new industry track — cover some familiar topics, such as natural-language understanding and question answering. But they also wander farther afield, taking in such disparate subjects as robotics and geospatial learning — and two of the papers concern pun generation.

Query rewriting, whose applications include self-learning and reference resolution, has emerged as a dynamic area of research at Amazon, with five related papers at this year’s EMNLP. And several papers explore the burgeoning field of prompt engineering, or priming large language models to produce the desired types of output.

Below is a quick guide to Amazon’s EMNLP papers, both academic track and industry track.

Continual learning

Iterative stratified testing and measurement for automated model updates
Elizabeth Dekeyser, Nicholas Comment, Shermin Pei, Rajat Kumar, Shruti Rai, Fengtao Wu, Lisa Haverty, Kanna Shimizu

Towards need-based spoken language understanding model updates: What have we learned?
Quynh Do, Judith Gaspers, Daniil Sorokin, Patrick Lehnen

Unsupervised training data reweighting for natural language understanding with local distribution approximation
Jose Garrido Ramas, Thu Le, Bei Chen, Manoj Kumar, Kay Rottmann

Dialogue

retrieval-based-response.png
"Deploying a retrieval based response model for task oriented dialogues" proposes a model in which cross-attention layers learn the semantic correlations between history, profile features, and candidate responses, and a score function computes and ranks the candidate responses.

Deploying a retrieval based response model for task oriented dialogues
Lahari Poddar, Gyuri Szarvas, Cheng Wang, Georges Balazs, Pavel Danchenko, Patrick Ernst

Dialogue meaning representation for task-oriented dialogue systems
Xiangkun Hu, Junqi Dai, Hang Yan, Yi Zhang, Qipeng Guo, Xipeng Qiu, Zheng Zhang

Injecting domain knowledge in language models for task-oriented dialogue systems
Denis Emelin, Daniele Bonadiman, Sawsan Alqahtani, Yi Zhang, Saab Mansour

Evaluation

GEMv2: Multilingual NLG benchmarking in a single line of code
Sebastian Gehrmann, Abhik Bhattacharjee, Abinaya Mahendiran, Alex Wang, Alexandros Papangelis, Aman Madaan, Angelina McMillan-Major, Anna Shvets, Ashish Upadhyay, Bernd Bohnet, Bingsheng Yao, Bryan Wilie, Chandra Bhagavatula, Chaobin You, Craig Thomson, Cristina Garbacea, Dakuo Wang, Daniel Deutsch, Deyi Xiong, Di Jin, Dimitra Gkatzia, Dragomir Radev, Elizabeth Clark, Esin Durmus, Faisal Ladhak, Filip Ginter, Genta Indra Winata, Hendrik Strobelt, Jekaterina Novikova, Jenna Kanerva, Jenny Chim, Jiawei Zhou, Jordan Clive, Joshua Maynez, João Sedoc, Juraj Juraska, Kaustubh Dhole, Khyathi Raghavi Chandu, Laura Perez-Beltrachini, Leonardo F. R. Ribeiro, Lewis Tunstall, Li Zhang, Mahima Pushkarna, Mathias Creutz, Michael White, Mihir Sanjay Kale, Moussa Kamal Eddine, Nico Daheim, Nishant Subramani, Ondrej Dusek, Paul Pu Liang, Pawan Sasanka Ammanamanch, Qi Zhu, Ratish Puduppully, Reno Kriz, Rifat Shahriyar, Saad Mahamood, Salomey Osei, Samuel Cahyawijaya, Sanja Štajner, Sebastien Montella, Shailza Jolly, Simon Mille, Tianhao Shen, Tosin Adewumi, Vikas Raunak, Vipul Raheja, Vitaly Nikolaev, Vivian Tsai, Yacine Jernite, Ying Xu, Yisi Sang, Yixin Liu, Yufang Hou

Fact verification

Fact checking machine generated text with dependency trees
Alex Estes, Nikhita Vedula, Marcus D. Collins, Matthew Cecil, Oleg Rokhlenko

Fact checking.png
The method proposed in "Fact checking machine generated text with dependency trees" identifies entity attributes from the dependency parse tree of an input claim whose factuality is to be assessed.

