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A quick guide to Amazon's 65-plus papers at this year's ACL

Familiar topics such as question answering and natural-language understanding remain well represented, but a new concentration on language modeling and multimodal models reflect the spread of generative AI.

By Staff writer
July 10, 2023
10 min read
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Between the main conference and the recently inaugurated ACL Proceedings, Amazon researchers have more than 65 papers at this year's meeting of the Association for Computational Linguistics (ACL).

Automatic speech recognition

Masked audio text encoders are effective multi-modal rescorers*
Jason Cai, Monica Sunkara, Xilai Li, Anshu Bhatia, Xiao Pan, Sravan Bodapati

Code generation

A static evaluation of code completion by large language models
Hantian Ding, Varun Kumar, Yuchen Tian, Zijian Wang, Rob Kwiatkowski, Xiaopeng LI, Murali Krishna Ramanathan, Baishakhi Ray, Parminder Bhatia, Sudipta Sengupta, Dan Roth, Bing Xiang

Multitask pretraining with structured knowledge for text-to-SQL generation
Robert Giaquinto, Dejiao Zhang, Benjamin Kleiner, Yang Li, Ming Tan, Parminder Bhatia, Ramesh Nallapati, Xiaofei Ma

Code switching

Code-switched text synthesis in unseen language pairs*
I-Hung Hsu, Avik Ray, Shubham Garg, Nanyun Peng, Jing Huang

Code-switched text synthesis.png
From "Code-switched text synthesis in unseen language pairs".

CoMix: Guide transformers to code-mix using POS structure and phonetics*
Gaurav Arora, Srujana Merugu, Vivek Sembium

Continual learning

Characterizing and measuring linguistic dataset drift
Tyler A. Chang, Kishaloy Halder, Neha Anna John, Yogarshi Vyas, Yassine Benajiba, Miguel Ballesteros, Dan Roth

Data-/table-to-text applications

An inner table retriever for robust table question answering
Weizhe Lin, Rexhina Blloshmi, Bill Byrne, Adrià de Gispert, Gonzalo Iglesias

Few-shot data-to-text generation via unified representation and multi-source learning
Alexander Hanbo Li, Mingyue Shang, Evangelia Spiliopoulou, JIE MA, Patrick Ng, Zhiguo Wang, Bonan Min, William Wang, Kathleen McKeown, Vittorio Castelli, Dan Roth, Bing Xiang

Improving cross-task generalization of unified table-to-text models with compositional task configurations*
Jifan Chen, Yuhao Zhang, Lan Liu, Rui Dong, Xinchi Chen, Patrick Ng, William Wang, Zhiheng Huang

LI-RAGE: Late interaction retrieval augmented generation with explicit signals for open-domain table question answering
Weizhe Lin, Rexhina Blloshmi, Bill Byrne, Adrià de Gispert, Gonzalo Iglesias

Dialogue

Diable: Efficient dialogue state tracking as operations on tables*
Pietro Lesci, Yoshinari Fujinuma, Momchil Hardalov, Chao Shang, Lluis Marquez

NatCS: Eliciting natural customer support dialogues
James Gung, Emily Moeng, Wesley Rose, Arshit Gupta, Yi Zhang, Saab Mansour

Schema-guided user satisfaction modeling for task-oriented dialogues
Yue Feng, Yunlong Jiao, Animesh Prasad, Nikolaos Aletras, Emine Yilmaz, Gabriella Kazai

Toward more accurate and generalizable evaluation metrics for task-oriented dialogs
Abi Komma, Nagesh Panyam, Timothy Leffel, Anuj Goyal, Angeliki Metallinou, Spyros Matsoukas, Aram Galstyan

Explainable AI

Efficient Shapley values estimation by amortization for text classification
Alan Yang, Fan Yin, He He, Kai-Wei Chang, Xiaofei Ma, Bing Xiang

Efficient Shapley values.png
From "Efficient Shapley values estimation by amortization for text classification".

Few shot rationale generation using self-training with dual teachers*
Aditya Srikanth Veerubhotla, Lahari Poddar, Jun Yin, Gyuri Szarvas, Sharanya Eswaran

Information extraction

An AMR-based link prediction approach for document-level event argument extraction
Yuqing Yang, Qipeng Guo, Xiangkun Hu, Yue Zhang, Qipeng Guo, Zheng Zhang

AVEN-GR: Attribute value extraction and normalization using product graphs
Donato Crisostomi, Thomas Ricatte

Large scale generative multimodal attribute extraction for e-commerce attributes
Anant Khandelwal, Happy Mittal, Shreyas Sunil Kulkarni, Deepak Gupta

ParaAMR: A large-scale syntactically diverse paraphrase dataset by AMR back-translation
Kuan-Hao Huang, Varun Iyer, I-Hung Hsu, Anoop Kumar, Kai-Wei Chang, Aram Galstyan

ParaAMR.png
From "ParaAMR: A large-scale syntactically diverse paraphrase dataset by AMR back-translation".

