Search Results for author: Cash Costello

Found 9 papers, 2 papers with code

Continual Reinforcement Learning with TELLA

no code implementations8 Aug 2022 Neil Fendley, Cash Costello, Eric Nguyen, Gino Perrotta, Corey Lowman

Training reinforcement learning agents that continually learn across multiple environments is a challenging problem.

Continual Learning reinforcement-learning +1

Patapasco: A Python Framework for Cross-Language Information Retrieval Experiments

1 code implementation24 Jan 2022 Cash Costello, Eugene Yang, Dawn Lawrie, James Mayfield

While there are high-quality software frameworks for information retrieval experimentation, they do not explicitly support cross-language information retrieval (CLIR).

Information Retrieval Retrieval

SanitAIs: Unsupervised Data Augmentation to Sanitize Trojaned Neural Networks

no code implementations9 Sep 2021 Kiran Karra, Chace Ashcraft, Cash Costello

Self-supervised learning (SSL) methods have resulted in broad improvements to neural network performance by leveraging large, untapped collections of unlabeled data to learn generalized underlying structure.

Data Augmentation Self-Supervised Learning

Tagging Location Phrases in Text

no code implementations LREC 2020 Paul McNamee, James Mayfield, Cash Costello, Caitlyn Bishop, Shelby Anderson

Throughout this time the majority of such work has focused on detection and classification of entities into coarse-grained types like: PERSON, ORGANIZATION, and LOCATION.


Dragonfly: Advances in Non-Speaker Annotation for Low Resource Languages

no code implementations LREC 2020 Cash Costello, Shelby Anderson, Caitlyn Bishop, James Mayfield, Paul McNamee

Dragonfly is an open source software tool that supports annotation of text in a low resource language by non-speakers of the language.

Platforms for Non-speakers Annotating Names in Any Language

no code implementations ACL 2018 Ying Lin, Cash Costello, Boliang Zhang, Di Lu, Heng Ji, James Mayfield, Paul McNamee

We demonstrate two annotation platforms that allow an English speaker to annotate names for any language without knowing the language.

Language-Independent Named Entity Analysis Using Parallel Projection and Rule-Based Disambiguation

no code implementations WS 2017 James Mayfield, Paul McNamee, Cash Costello

The 2017 shared task at the Balto-Slavic NLP workshop requires identifying coarse-grained named entities in seven languages, identifying each entity{'}s base form, and clustering name mentions across the multilingual set of documents.

named-entity-recognition Named Entity Recognition +1

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