Fairness

MT-GenEval: A counterfactual and contextual dataset for evaluating gender accuracy in machine translation
Anna Currey, Maria Nadejde, Raghavendra Pappagari, Mia Mayer, Stanislas Lauly, Xing Niu, Benjamin Hsu, Georgiana Dinu

Humor

Context-situated pun generation
Jiao Sun, Anjali Narayan-Chen, Shereen Oraby, Shuyang Gao, Tagyoung Chung, Jing Huang, Yang Liu, Nanyun Peng

ExPUNations: Augmenting puns with keywords and explanations
Jiao Sun, Anjali Narayan-Chen, Shereen Oraby, Alessandra Cervone, Tagyoung Chung, Jing Huang, Yang Liu, Nanyun Peng

Information extraction

A hybrid approach to cross-lingual product review summarization
Saleh Soltan, Victor Soto, Ke Tran, Wael Hamza

Ask-and-Verify: Span candidate generation and verification for attribute value extraction
Yifan Ding, Yan Liang, Nasser Zalmout, Xian Li, Christan Grant, Tim Weninger

DORE: Document ordered relation extraction based on generative framework
Qipeng Guo, Yuqing Yang, Hang Yan, Xipeng Qiu, Zheng Zhang

DORE.png
The method proposed in "DORE: Document ordered relation extraction based on generative framework" identifies multiple instances of the same entity in an input document and builds a relation matrix that records relations between entities.

Learning to revise references for faithful summarization
Griffin Adams, Han-Chin Shing, Qing Sun, Christopher Winestock, Kathleen McKeown, Noémie Elhadad

Prototype-representations for training data filtering in weakly-supervised information extraction
Nasser Zalmout, Xian Li

Information retrieval

Accelerating learned sparse indexes via term impact decomposition
Joel Mackenzie, Antonio Mallia, Alistair Moffat, Matthias Petri

Machine translation impact in e-commerce multilingual search
Bryan Zhang, Amita Misra

Knowledge distillation

Distilling multilingual transformers into CNNs for scalable intent classification
Besnik Fetahu, Akash Veeragouni, Oleg Rokhlenko, Shervin Malmasi

Knowledge distillation transfer sets and their impact on downstream NLU tasks
Charith Peris, Lizhen Tan, Thomas Gueudre, Turan Gojayev, Pan Wei, Gokmen Oz

Machine learning

Calibrating imbalanced classifiers with focal loss: An empirical study
Cheng Wang, Georges Balazs, Gyuri Szarvas, Patrick Ernst, Lahari Poddar, Pavel Danchenko

Model adaptation

Meta-learning the difference: Preparing large language models for efficient adaptation
Zejiang Hou, Julian Salazar, George Polovets

Open world.png
In "Open world classification with adaptive negative samples", Amazon researchers propose a new method for discriminating known and open (unknown) categories of data. This figure compares their approach (d) to ordinary supervised learning (a) and an adaptive-decision-boundary method (c).

Open world classification with adaptive negative samples
Ke Bai, Guoyin Wang, Jiwei Li, Sunghyun Park, Sungjin Lee, Puyang Xu, Ricardo Henao, Lawrence Carin

Multimodal interaction

Multimodal context carryover
Prashan Wanigasekara, Nalin Gupta, Fan Yang, Emre Barut, Zeynab Raeesy, Kechen Qin, Stephen Rawls, Xinyue Liu, Chengwei Su, Spurthi Sandiri

Natural-language processing

McPhraSy: Multi context phrase similarity and clustering
Amir DN Cohen, Hila Gonen, Ori Shapira, Ran Levy, Yoav Goldberg

Unsupervised syntactically controlled paraphrase generation with abstract meaning representations
Kuan-Hao Huang, Varun Iyer, Anoop Kumar, Sriram Venkatapathy, Kai-Wei Chang, Aram Galstyan

Natural-language understanding

Improving large-scale conversational assistants using model interpretation based training sample selection
Stefan Schroedl, Manoj Kumar, Kiana Hajebi, Morteza Ziyadi, Sriram Venkatapathy, Anil Ramakrishna, Rahul Gupta, Pradeep Natarajan

Improving text-to-SQL semantic parsing with fine-grained query understanding
Jun Wang, Patrick Ng, Alexander Hanbo Li, Jiarong Jiang, Zhiguo Wang, Ramesh Nallapati, Bing Xiang, Sudipta Sengupta

Learning geolocations for cold-start and hard-to-resolve addresses via deep metric learning
Govind, Saurabh Sohoney

Geolocation.png
"Learning geolocations for cold-start and hard-to-resolve addresses via deep metric learning" proposes a way to use deep metric learning on addresses to capture geospatial distance semantics.

