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A quick guide to Amazon’s 45-plus NAACL papers

The breadth and originality of Amazon’s natural-language-processing research are on display at the annual meeting of the North American chapter of the Association for Computational Linguistics.

By Staff writer
July 7, 2022
6 min read
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Amazon’s 45-plus papers at the annual meeting of the North American chapter of the Association for Computational Linguistics, which begins next week, sorted by research area.

Continual learning

Lifelong pretraining: Continually adapting language models to emerging corpora
Xisen Jin, Dejiao Zhang, Henghui Zhu, Wei Xiao, Shang-Wen Li, Xiaokai Wei, Andrew O. Arnold, Xiang Ren

Local-to-global learning for iterative training of production SLU models on new features
Yulia Grishina, Daniil Sorokin

Overcoming catastrophic forgetting during domain adaptation of seq2seq language generation
Dingcheng Li, Zheng Chen, Eunah Cho, Jie Hao, Xiaohu Liu, Xing Fan, Chenlei (Edward) Guo, Yang Liu

Overcoming catastrophic forgetting.png
In "Overcoming catastrophic forgetting during domain adaptation of seq2seq language generation", Amazon researchers propose a method for estimating how much data representations shift when an existing model is trained on a new task (right).

Temporal generalization for spoken language understanding
Judith Gaspers, Anoop Kumar, Greg Ver Steeg, Aram Galstyan

Data augmentation

Constraining word alignments with posterior regularization for label transfer
Kevin Martin Jose, Thomas Gueudré

Word alignments.png
An example of the difficulty in using word alignment to transfer textual labels from one language to another. In English, the article "the" is assigned the label "o", for "other"; in French, the abbreviated article is combined with its noun, and both receive the same label ("type"). From "Constraining word alignments with posterior regularization for label transfer".

Controlled data generation via insertion operations for NLU
Manoj Kumar, Haidar Khan, Yuval Merhav, Wael Hamza, Anna Rumshisky, Rahul Gupta

Efficient semi supervised consistency training for natural language understanding
George Leung, Joshua Tan

Learning to generate examples for semantic processing tasks
Danilo Croce, Simone Filice, Giuseppe Castellucci, Roberto Basili

Dialogue

Learning dialogue representations from consecutive utterances
Zhihan Zhou, Dejiao Zhang, Wei Xiao, Nicholas Dingwall, Xiaofei Ma, Andrew O. Arnold, Bing Xiang

Massive-scale decoding for text generation using lattices
Jiacheng Xu, Siddhartha Reddy Jonnalagadda, Greg Durrett

Entity linking, resolution, and typing

Contrastive representation learning for cross-document coreference resolution of events and entities
Benjamin Hsu, Graham Horwood

Improving entity disambiguation by reasoning over a knowledge base
Tom Ayoola, Joseph Fisher, Andrea Pierleoni

ReFinED: An efficient zero-shot-capable approach to end-to-end entity linking
Tom Ayoola, Shubhi Tyagi, Joseph Fisher, Christos Christodoulopoulos, Andrea Pierleoni

Instilling type knowledge in language models via multi-task QA
Shuyang Li, Mukund Sridhar, Chandana Satya Prakash, Jin Cao, Wael Hamza, Julian McAuley

Explainable AI

Entailment trees.png
In "Entailment tree explanations via iterative retrieval-generation reasoner", Amazon researchers propose a method for explaining the outputs of large language models by logically recombining premises extracted from supporting textual evidence.

Entailment tree explanations via iterative retrieval-generation reasoner
Danilo Neves Ribeiro, Shen Wang, Xiaofei Ma, Rui Dong, Xiaokai Wei, Henry Zhu, Xinchi Chen, Zhiheng Huang, Peng Xu, Andrew O. Arnold, Dan Roth

Locally aggregated feature attribution on natural language model understanding
Sheng Zhang, Jin Wang, Haitao Jiang, Rui Song

Extreme multilabel classification

Augmenting training data for massive semantic matching models in low-traffic e-commerce stores
Ashutosh Joshi, Shankar Vishwanath, Choon Hui Teo, Vaclav Petricek, Vishy Vishwanathan, Rahul Bhagat, Jonathan May

Extreme zero shot learning for extreme text classification
Yuanhao Xiong, Wei-Cheng Chang, Cho-Jui Hsieh, Hsiang-Fu Yu, Inderjit S. Dhillon

Federated learning

Federated learning with noisy user feedback
Rahul Sharma, Anil Ramakrishna, Ansel MacLaughlin, Anna Rumshisky, Jimit Majmudar, Clement Chung, Salman Avestimehr, Rahul Gupta

Keyword spotting

AB/BA analysis: A framework for estimating keyword spotting recall improvement while maintaining audio privacy
Raphael Petegrosso, Vasistakrishna Baderdinni, Thibaud Senechal, Benjamin L. Bullough

Machine translation

CoCoA-MT: A dataset and benchmark for contrastive controlled MT with application to formality
Maria Nadejde, Anna Currey, Benjamin Hsu, Xing Niu, Marcello Federico, Georgiana Dinu

Dynamic pulling.png
In federated learning, distributed copies of a neural network are trained locally, and only their updates (red) are sent to a central model. "Training mixed-domain translation models via federated learning" introduces a technique called dynamic pulling, in which distributed models with large shifts in parameter values between training rounds (lower left) see their parameters pulled into the central model separately from those of models with smaller shifts.