Weakly supervised hierarchical multi-task classification of customer questions
Jitenkumar Rana, Promod Yenigalla, Chetan Aggarwal, Sandeep Mukku, Manan Soni, Rashmi Patange

WebIE: Faithful and robust information extraction on the web
Chenxi Whitehouse, Clara Vania, Alham Fikri Aji, Christos Christodoulopoulos, Andrea Pierleoni

Information retrieval

CUPID: Curriculum learning based real-time prediction using distillation
Arindam Bhattacharya, Ankith M S, Ankit Gandhi, Vijay Huddar, Atul Saroop, Rahul Bhagat

Direct fact retrieval from knowledge graphs without entity linking
Jinheon Baek, Alham Fikri Aji, Jens Lehmann, Sung Ju Hwang

Language modeling

Adaptation approaches for nearest neighbor language models*
Rishabh Bhardwaj, George Polovets, Monica Sunkara

CONTRACLM: Contrastive learning for causal language model
Nihal Jain, Dejiao Zhang, Wasi Ahmad, Zijian Wang, Feng Nan, Xiaopeng LI, Ming Tan, Baishakhi Ray, Parminder Bhatia, Xiaofei Ma, Ramesh Nallapati, Bing Xiang

Controlled text generation with hidden representation transformations*
Vaibhav Kumar, Hana Koorehdavoudi, Masud Moshtaghi, Amita Misra, Ankit Chadha, Emilio Ferrara

KILM: Knowledge injection into encoder-decoder language models
Yan XU, Mahdi Namazifar, Devamanyu Hazarika, Aishwarya Padmakumar, Yang Liu, Dilek Hakkani-Tür

KILM.png
From "KILM: Knowledge injection into encoder-decoder language models".

ReAugKD: Retrieval-augmented knowledge distillation for pre-trained language models
Jianyi Zhang, Aashiq Muhamed, Aditya Anantharaman, Guoyin Wang, Changyou Chen, Kai Zhong, Qingjun Cui, Yi Xu, Belinda Zeng, Trishul Chilimbi, Yiran Chen

Recipes for sequential pre-training of multilingual encoder and seq2seq models*
Saleh Soltan, Andy Rosenbaum, Tobias Falke, Qin Lu, Anna Rumshisky, Wael Hamza

Rethinking the role of scale for in-context learning: An interpretability-based case study at 66 billion scale
Hritik Bansal, Karthik Gopalakrishnan, Saket Dingliwal, Sravan Bodapati, Katrin Kirchhoff, Dan Roth

Machine learning

Mitigating the burden of redundant datasets via batch-wise unique samples and frequency-aware losses
Donato Crisostomi, Andrea Caciolai, Alessandro Pedrani, Alessandro Manzotti, Enrico Palumbo, Kay Rottmann, Davide Bernardi

Machine translation

RAMP: Retrieval and attribute-marking enhanced prompting for attribute-controlled translation
Gabriele Sarti, Phu Mon Htut, Xing Niu, Benjamin Hsu, Anna Currey, Georgiana Dinu, Maria Nădejde

Multimodal models

Benchmarking diverse-modal entity linking with generative models*
Sijia Wang, Alexander Li, Henry Zhu, Sheng Zhang, Pramuditha Perera, Chung-Wei Hang, JIE MA, William Wang, Zhiguo Wang, Vittorio Castelli, Bing Xiang, Patrick Ng

Generate then select: Open-ended visual question answering guided by world knowledge*
Xingyu Fu, Sheng Zhang, Gukyeong Kwon, Pramuditha Perera, Henry Zhu, Yuhao Zhang, Alexander Hanbo Li, William Wang, Zhiguo Wang, Vittorio Castelli, Patrick Ng, Dan Roth, Bing Xiang

KG-FLIP: Knowledge-guided fashion-domain language-image pre-training for e-commerce
Qinjin Jia, Yang Liu, Shaoyuan Xu, Huidong Liu, Daoping Wu, Jinmiao Fu, Roland Vollgraf, Bryan Wang

Resolving ambiguities in text-to-image generative models
Ninareh Mehrabi, Palash Goyal, Apurv Verma, Jwala Dhamala, Varun Kumar, Qian Hu, Kai-Wei Chang, Richard Zemel, Aram Galstyan, Rahul Gupta

Translation-enhanced multilingual text-to-image generation
Yaoyiran Li, Ching-Yun (Frannie) Chang, Stephen Rawls, Ivan Vulić, Anna Korhonen

Unsupervised melody-to-lyric generation
Yufei Tian, Anjali Narayan-Chen, Shereen Oraby, Alessandra Cervone, Chenyang Tao, Gunnar Sigurdsson, Wenbo Zhao, Tagyoung Chung, Jing Huang, Violet Peng

Unsupervised melody-to-lyric.png
From "Unsupervised melody-to-lyric generation".

