Search Results for author: Christopher M. Homan

Found 8 papers, 2 papers with code

Improving Label Quality by Jointly Modeling Items and Annotators

no code implementations20 Jun 2021 Tharindu Cyril Weerasooriya, Alexander G. Ororbia, Christopher M. Homan

We propose a fully Bayesian framework for learning ground truth labels from noisy annotators.

Domain-specific MT for Low-resource Languages: The case of Bambara-French

no code implementations31 Mar 2021 Allahsera Auguste Tapo, Michael Leventhal, Sarah Luger, Christopher M. Homan, Marcos Zampieri

Translating to and from low-resource languages is a challenge for machine translation (MT) systems due to a lack of parallel data.

Machine Translation Translation

Assessing Human Translations from French to Bambara for Machine Learning: a Pilot Study

no code implementations31 Mar 2020 Michael Leventhal, Allahsera Tapo, Sarah Luger, Marcos Zampieri, Christopher M. Homan

We present novel methods for assessing the quality of human-translated aligned texts for learning machine translation models of under-resourced languages.

Machine Translation Translation

Twitter Job/Employment Corpus: A Dataset of Job-Related Discourse Built with Humans in the Loop

no code implementations30 Jan 2019 Tong Liu, Christopher M. Homan

We present the Twitter Job/Employment Corpus, a collection of tweets annotated by a humans-in-the-loop supervised learning framework that integrates crowdsourcing contributions and expertise on the local community and employment environment.

Learning from various labeling strategies for suicide-related messages on social media: An experimental study

1 code implementation30 Jan 2017 Tong Liu, Qijin Cheng, Christopher M. Homan, Vincent M. B. Silenzio

Suicide is an important but often misunderstood problem, one that researchers are now seeking to better understand through social media.

Cannot find the paper you are looking for? You can Submit a new open access paper.