Papers by Nicholas Andrews
Learning Invariant Representations of Social Media Users (D19-1)
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| Challenge: | Existing methods for learning to compare social media users fail to generalize to new users or even to previously known users. |
| Approach: | They propose a procedure to learn a mapping from short episodes of user activity to a vector space in which the distance between points captures the similarity of the corresponding users’ invariant features. |
| Outcome: | The proposed procedure can be applied to users not seen at training time and enables efficient comparisons of users in the resulting vector space. |
Mitigating Paraphrase Attacks on Machine-Text Detection via Paraphrase Inversion (2025.findings-acl)
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| Challenge: | Paraphrases applied to machine-generated texts can degrade performance of machine-text detectors. |
| Approach: | They propose an approach which frames the problem as translation from paraphrased text back to the original text. |
| Outcome: | The proposed approach yields an average improvement of +22% AUROC across seven detectors and three different domains. |
Multi-Task Transfer Matters During Instruction-Tuning (2024.findings-acl)
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| Challenge: | Instruction-tuning improves a model’s ability to learn in-context, but the mechanisms that drive in-constext learning are poorly understood. |
| Approach: | They propose to train a model on hundreds of tasks to improve its ability to learn in-context. |
| Outcome: | The proposed methods improve model transfer and in-context generalization, suggesting catastrophic forgetting may impact in-constext learning. |
Learning Universal Authorship Representations (2021.emnlp-main)
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Rafael A. Rivera-Soto, Olivia Elizabeth Miano, Juanita Ordonez, Barry Y. Chen, Aleem Khan, Marcus Bishop, Nicholas Andrews
| Challenge: | authorship verification has traditionally relied on modeling stylometric linguistic properties . but neural methods introduce a tradeoff: they obviate the need for manual feature design . |
| Approach: | They propose to use domain-specific features to improve authorship representations . they propose to study Amazon reviews, fanfiction short stories, and Reddit comments . |
| Outcome: | The proposed methods outperform existing methods in large-scale authorship verification scenarios. |
CommonLID: Re-evaluating State-of-the-Art Language Identification Performance on Web Data (2026.acl-long)
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Pedro Ortiz Suarez, Laurie Burchell, Catherine Arnett, Rafael Mosquera, Sara Hincapié Monsalve, Thom Vaughan, Damian Stewart, Malte Ostendorff, Idris Abdulmumin, Vukosi Marivate, Shamsuddeen Hassan Muhammad, Atnafu Lambebo Tonja, Hend Al-Khalifa, Nadia Ghezaiel Hammouda, Verrah Akinyi Otiende, Tack Hwa Wong, Jakhongir Saydaliev, Melika Nobakhtian, Muhammad Ravi Shulthan Habibi, Chalamalasetti Kranti, Carol Muchemi, Khang Nguyen, Faisal Muhammad Adam, Luis Frentzen Salim, Reem Alqifari, Cynthia Jayne Amol, Joseph Marvin Imperial, Ilker Kesen, Ahmad Mustafid, Pavel Stepachev, Leshem Choshen, David Anugraha, Hamada Nayel, Seid Muhie Yimam, Vallerie Alexandra Putra, My Chiffon Nguyen, Azmine Toushik Wasi, Gouthami Vadithya, Rob Van Der Goot, Lanwenn ar C’horr, Karan Dua, Andrew Yates, Mithil Bangera, Yeshil Bangera, Hitesh Laxmichand Patel, Shu Okabe, Fenal Ashokbhai Ilasariya, Dmitry Gaynullin, Genta Indra Winata, Yiyuan Li, Juan Pablo Martínez, Amit Agarwal, Ikhlasul Akmal Hanif, Raia Abu Ahmad, Esther Adenuga, Filbert Aurelian Tjiaranata, Weerayut Buaphet, Michael Anugraha, Sowmya Vajjala, Benjamin L Rice, Azril Hafizi Amirudin, Jesujoba Oluwadara Alabi, Srikant Panda, Yassine Toughrai, Bruhan Kyomuhendo, Daniel Ruffinelli, null Akshata, Manuel Goulão, Ej Zhou, Ingrid Gabriela Franco Ramirez, Cristina Aggazzotti, Konstantin Dobler, Jun Kevin, Quentin Pagès, Nicholas Andrews, Nuhu Ibrahim, Mattes Ruckdeschel, Amr Keleg, Mike Zhang, Casper Rufaro Muziri, Saron Samuel, Sotaro Takeshita, Kun Kerdthaisong, Luca Foppiano, Rasul Dent, Tommaso Green, Ahmad Mustapha Wali, Kamohelo Makaaka, Vicky Feliren, Inshirah Idris, Hande Celikkanat, Abdulhamid Abubakar, Jean Maillard, Benoît Sagot, Thibault Clérice, Kenton Murray, Sarah K. K. Luger
| Challenge: | Language identification (LID) is a fundamental step in curating multilingual corpora. |
| Approach: | They introduce CommonLID, a community-driven, human-annotated LID benchmark for the web domain, covering 109 languages. |
| Outcome: | The proposed benchmark covers 109 languages and shows that existing evaluations overestimate accuracy for many languages in the web domain. |
AnaloBench: Benchmarking the Identification of Abstract and Long-context Analogies (2024.emnlp-main)
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Xiao Ye, Andrew Wang, Jacob Choi, Yining Lu, Shreya Sharma, Lingfeng Shen, Vijay Murari Tiyyala, Nicholas Andrews, Daniel Khashabi
| Challenge: | Analogical reasoning is an important part of human communication, says a new study . a benchmark to determine analogical reasoning ability in language models is needed . |
| Approach: | They propose to benchmark analogical reasoning ability in language models by collecting 340 analogies from human writings. |