Natural-language processing

Multi-VALUE: A framework for cross-dialectal English NLP
Caleb Ziems, William Held, Jingfeng Yang, Jwala Dhamala, Rahul Gupta, Diyi Yang

vONTSS: vMF based semi-supervised neural topic modeling with optimal transport*
Weijie Xu, Xiaoyu Jiang, Srinivasan Sengamedu, "SHS", Francis Iannacci, Jinjin Zhao

Natural-language understanding

ECG-QALM: Entity-controlled synthetic text generation using contextual Q&A for NER*
Karan Aggarwal, Henry Jin, Aitzaz Ahmad

Entity contrastive learning in a large-scale virtual assistant system
Jonathan Rubin, Jason Crowley, George Leung, Morteza Ziyadi, Maria Minakova

EPIC: Multi-perspective annotation of a corpus of irony
Simona Frenda, Alessandro Pedrani, Valerio Basile, Soda Marem Lo, Alessandra Teresa Cignarella, Raffaella Panizzon, Cristina Marco, Bianca Scarlini, Viviana Patti, Cristina Bosco, Davide Bernardi

Measuring and mitigating local instability in deep neural networks*
Arghya Datta, Subhrangshu Nandi, Jingcheng Xu, Greg Ver Steeg, He Xie, Anoop Kumar, Aram Galstyan

Reducing cohort bias in natural language understanding systems with targeted self-training scheme
Thu Le, Gabriela Cortes Hernandez, Bei Chen, Melanie Bradford

Privacy

Controlling the extraction of memorized data from large language models via prompt-tuning
Mustafa Ozdayi, Charith Peris, Jack G. M. FitzGerald, Christophe Dupuy, Jimit Majmudar, Haidar Khan, Rahil Parikh, Rahul Gupta

Query rewriting

Context-aware query rewriting for improving users’ search experience on e-commerce websites
Simiao Zuo, Qingyu Yin, Haoming Jiang, Shaohui Xi, Bing Yin, Chao Zhang, Tuo Zhao

Unified contextual query rewriting
Yingxue Zhou, Jie Hao, Mukund Rungta, Yang Liu, Eunah Cho, Xing Fan, Yanbin Lu, Vishal Vasudevan, Kellen Gillespie, Zeynab Raeesy, Sawyer Shen, Edward Guo, Gokhan Tur

Question answering

Accurate training of web-based question answering systems with feedback from ranked users
Liang Wang, Ivano Lauriola, Alessandro Moschitti

Context-aware transformer pre-training for answer sentence selection
Luca Di Liello, Siddhant Garg, Alessandro Moschitti

Cross-Lingual Knowledge Distillation for answer sentence selection in low-resource languages*
Shivanshu Gupta, Yoshitomo Matsubara, Ankit Chadha, Alessandro Moschitti

Exploiting abstract meaning representation for open-domain question answering*
Cunxiang Wang, Zhikun Xu, Qipeng Guo, Xiangkun Hu, Xuefeng Bai, Zheng Zhang, Yue Zhang

Exploiting abstract meaning representation.png
From "Exploiting abstract meaning representation for open-domain question answering".

Hybrid hierarchical retrieval for open-domain question answering*
Manoj Ghuhan Arivazhagan, Lan Liu, Peng Qi, Xinchi Chen, William Wang, Zhiheng Huang

Learning answer generation using supervision from automatic question answering evaluators
Matteo Gabburo, Siddhant Garg, Rik Koncel-Kedziorski, Alessandro Moschitti

RobustQA: Benchmarking the robustness of domain adaptation for open-domain question answering*
Rujun Han, Peng Qi, Yuhao Zhang, Lan Liu, Juliette Burger, William Wang, Zhiheng Huang, Bing Xiang, Dan Roth

Reasoning

FolkScope: Intention knowledge graph construction for e-commerce commonsense discovery*
Changlong Yu, Weiqi Wang, Xin Liu, Jiaxin Bai, Yangqiu Song, Zheng Li, Yifan Gao, Tianyu Cao, Bing Yin

SCOTT: Self-consistent chain-of-thought distillation
Peifeng Wang, Zhengyang Wang, Zheng Li, Yifan Gao, Bing Yin, Xiang Ren

Self-learning

Constrained policy optimization for controlled self-learning in conversational AI systems
Mohammad Kachuee, Sungjin Lee

Scalable and safe remediation of defective actions in self-learning conversational systems
Sarthak Ahuja, Mohammad Kachuee, Fateme Sheikholeslami, Weiqing Liu, Jae Do

Semantic parsing

An empirical analysis of leveraging knowledge for low-resource task-oriented semantic parsing*
Mayank Kulkarni, Aoxiao Zhong, Nicolas Guenon Des Mesnards, Sahar Movaghati, Mukund Harakere, He Xie, Jianhua Lu

XSEMPLR: Cross-lingual semantic parsing in multiple natural languages and meaning representations
Yusen Zhang, Jun Wang, Zhiguo Wang, Rui Zhang

XSemPLR.png
From "XSEMPLR: Cross-lingual semantic parsing in multiple natural languages and meaning representations".