The devil is in the details: On the pitfalls of vocabulary selection in neural machine translation
Tobias Domhan, Eva Hasler, Ke Tran, Sony Trenous, Bill Byrne, Felix Hieber

Training mixed-domain translation models via federated learning
Peyman Passban, Tanya G. Roosta, Rahul Gupta, Ankit Chadha, Clement Chung

Multitask learning

Asynchronous convergence in multi-task learning via knowledge distillation from converged tasks
Weiyi Lu, Sunny Rajagopalan, Priyanka Nigam, Jaspreet Singh, Xiaodi Sun, Yi Xu, Belinda Zeng, Trishul Chilimbi

Exploring the role of task transferability in large-scale multi-task learning
Vishakh Padmakumar, Leonard Lausen, Miguel Ballesteros, Sheng Zha, He He, George Karypis

Named-entity recognition

Dynamic gazetteer integration in multilingual models for cross-lingual and cross-domain named entity recognition
Besnik Fetahu, Anjie Fang, Oleg Rokhlenko, Shervin Malmasi

NER-MQMRC: Formulating named entity recognition as multi question machine reading comprehension
Anubhav Shrimal, Avi Jain, Kartik Mehta, Promod Yenigalla

Question answering

Answer consolidation: Formulation and benchmarking
Wenxuan Zhou, Qiang Ning, Heba Elfardy, Kevin Small, Muhao Chen

Paragraph-based transformer pre-training for multi-sentence inference
Luca Di Liello, Siddhant Garg, Luca Soldaini, Alessandro Moschitti

PerKGQA: Question answering over personalized knowledge graphs
Ritam Dutt, Kasturi Bhattacharjee, Rashmi Gangadharaiah, Dan Roth, Carolyn Penstein Rosé

Product answer generation from heterogeneous sources: A new benchmark and best practices
Xiaoyu Shen, Gianni Barlacchi, Marco Del Tredici, Weiwei Cheng, Adria de Gispert, Bill Byrne

Recommender systems

CERES: Pretraining of graph-conditioned transformer for semi-structured session data
Rui Feng, Chen Luo, Qingyu Yin, Bing Yin, Tuo Zhao, Chao Zhang

Self-learning

Failure point isolation.png
In "FPI: Failure point isolation in large-scale conversational assistants", Amazon researchers propose a method for deducing where in a conversational agent's processing pipeline an error has occurred.

FPI: Failure point isolation in large-scale conversational assistants
Rinat Khaziev, Usman Shahid, Tobias Röding, Rakesh Chada, Emir Kapanci, Pradeep Natarajan

Scalable and robust self-learning for skill routing in large-scale conversational AI systems
Mohammad Kachuee, Jinseok Nam, Sarthak Ahuja, Jin-Myung Won, Sungjin Lee

Self-aware feedback-based self-learning in large-scale conversational AI
Pragaash Ponnusamy, Clint Solomon Mathialagan, Gustavo Aguilar, Chengyuan Ma, Chenlei (Edward) Guo

Task-oriented parsing.png
An example of task-oriented semantic parsing, which converts natural language into a formal representation that an AI agent can act on. From "Compositional task-oriented parsing as abstractive question answering".

Semantic parsing

Compositional task oriented parsing as abstractive question answering
Wenting Zhao, Konstantine Arkoudas, Weiqi Sun, Claire Cardie

SeqZero: Few-shot compositional semantic parsing with sequential prompts and zero-shot models
Jingfeng Yang, Haoming Jiang, Qingyu Yin, Danqing Zhang, Bing Yin, Diyi Yang

Task adaptation

Attention fusion: A light yet efficient late fusion mechanism for task adaptation in NLU
Jin Cao, Chandana Satya Prakash, Wael Hamza

Empowering parameter-efficient transfer learning by recognizing the kernel structure in attention
Yifan Chen, Devamanyu Hazarika, Mahdi Namazifar, Yang Liu, Di Jin, Dilek Hakkani-Tür

Text mining

Distantly supervised aspect clustering and naming for e-commerce reviews
Prateek Sircar, Aniket Chakrabarti, Deepak Gupta, Anirban Majumdar

Efficient few-shot fine-tuning for opinion summarization
Arthur Bražinskas, Ramesh Nallapati, Mohit Bansal, Markus Dreyer

FactGraph: Evaluating factuality in summarization with semantic graph representations
Leonardo F. R. Ribeiro, Mengwen Liu, Iryna Gurevych, Markus Dreyer, Mohit Bansal

Knowledge selection.png
An example of how a conversational agent might incorporate facts gleaned form online sources (white boxes) into its conversational replies (blue boxes). From "Enhanced knowledge selection for grounded dialogues via document semantic graphs".