| Outcome: | The proposed benchmark aims to determine analogical reasoning ability in language models. |
Learning to Compare Financial Reports for Financial Forecasting (2024.findings-eacl)
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| Challenge: | Public companies in the US are required to publish annual reports that contain over 25,000 words across all sections and a high percentage of boilerplate content that does not change much year-to-year. |
| Approach: | They propose to model complex, cross-document relationships between financial reports using paired financial reports. |
| Outcome: | The proposed model can predict company risk and correlation from financial reports . the proposed model is able to recognize complex, nuanced relationships with complex signals . |
A Deep Metric Learning Approach to Account Linking (2021.naacl-main)
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| Challenge: | Existing methods to identify abusive content may fail to adapt to new trends, and individual posts may fail . |
| Approach: | They propose a method that embeds variable-sized samples of user activity into a vector space, where samples by the same author map to nearby points. |
| Outcome: | The proposed model outperforms several competitive baselines under a new evaluation framework modeled after established benchmarks in other domains. |
Forecasting Earnings Surprises from Conference Call Transcripts (2023.findings-acl)
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| Challenge: | Earnings conference calls contain over 5,000 words of text and large amounts of industry jargon . this length and domain-specific language present problems for generic pretrained language models. |
| Approach: | They propose a task of predicting earnings surprises from earnings call transcripts and propose linguistic models that use a long document dataset to test financial understanding. |
| Outcome: | The proposed model can predict earnings surprises from earnings conference calls with reasonable accuracy and shows that it is possible to interpret the data with different interpretability methods. |
Can Authorship Representation Learning Capture Stylistic Features? (2023.tacl-1)
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Andrew Wang, Cristina Aggazzotti, Rebecca Kotula, Rafael Rivera Soto, Marcus Bishop, Nicholas Andrews
| Challenge: | Existing methods to disentangle an author's style from the content of their writing are limited by the reliance on human labels and the narrow focus of stylistic distinctions. |
| Approach: | They propose to use a surrogate task to learn authorship representations that are sensitive to writing style and to validate their hypothesis . |
| Outcome: | The proposed representations are sensitive to writing style and may be robust to topic drift over time. |
Do Text-to-Text Multi-Task Learners Suffer from Task Conflict? (2022.findings-emnlp)
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| Challenge: | Existing multi-task learning architectures learn a single model across multiple tasks through a shared encoder followed by task-specific decoders. |
| Approach: | They propose to use a shared encoder and language model decoder to learn a single model across multiple tasks. |
| Outcome: | The proposed architecture does surprisingly well across a range of diverse tasks. |
Sources of Transfer in Multilingual Named Entity Recognition (2020.acl-main)
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| Challenge: | naive training of named-entity recognition models using annotated data from multiple languages consistently underperforms monolingual models. |
| Approach: | They propose a polyglot named-entity recognition model where one model is trained using annotated data drawn from multiple languages. |
| Outcome: | The proposed model outperforms models trained on monolingual data despite more training data . the proposed model shares many parameters across languages and fine-tunes them to outperFORM monolingual models. |
Ensemble Distillation for Structured Prediction: Calibrated, Accurate, Fast—Choose Three (2020.emnlp-main)
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| Challenge: | Modern neural networks do not always produce wellcalibrated predictions . post-hoc calibration methods require a held-out calibration dataset, which may not be available in all circumstances. |
| Approach: | They validate ensemble distillation framework for producing well-calibrated structured prediction models without the prohibitive inference-time cost of ensembles. |
| Outcome: | The proposed framework produces well-calibrated predictions without the prohibitive inference-time cost of ensembles. |
Financial Forecasting from Textual and Tabular Time Series (2024.findings-emnlp)
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| Challenge: | Existing models that combine multiple data sources and combine them to form accurate financial predictions are challenging to model without inductive biases. |
| Approach: | They propose to use numerical financial results, macroeconomic states, and long financial documents to model company earnings relative to analyst expectations. |
| Outcome: | The proposed model outperforms existing models in a simulated trading environment and demonstrates that each modality contains unique information. |