Spoken-language understanding

Regression-free model updates for spoken language understanding
Andrea Caciolai, Verena Weber, Tobias Falke, Alessandro Pedrani, Davide Bernardi

Sharing encoder representations across languages, domains and tasks in large-scale spoken language understanding
Jonathan Hueser, Judith Gaspers, Thomas Gueudre, Chandana Satya Prakash, Jin Cao, Daniil Sorokin, Quynh Do, Nicolas Anastassacos, Tobias Falke, Turan Gojayev, Mariusz Momotko, Denis Romasanta Rodriguez, Austin Doolittle, Kartik Balasubramaniam, Wael Hamza, Fabian Triefenbach, Patrick Lehnen

Toxic-language classification

QCon at SemEval-2023 Task 10: Data augmentation and model ensembling for detection of online sexism
Wes Feely, Prabhakar Gupta, Manas Mohanty, Tim Chon, Tuhin Kundu, Vijit Singh, Sandeep Atluri, Tanya Roosta, Viviane Ghaderi, Peter Schulam, Heba Elfardy

Towards building a robust toxicity predictor
Dmitriy Bespalov, Sourav Bhabesh, Yi Xiang, Yanjun (Jane) Qi

*Accepted to ACL Findings

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Data Scientist II - AMZ27351.1
US, CA, San Francisco
Employer: Amazon Web Services, Inc. Position: Data Scientist II - AMZ27351.1 Location: San Francisco, CA Multiple Positions Available: Design and implement scalable and reliable approaches to support or automate decision making throughout the business. Apply a range of data science techniques and tools combined with subject matter expertise to solve difficult business problems and cases in which the solution approach is unclear. Acquire data by building the necessary SQL / ETL queries. Import processes through various company specific interfaces for accessing Oracle, RedShift, and Spark storage systems. Build relationships with stakeholders and counterparts. Analyze data for trends and input validity by inspecting univariate distributions, exploring bivariate relationships, constructing appropriate transformations, and tracking down the source and meaning of anomalies. Build models using statistical modeling, mathematical modeling, econometric modeling, network modeling, social network modeling, natural language processing, machine learning algorithms, genetic algorithms, and neural networks. Validate models against alternative approaches, expected and observed outcome, and other business defined key performance indicators. Implement models that comply with evaluations of the computational demands, accuracy, and reliability of the relevant ETL processes at various stages of production. (40 hours / week, 8:00am-5:00pm, Salary Range $175425 - $212800) Amazon.com is an Equal Opportunity – Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation
Applied Scientist, AWS Automated Reasoning
GB, London
The Agentic Automated Reasoning Group is building the next generation of software verification tools combining advances in artificial intelligence, the computational capacity of the cloud, and our deep expertise in the domain. Join us if you want to be a part of this transformational endeavor. The Strata team (https://github.com/strata-org) is seeking an applied scientist with broad interest and expertise in model checking, interactive theorem proving, programming language semantics, and generative AI. You will combine your expertise with that of your coworkers to build new tools that solve code analysis problems previously considered beyond reach. Our application areas span all the way from Infrastructure as Code to high-performance cryptography written in assembly code, while our methods span from interactive theorem proving to automated test generation. Each day, hundreds of thousands of developers make billions of transactions worldwide on AWS. They harness the power of the cloud to enable innovative applications, websites, and businesses. Using automated reasoning technology and mathematical proofs, AWS allows customers to answer questions about security, availability, durability, and functional correctness. We call this provable security, absolute assurance in security of the cloud and in the cloud. https://aws.amazon.com/security/provable-security/ Key job responsibilities Work with customer teams to understand the nature of their software and the properties they need to establish of it. Identify tools and methods capable of addressing the verification needs of customers, including any novel analysis capabilities required. Use techniques spanning property-based testing to model checkers, and interactive theorem provers to establish program properties. Explore generative AI techniques to help customers formalize their requirements, find revealing tests, generate required boiler plate for testing and model checking, and find and repair program proofs. About the team The Agentic Automated Reasoning Group at AWS develops and applies state of the art formal methods and automated reasoning techniques to ensure the security, reliability, and correctness of AWS services and customer applications, with a strong focus on AI based agents. Our work innovates tools and services to perform verification at scale and apply them to build safe and secure systems at AWS. We are also pioneering the use of formal verification and automated reasoning to develop agentic systems, ensuring AI agents operate within defined safety boundaries.
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