Prompt engineering

DynaMaR: Dynamic prompt with mask token representation
Xiaodi Sun, Sunny Rajagopalan, Priyanka Nigam, Weiyi Lu, Yi Xu, Iman Keivanloo, Belinda Zeng, Trishul Chilimbi

Inducer-tuning: Connecting prefix-tuning and adapter-tuning
Yifan Chen, Devamanyu Hazarika, Mahdi Namazifar, Yang Liu, Di Jin, Dilek Hakkani-Tür

Query rewriting

CGF.png
In "CGF: Constrained generation framework for query rewriting in conversational AI", Amazon researchers use tries — trees in which each node extends a text by one word — to constrain the outputs of a model that generates query rewrites.

CGF: Constrained generation framework for query rewriting in conversational AI
Jie Hao, Yang Liu, Xing Fan, Saurabh Gupta, Saleh Soltan, Rakesh Chada, Pradeep Natarajan, Edward Guo, Gokhan Tur

CycleKQR: Unsupervised bidirectional keyword question rewriting
Andrea Iovine, Anjie Fang, Besnik Fetahu, Jie Zhao, Oleg Rokhlenko, Shervin Malmasi

PAIGE: Personalized adaptive interactions graph encoder for query rewriting in dialogue systems
Daniel Bis, Saurabh Gupta, Jie Hao, Xing Fan, Edward Guo

PENTATRON: PErsonalized coNText-aware transformer for retrieval-based cOnversational uNderstanding
Niranjan Uma Naresh, Ziyan Jiang, Ankit, Sungjin Lee, Jie Hao, Xing Fan, Edward Guo

Reinforced question rewriting for conversational question answering
Zhiyu Chen, Jie Zhao, Anjie Fang, Besnik Fetahu, Oleg Rokhlenko, Shervin Malmasi

Question answering

Ensemble transformer for efficient and accurate ranking tasks: An application to question answering systems
Yoshitomo Matsubara, Luca Soldaini, Eric Lind, Alessandro Moschitti

FocusQA: Open-domain question answering with a context in focus
Gianni Barlacchi, Ivano Lauriola, Alessandro Moschitti, Marco Del Tredici, Xiaoyu Shen, Thuy Vu, Bill Byrne, Adrià de Gispert

Knowledge transfer from answer ranking to answer generation
Matteo Gabburo, Rik Koncel-Kedziorski, Siddhant Garg, Luca Soldaini, Alessandro Moschitti

Pre-training transformer models with sentence-level objectives for answer sentence selection
Luca Di Liello, Siddhant Garg, Luca Soldaini, Alessandro Moschitti

RLET: A reinforcement learning based approach for explainable QA with entailment trees
Tengxiao Liu, Qipeng Guo, Xiangkun Hu, Yue Zhang, Xipeng Qiu, Zheng Zhang

Robotics

ALFRED-L: Investigating the role of language for action learning in interactive visual environments
Arjun R. Akula, Spandana Gella, Aishwarya Padmakumar, Mahdi Namazifar, Mohit Bansal, Jesse Thomason, Dilek Hakkani-Tür

ALFRED-L.png
"ALFRED-L: Investigating the role of language for action learning in interactive visual environments" proposes a new test split to the ALFRED benchmark for embodied-task completion. The test split — ALFRED-L — includes instructions that an agent backtrack to known reference positions along its trajectory, to evaluate whether it can remember their locations.

