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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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Machine Learning Engineer, WWPS ProServe Data and Machine Learning
US, VA, Herndon
The Amazon Web Services Professional Services (ProServe) team is seeking a skilled Machine Learning Engineer to join our team at Amazon Web Services (AWS). Are you looking to work at the forefront of Machine Learning and AI? Would you be excited to apply Generative AI algorithms to solve real world problems with significant impact? In this role, you'll work directly with customers to design, evangelize, implement, and scale AI/ML solutions that meet their technical requirements and business objectives. You'll be a key player in driving customer success through their AI transformation journey, providing deep expertise in machine learning, generative AI, and best practices throughout the project lifecycle. As a Machine Learning Engineer within the AWS Professional Services organization, you will be proficient in architecting complex, scalable, and secure machine learning solutions tailored to meet the specific needs of each customer. You'll help customers imagine and scope the use cases that will create the greatest value for their businesses, select and train and fine tune the right models, and define paths to navigate technical or business challenges. Working closely with stakeholders, you'll assess current data infrastructure, develop proof-of-concepts, and propose effective strategies for implementing AI and generative AI solutions at scale. You will design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. The AWS Professional Services organization is a global team of experts that help customers realize their desired business outcomes when using the AWS Cloud. We work together with customer teams and the AWS Partner Network (APN) to execute enterprise cloud computing initiatives. Our team provides assistance through a collection of offerings which help customers achieve specific outcomes related to enterprise cloud adoption. We also deliver focused guidance through our global specialty practices, which cover a variety of solutions, technologies, and industries. This position requires that the candidate selected must currently possess and maintain an active TS/SCI security clearance with polygraph. Key job responsibilities - Designing and implementing complex, scalable, and secure AI/ML solutions on AWS tailored to customer needs, including selecting and fine-tuning appropriate models for specific use cases - Developing and deploying machine learning models and generative AI applications that solve real-world business problems, conducting experiments and optimizing for performance at scale - Collaborating with customer stakeholders to identify high-value AI/ML use cases, gather requirements, and propose effective strategies for implementing machine learning and generative AI solutions - Providing technical guidance on applying AI, machine learning, and generative AI responsibly and cost-efficiently, troubleshooting throughout project delivery and ensuring adherence to best practices - Acting as a trusted advisor to customers on the latest advancements in AI/ML, emerging technologies, and innovative approaches to leveraging diverse data sources for maximum business impact - Sharing knowledge within the organization through mentoring, training, creating reusable AI/ML artifacts, and working with team members to prototype new technologies and evaluate technical feasibility About the team 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. 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. 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. 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.
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