Enhanced knowledge selection for grounded dialogues via document semantic graphs
Sha Li, Madhi Namazifar, Di Jin, Mohit Bansal, Heng Ji, Yang Liu, Dilek Hakkani-Tür

Retrieval-augmented multilingual keyphrase generation with retriever-generator iterative training
Yifan Gao, Qingyu Yin, Zheng Li, Rui Meng, Tong Zhao, Bing Yin, Irwin King, Michael R. Lyu

What do users care about? Detecting actionable insights from user feedback
Kasturi Bhattacharjee, Rashmi Gangadharaiah, Kathleen McKeown, Dan Roth

Text-to-speech

Empathic machines: using intermediate features as levers to emulate emotions in text-to-speech systems
Saiteja Kosgi, Sarath Sivaprasad, Niranjan Pedanekar, Anil Nelakanti, Vineet Gandhi

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US, TX, Austin
What happens when you combine startup speed with Amazon-scale impact? You get this team. Amazon Enterprise Security Products is a newly launched group building intelligent, cloud-agnostic security tools using AI-first development practices. Here, you build AI and you build with AI — at the same time. This role is a chance to shape the future of security tooling with a small, fast team that ships like a startup but deploys at Amazon scale. We're looking for a Data Scientist who thrives at the intersection of applied ML, agentic AI, and security. You'll design and deploy models that detect threats, power intelligent agents, and make security decisions at cloud scale. You'll work shoulder-to-shoulder with SDEs, applied scientists, security researchers, and PMs on a team where the best idea wins, regardless of title or tenure. Key job responsibilities * Build the intelligence behind AI-first security products: Design, train, and ship ML models that power agentic systems, anomaly detection, threat classification, and automated response — all running across multi-cloud environments. * Own the full science lifecycle: From problem framing and data exploration through model development, evaluation, production deployment, and monitoring. You build it, you ship it, you run it. * Build with AI to build AI: Use agentic coding tools, LLM-powered workflows, and experimental AI tooling to accelerate every phase of your work; from EDA to feature engineering to model iteration. Multiply your velocity and raise the bar for what one scientist can deliver. * Power agentic architectures: Develop the models, embeddings, RAG pipelines, evaluation frameworks, and feedback loops that make multi-agent security systems smart, safe, and customer-ready. * Prototype rapidly and validate with customers: Turn hypotheses into prototypes in days, not quarters. Iterate based on real customer signal and ship what works. * Partner across disciplines: Work directly with SDEs, applied scientists, security researchers, PMs, and UX designers to turn ambiguous problems into shipped solutions. Small team means short lines between you and the decision. * Communicate with impact: Translate complex modeling results into clear recommendations for engineers, product leaders, and senior executives. Influence direction with data. * Raise the science bar: Contribute to technical and science reviews, mentor teammates, and champion AI-first development practices. Help shape the science culture of a fast-growing team from the ground floor. A day in the life No two days look the same on this fast-growing, AI-first team. You might start your morning reviewing evaluation results from overnight model training runs, then dive into building a RAG pipeline or tuning a multi-agent orchestration loop. Before lunch, you're pair-prompting with an agentic coding assistant to stand up a new feature pipeline. In the afternoon, you join a design session with senior and principal scientists and engineers where your ideas carry weight regardless of title. You own science problems end to end, ship using the latest AI-assisted workflows, and see your models reach production fast. This is where builders thrive. About the team Amazon Enterprise Security Products is built by builders who tackle challenges others might consider too ambitious. We're a small team where there are no layers between you and the decision, no waiting quarters to see your work reach customers. Every team member brings an owner's mentality. If there's a problem worth solving, we solve it. No mission is beyond reach, no detail beneath our attention. We move fast, we ship fast, and we learn from what we ship. This is where builders who want to make the impossible routine come to do their best work. Diverse Experiences Amazon Security values diverse experiences. Even if you do not meet all of the 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 Amazon Security? At Amazon, security is central to maintaining customer trust and delivering delightful customer experiences. Our organization is responsible for creating and maintaining a high bar for security across all of Amazon’s products and services. We offer talented security professionals the chance to accelerate their careers with opportunities to build experience in a wide variety of areas including cloud, devices, retail, entertainment, healthcare, operations, and physical stores. Inclusive Team Culture In Amazon Security, it’s in our nature to learn and be curious. Ongoing DEI events and learning experiences inspire us to continue learning and to embrace our uniqueness. Addressing the toughest security challenges requires that we seek out and celebrate a diversity of ideas, perspectives, and voices. Training & 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, training, 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 flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve.
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