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Sr. Applied Scientist, Foundation Model, Industrial Robotics Group
US, CA, Sunnyvale
Amazon Industrial Robotics Group is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine innovative AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic manipulation, locomotion, and human-robot interaction. This role presents an opportunity to shape the future of robotics through innovative applications of deep learning and large language models. We leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence. We are pioneering the development of robotics foundation models that: - Enable unprecedented generalization across diverse tasks - Integrate multi-modal learning capabilities (visual, tactile, linguistic) - Accelerate skill acquisition through demonstration learning - Enhance robotic perception and environmental understanding - Streamline development processes through reusable capabilities The ideal candidate will contribute to research that bridges the gap between theoretical advancement and practical implementation in robotics. You will be part of a team that's revolutionizing how robots learn, adapt, and interact with their environment. Join us in building the next generation of intelligent robotics systems that will transform the future of automation and human-robot collaboration. As a Senior Applied Scientist, you will lead the development of machine learning systems that help robots perceive, reason, and act in real-world environments. You will set technical direction for adapting and advancing state-of-the-art models (open source and internal research) into robust, safe, and high-performing “robot brain” capabilities for our target tasks, environments, and robot embodiments. You will drive rigorous capability profiling and experimentation, lead targeted innovation where gaps exist, and partner across research, controls, hardware, and product teams to ensure outputs can be further customized and deployed on specific robots. Key job responsibilities - Lead technical initiatives for foundation-model capabilities (e.g., visuomotor / VLA / video-action worldmodel-action policies), from problem definition through validated model deliverables. - Own model readiness for our embodiment class: drive adaptation, fine-tuning, and optimization (latency/throughput/robustness), and define success criteria that downstream teams can build on. - Establish and evolve capability evaluation: define benchmark strategy, metrics, and profiling methodology to quantify performance, generalization, and failure modes; ensure evaluations drive clear roadmap decisions. - Drive the data + training strategy needed to close key capability gaps, including data requirements, collection/curation standards, dataset quality/provenance, and repeatable training recipes (sim + real). - Invent and validate new methods when leveraging SOTA is insufficient—new training schemes, model components, supervision signals, or sim↔real techniques—backed by strong empirical evidence. - Influence cross-team technical decisions by collaborating with controls/WBC, hardware, and product teams on interfaces, constraints, and integration plans; communicate results via design docs and technical reviews. - Mentor and raise the bar: guide junior scientists/engineers, set best practices for experimentation and code quality, and drive a culture of rigor and reproducibility.
Applied Scientist II, Sponsored Products and Brands - Advertiser Guidance
US, WA, Seattle
We are looking for a passionate Applied Scientist to help pioneer the next generation of agentic AI applications for Amazon advertisers. In this role, you will design agentic architectures, develop tools and datasets, and contribute to building systems that can reason, plan, and act autonomously across complex advertiser workflows. You will work at the forefront of applied AI, developing methods for fine-tuning, reinforcement learning, and preference optimization, while helping create evaluation frameworks that ensure safety, reliability, and trust at scale. You will work backwards from the needs of advertisers—delivering customer-facing products that directly help them create, optimize, and grow their campaigns. Beyond building models, you will advance the agent ecosystem by experimenting with and applying core primitives such as tool orchestration, multi-step reasoning, and adaptive preference-driven behavior. This role requires working independently on ambiguous technical problems, collaborating closely with scientists, engineers, and product managers to bring innovative solutions into production. Key job responsibilities - Design and build agents to guide advertisers in conversational and non-conversational experience. - Design and implement advanced model and agent optimization techniques, including supervised fine-tuning, instruction tuning and preference optimization (e.g., DPO/IPO). - Curate datasets and tools for MCP. - Build evaluation pipelines for agent workflows, including automated benchmarks, multi-step reasoning tests, and safety guardrails. - Develop agentic architectures (e.g., CoT, ToT, ReAct) that integrate planning, tool use, and long-horizon reasoning. - Prototype and iterate on multi-agent orchestration frameworks and workflows. - Collaborate with peers across engineering and product to bring scientific innovations into production. - Stay current with the latest research in LLMs, RL, and agent-based AI, and translate findings into practical applications. About the team The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through the latest generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. The Campaign Strategies team within Sponsored Products and Brands is focused on guiding and supporting 1.6MM advertisers to meet their advertising needs of creating and managing ad campaigns. At this scale, the complexity of diverse advertiser goals, campaign types, and market dynamics creates both a massive technical challenge and a transformative opportunity: even small improvements in guidance systems can have outsized impact on advertiser success and Amazon’s retail ecosystem. Our vision is to build a highly personalized, context-aware agentic advertiser guidance system that leverages LLMs together with tools such as auction simulations, ML models, and optimization algorithms. This agentic framework, will operate across both chat and non-chat experiences in the ad console, scaling to natural language queries as well as proactively delivering guidance based on deep understanding of the advertiser. To execute this vision, we collaborate closely with stakeholders across Ad Console, Sales, and Marketing to identify opportunities—from high-level product guidance down to granular keyword recommendations—and deliver them through a tailored, personalized experience. Our work is grounded in state-of-the-art agent architectures, tool integration, reasoning frameworks, and model customization approaches (including tuning, MCP, and preference optimization), ensuring our systems are both scalable and adaptive.
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