1 code implementation • EMNLP 2021 • Rafael A. Rivera-Soto, Olivia Elizabeth Miano, Juanita Ordonez, Barry Y. Chen, Aleem Khan, Marcus Bishop, Nicholas Andrews
Determining whether two documents were composed by the same author, also known as authorship verification, has traditionally been tackled using statistical methods.
1 code implementation • 19 Feb 2024 • Xiao Ye, Andrew Wang, Jacob Choi, Yining Lu, Shreya Sharma, Lingfeng Shen, Vijay Tiyyala, Nicholas Andrews, Daniel Khashabi
Our benchmarking approach focuses on aspects of this ability that are common among humans: (i) recalling related experiences from a large amount of information, and (ii) applying analogical reasoning to complex and lengthy scenarios.
1 code implementation • 12 Jan 2024 • Rafael Rivera Soto, Kailin Koch, Aleem Khan, Barry Chen, Marcus Bishop, Nicholas Andrews
Furthermore, given a handful of examples composed by each of several specific language models of interest, our approach affords the ability to predict which model generated a given document.
no code implementations • 28 Dec 2023 • Aleem Khan, Andrew Wang, Sophia Hager, Nicholas Andrews
However, in applications such as writing assistants, it is desirable for language models to produce text in an author-specific style on the basis of a potentially small writing sample.
1 code implementation • 13 Nov 2023 • Cristina Aggazzotti, Nicholas Andrews, Elizabeth Allyn Smith
Authorship verification is the task of determining if two distinct writing samples share the same author and is typically concerned with the attribution of written text.
1 code implementation • 22 Aug 2023 • Andrew Wang, Cristina Aggazzotti, Rebecca Kotula, Rafael Rivera Soto, Marcus Bishop, Nicholas Andrews
Automatically disentangling an author's style from the content of their writing is a longstanding and possibly insurmountable problem in computational linguistics.
no code implementations • 18 Dec 2022 • Ajay Patel, Nicholas Andrews, Chris Callison-Burch
Existing unsupervised approaches like STRAP have largely focused on style transfer to target authors with many examples of their writing style in books, speeches, or other published works.
1 code implementation • 13 Dec 2022 • David Mueller, Nicholas Andrews, Mark Dredze
Learning these models often requires specialized training algorithms that address task-conflict in the shared parameter updates, which otherwise can lead to negative transfer.
2 code implementations • NAACL 2021 • Aleem Khan, Elizabeth Fleming, Noah Schofield, Marcus Bishop, Nicholas Andrews
We consider the task of linking social media accounts that belong to the same author in an automated fashion on the basis of the content and metadata of their corresponding document streams.
no code implementations • EMNLP 2020 • Steven Reich, David Mueller, Nicholas Andrews
However, extending these methods to structured prediction is not always straightforward or effective; furthermore, a held-out calibration set may not always be available.
1 code implementation • ACL 2020 • David Mueller, Nicholas Andrews, Mark Dredze
However, a straightforward implementation of this simple idea does not always work in practice: naive training of NER models using annotated data drawn from multiple languages consistently underperforms models trained on monolingual data alone, despite having access to more training data.
Multilingual Named Entity Recognition named-entity-recognition +2
1 code implementation • ACL 2020 • Mitchell A. Gordon, Kevin Duh, Nicholas Andrews
Low levels of pruning (30-40%) do not affect pre-training loss or transfer to downstream tasks at all.
1 code implementation • IJCNLP 2019 • Nicholas Andrews, Marcus Bishop
The evolution of social media users' behavior over time complicates user-level comparison tasks such as verification, classification, clustering, and ranking.
no code implementations • WS 2018 • Zach Wood-Doughty, Nicholas Andrews, Mark Dredze
While recurrent neural networks (RNNs) are widely used for text classification, they demonstrate poor performance and slow convergence when trained on long sequences.
1 code implementation • WS 2018 • Zach Wood-Doughty, Nicholas Andrews, Rebecca Marvin, Mark Dredze
Social media analysis frequently requires tools that can automatically infer demographics to contextualize trends.
no code implementations • ACL 2017 • Nicholas Andrews, Mark Dredze, Benjamin Van Durme, Jason Eisner
Practically, this means that we may treat the lexical resources as observations under the proposed generative model.
Low Resource Named Entity Recognition named-entity-recognition +2