I'm a Senior Research Scientist at the JHU HLTCOE and an Assistant Research Scientist in the Department of Computer Science. I also work with PhD students and faculty at the Center for Language and Speech Processing.
PhD students:
David Mueller (co-advised by Mark Dredze)
Aleem Khan (co-advised by Benjamin Van Durme)
Sophia Hager (co-advised by Kevin Duh)
Rafael Rivera-Soto
Andrew Wang (co-advised by Daniel Khashabi)
Postdocs:
Cristina Aggazzotti
Zexin Cai (co-advised with Matthew Wiesner)
Papers
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HLTCOE Submission to the 2024 Voice Privacy Challenge. VPC (2024)
Henry Li Xinyuan, Zexin Cai, Ashi Garg, Leibny Paola Garcia-Perera, Kevin Duh, Sanjeev Khudanpur, Nicholas Andrews, and Matthew Wiesner
[pdf] [Best paper award] -
Privacy versus Emotion Preservation Trade-Offs in Emotion-Preserving Speaker Anonymization. SLT (2024)
Zexin Cai, Henry Li Xinyuan, Ashi Garg, Leibny Paola Garcia-Perera, Kevin Duh, Sanjeev Khudanpur, Nicholas Andrews, and Matthew Wiesner
[arxiv] [pdf] -
Can Optimization Trajectories Explain Multi-Task Transfer? Preprint (2024)
David Mueller, Mark Dredze, Nicholas Andrews
[arxiv] [pdf] -
Multi-Task Transfer Matters During Instruction-Tuning. ACL Findings (2024)
David Mueller, Mark Dredze, Nicholas Andrews
[pdf] -
AnaloBench: Benchmarking the Identification of Abstract and Long-context Analogies. Preprint (2024)
Xiao Ye, Andrew Wang, Jacob Choi, Yining Lu, Shreya Sharma, Lingfeng Shen, Vijay Tiyyala, Nicholas Andrews, Daniel Khashabi
[arxiv] [pdf] -
Can Authorship Attribution Models Distinguish Speakers in Speech Transcripts? TACL (2024)
Cristina Aggazzotti, Nicholas Andrews, Elizabeth Allyn Smith
[arxiv] [pdf] -
Learning to Compare Financial Reports for Financial Forecasting. Findings of EACL (2024)
Ross Koval, Nicholas Andrews, and Xifeng Yan
-
Few-Shot Detection of Machine-Generated Text using Style Representations. ICLR (2024)
Rafael Rivera Soto, Kailin Koch, Aleem Khan, Barry Chen, Marcus Bishop, Nicholas Andrews
[arxiv] [pdf] [code] [demo] -
Learning to Generate Text in Arbitrary Writing Styles. Preprint (2023)
Aleem Khan, Andrew Wang, Sophia Hager, Nicholas Andrews
[arxiv] [pdf] -
Can Authorship Representation Learning Capture Stylistic Features? TACL (2023)
Andrew Wang, Cristina Aggazzotti, Rebecca Kotula, Rafael Rivera-Soto, Marcus Bishop, and Nicholas Andrews
[arxiv] [pdf] -
Forecasting Earnings Surprises from Conference Call Transcripts. Findings of ACL (2023)
Ross Koval, Nicholas Andrews, and Xifeng Yan
[aclanthology] [pdf] - Low-Resource Authorship Style Transfer: Can Non-Famous Authors Be Imitated? Preprint (2023)
Ajay Patel, Nicholas Andrews, Chris Callison-Burch [arxiv] [pdf] -
The Importance of Temperature in Multi-Task Optimization. OPT Workshop @ NeurIPS (2022)
David Mueller, Mark Dredze, and Nicholas Andrews
[pdf] -
Do Text-to-Text Multi-Task Learners Suffer from Task Conflict? Findings of EMNLP (2022)
David Mueller, Mark Dredze, and Nicholas Andrews
[pdf] -
Learning Universal Authorship Representations. EMNLP (2021)
Rafael Rivera-Soto, Olivia Miano, Juanita Ordonez, Barry Chen, Aleem Khan, Marcus Bishop and Nicholas Andrews
[pdf] -
A Deep Metric Learning Approach to Account Linking. NAACL (2021)
Aleem Khan, Elizabeth Fleming, Noah Schofield, Marcus Bishop, Nicholas Andrews
[aclweb] [arxiv] [code + data] -
Ensemble Distillation for Structured Prediction: Calibrated, Accurate, Fast - Choose Three. EMNLP (2020)
Steven Reich, David Mueller, Nicholas Andrews
[aclweb] [arxiv] [code] -
Sources of Transfer in Multilingual Named Entity Recognition. ACL (2020)
David Mueller, Nicholas Andrews, Mark Dredze
[aclweb] [paper] [bib] [arxiv] [video] -
Compressing BERT: Studying the Effects of Weight Pruning on Transfer Learning. RepL4NLP (2020)
Mitchell A Gordon, Kevin Duh, Nicholas Andrews
[aclweb] [paper] [bib] [arxiv] [video] -
Learning Invariant Representations of Social Media Users. EMNLP (2019)
Nicholas Andrews and Marcus Bishop
[aclweb] [arxiv] [code] [data] [video] -
Convolutions Are All You Need (For Classifying Character Sequences). EMNLP WNUT (2018)
Zach Wood-Doughty, Nicholas Andrews, and Mark Dredze
[pdf] -
Predicting Twitter User Demographics from Names Alone. NAACL PEOPLES (2018)
Zach Wood-Doughty, Nicholas Andrews, Rebecca Marvin, and Mark Dredze
[pdf] -
Bayesian Modeling of Lexical Resources for Low-Resource Settings. ACL (2017)
Nicholas Andrews, Mark Dredze, Benjamin Van Durme, and Jason Eisner
[pdf] [code] [slides] -
Twitter at the Grammys: A Social Media Corpus for Entity Linking and Disambiguation.
SocialNLP (2016)
Mark Dredze, Nicholas Andrews and Jay DeYoung [pdf] -
Generative Non-Markov Models for Information Extraction. Dissertation (2015)
Nicholas Andrews (advised by Jason Eisner and Mark Dredze) -
Robust Entity Clustering via Phylogenetic Inference. ACL (2014)
Nicholas Andrews, Jason Eisner, and Mark Dredze
[pdf] [full paper] [bib] [code] [slides] -
PARMA: A Predicate Argument Aligner. ACL (2013)
Travis Wolfe, Benjamin Van Durme, Mark Dredze, Nicholas Andrews, Charley Beller, Chris Callison-Burch, Jay DeYoung, Justin Snyder, Jonathan Weese, Tan Xu, and Xuchen Yao
[pdf] [bib] -
Name Phylogeny: A Generative Model of String Variation. EMNLP (2012)
Nicholas Andrews, Jason Eisner, and Mark Dredze
[pdf] [bib] -
Entity Clustering Across Languages. NAACL (2012)
Spence Green, Nicholas Andrews, Matthew R. Gormley, Mark Dredze,
and Christopher D. Manning
[pdf] -
Transformation Process Priors. NeuIPS NP Bayes (2011)
Nicholas Andrews and Jason Eisner
[pdf] [bib] -
Seeded Discovery of Base Relations in Large Corpora. EMNLP (2008)
Nicholas Andrews and Naren Ramakrishnan
[pdf] [bib] -
Recent Developments in Document Clustering. TR (2008)
Nicholas Andrews and Edward A. Fox
[pdf]